WO2025140663A1 - Model data acquisition method, apparatus and system - Google Patents

Abstract

The present application provides an artificial intelligence (AI) model data acquisition method, apparatus and a system. The method may comprise: receiving first information from a second node, the first information comprising the following types of information: original data and feature information of the original data; and on the basis of the first information, generating multiple pieces of synthetic data, the multiple pieces of synthetic data being used for model processing. In said example, synthetic data that meets diversity requirements and has the same data distribution as measured data can be generated on the basis of original data and feature information of the original data, such that the quality of the synthetic data is improved, thereby improving the performance of model training or updating.

Description

Model data acquisition method, device and system

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on December 29, 2023, with application number 202311871100.7, and priority to the Chinese patent application with the invention name “Model Data Acquisition Method, Device and System”, all contents of which are incorporated by reference in this application.

Technical Field

The present application relates to the field of artificial intelligence (AI), and in particular to a model data acquisition method, device and system.

Background Art

With the development of artificial intelligence research, the application scenarios of neural networks continue to expand, including smart medical care, smart networks, etc. Therefore, how to effectively train neural networks for specific scenarios has become an important research direction. The three important factors in neural network training are data, neural networks, and computing platforms. With the popularization of graphics processing units (GPUs), the proportion of devices equipped with neural network computing platforms has gradually increased, so the demand for proprietary neural network training for workers has increased.

The quality of training data is an important factor affecting the performance of model training and fine-tuning. However, for the training or fine-tuning of dedicated models/dedicated models for specific scenarios deployed on discrete devices, such as the terminal side/specific base station side, it is very challenging to obtain training data. On the one hand, model training or fine-tuning requires sufficient training data for the corresponding scenarios to ensure the convergence performance of the model, but the measured data obtained on the device side is limited, and it is difficult to ensure model performance only by training with data collected by the device; on the other hand, model training or fine-tuning requires data features that are rich enough to ensure the generalization and robustness of the model, but the measured data obtained on the device side is limited, resulting in limited diversity of data features.

In the prior art, the central node receives and merges the original local data of multiple devices as a training set to increase the amount of training data and data diversity of the training set. Then, the central node uses the merged training set to perform model training or model update. The trained model is deployed on the device side for model inference. Among them, the central node only uses the original local data of multiple devices as the input of the generation model to obtain synthetic data, and the quality of the synthetic data is not high.

Summary of the invention

The present application discloses a model data acquisition method, device and system, which can improve the quality of synthetic data.

In a first aspect, an embodiment of the present application provides a model data acquisition method, which is performed by a first node or a circuit for a first node. The method includes: receiving first information from a second node. The first information includes the following types of information: original data and feature information of the original data. And based on the first information, generating multiple synthetic data. The multiple synthetic data are used for model processing.

In an embodiment of the present application, the first node or a circuit used for the first node, such as a chip, generates a plurality of synthetic data based on the acquired original data and characteristic information of the original data.

Based on the original data and the feature information of the original data, synthetic data that meets the diversity requirements and has the same data distribution as the measured data distribution can be generated, thereby improving the quality of the synthetic data, and thus helping to improve the performance of model training or updating. The following description is based on the first node as the execution subject. It can be understood that the first node can also be replaced by a circuit for the first node, such as a chip.

In a possible implementation, the first node further sends second information to the second node. The second information indicates a processing method for the second node to obtain the first information. The processing method for the second node to obtain the first information includes a method for acquiring feature information of the original data.

Based on the instruction of the second information, the second node processes the original data to obtain characteristic information of the original data.

In a possible implementation manner, the first information further includes at least one of the following types of information: a priority of the original data and a data label of the original data.

By reporting the priority of the original data, the first node determines the number of similar synthetic data to be generated according to the priority or probability of each original data, so that the distribution of the synthetic data is consistent with expectations.

In a possible implementation manner, the processing method for the second node to obtain the first information also includes a method for obtaining the data label of the original data.

Based on the method for obtaining the data label of the original data, the second node processes the original data to obtain the data label of the original data.

In a possible implementation manner, the first node further sends third information to the second node, where the third information indicates a reporting configuration of the first information.

The first node sends the third information to the second node so that the second node reports based on the reporting configuration of the first information indicated in the third information, so that the data provider and the synthesized data end separated from each other can align their understanding of the data and improve the quality of the synthesized data.

Optionally, the reporting configuration includes at least one of the following: the type of the first information, and the data volume of the first information.

The first node sends a reporting configuration of the type of first information and/or a reporting configuration of the data amount of the first information to the second node, so that the second node sends the corresponding type of first information or the corresponding data amount of first information to the first node, which helps to improve the quality of the synthesized data.

In a possible implementation, the first node further sends fourth information to the second node. The fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The joint processing may be, for example, weighted summing of feature information of multiple original data of the same group.

The first node sends the fourth information to the second node so that the second node can divide the original data that can be jointly processed into the same group, while dividing the original data that cannot be jointly processed into the same group, thereby avoiding the fusion of original data from different groups to generate unexpected synthetic data.

In a possible implementation manner, the first information further includes the following type of information: group information of the original data.

The second node reports the group information of the original data to the first node, and then the first node can jointly process the feature information of multiple original data in the same group.

In a possible implementation, the first node further sends at least one subset of the plurality of synthesized data to the second node. The first node further receives fifth information from the second node. The fifth information indicates an evaluation result of at least one synthesized data in the at least one subset. The evaluation result indicates at least one synthesized data that needs to be eliminated in the at least one subset, or indicates at least one synthesized data that does not need to be eliminated in the at least one subset.

Optionally, the evaluation result is used to filter the plurality of synthetic data to obtain at least one filtered synthetic data, wherein the at least one filtered synthetic data is used for processing the model.

In a possible implementation, the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information.

By screening the synthetic data through the synthetic data evaluation process, the quality of the synthetic data can be effectively improved.

Alternatively, an embodiment of the present application provides a model data acquisition method, which is executed by a first node or a circuit for the first node. The method includes: the first node receives sixth information from the second node. The sixth information includes the following types of information: original data. And the first node generates multiple synthetic data based on the sixth information and the characteristic information of the original data. The multiple synthetic data are used for model processing. The following description is based on the first node as the execution subject. It can be understood that the first node can also be replaced by a circuit for the first node, such as a chip.

In a first possible implementation manner, the first node further receives eleventh information from the third node, where the eleventh information includes the following type of information: characteristic information of the original data.

In a second possible implementation, the first node further receives ninth information from the second node, the ninth information indicating a method for the first node to obtain the characteristic information of the original data. Further, the first node processes the original data based on the ninth information to obtain the characteristic information of the original data.

In a third possible implementation, the first node further receives ninth information from the third node, the ninth information indicating a method for the first node to obtain the characteristic information of the original data. Further, the first node processes the original data based on the ninth information to obtain the characteristic information of the original data.

In a fourth possible implementation manner, the characteristic information of the original data is determined based on a method for acquiring the characteristic information of the original data, and the method for acquiring the characteristic information of the original data is determined by the first node.

In a possible implementation manner, the sixth information further includes at least one of the following types of information: the priority of the original data, or a data label of the original data.

By reporting the priority of the original data, the first node determines the number of similar synthetic data to be generated according to the priority or probability of each original data, so that the distribution of the synthetic data is consistent with expectations.

In a possible implementation, the first node also sends eighth information to the second node, where the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of the original data.

Based on the method for obtaining the data label of the original data, the second node processes the original data to obtain the data label of the original data.

In a possible implementation manner, the first node further sends seventh information to the second node, where the seventh information indicates a reporting configuration of the sixth information.

The first node sends the seventh information to the second node so that the second node reports based on the reporting configuration of the sixth information indicated in the seventh information. In this way, the data provider and the synthesized data end separated from each other can align their understanding of the data, thereby improving the quality of the synthesized data.

Optionally, the reporting configuration includes at least one of the following: the type of the sixth information, or the data volume of the sixth information.

The first node sends a reporting configuration of the type of sixth information and/or a reporting configuration of the data amount of the sixth information to the second node, so that the second node sends the corresponding type of sixth information, or the corresponding data amount of sixth information to the first node, which helps to improve the quality of the synthesized data.

In a possible implementation, the first node further sends fourth information to the second node. The fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The joint processing may be, for example, weighted summing of feature information of multiple original data of the same group.

The first node sends the fourth information to the second node so that the second node can divide the original data that can be jointly processed into the same group, while dividing the original data that cannot be jointly processed into the same group, thereby avoiding the fusion of original data from different groups to generate unexpected synthetic data.

In a possible implementation manner, the sixth information further includes the following type of information: group information of the original data.

The second node reports the group information of the original data to the first node, and then the first node can jointly process the feature information of multiple original data in the same group.

In a possible implementation, the first node further sends at least one subset of the plurality of synthesized data to the second node. The first node further receives fifth information from the second node. The fifth information indicates an evaluation result of at least one synthesized data in the at least one subset. The evaluation result indicates at least one synthesized data that needs to be eliminated in the at least one subset, or indicates at least one synthesized data that does not need to be eliminated in the at least one subset.

Optionally, the evaluation result is used to filter the plurality of synthetic data to obtain at least one filtered synthetic data, wherein the at least one filtered synthetic data is used for processing the model.

In a possible implementation, the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information.

By screening the synthetic data through the synthetic data evaluation process, the quality of the synthetic data can be effectively improved.

In a second aspect, an embodiment of the present application provides a model data acquisition method, which is executed by a second node or a circuit for a second node. The method includes: sending first information to a first node. The first information includes the following types of information: original data and characteristic information of the original data. The following description is based on the second node as the execution subject. It can be understood that the second node can also be replaced by a circuit for the second node, such as a chip.

In an embodiment of the present application, the second node sends original data and characteristic information of the original data to the first node, and the original data and the characteristic information of the original data are used to generate multiple synthetic data.

Based on the original data and its feature information, synthetic data that meets diversity requirements and has the same data distribution as the measured data can be generated, which can improve the quality of the synthetic data and thus help improve the performance of model training or updating.

In a possible implementation, the second node further receives second information from the first node. The second information indicates a processing method by which the second node obtains the first information. Exemplarily, the processing method by which the second node obtains the first information includes a method for obtaining characteristic information of the original data. Further, the second node obtains the first information based on the second information.

In a possible implementation manner, the first information further includes at least one of the following types of information: a data label of the original data, or a priority of the original data.

In a possible implementation manner, the processing method also includes a method for acquiring data labels of the original data.

In a possible implementation manner, the second node further receives third information from the first node, where the third information indicates a reporting configuration of the first information.

Optionally, the reporting configuration includes at least one of the following: the type of the first information, and the data volume of the first information.

In a possible implementation, the second node further receives fourth information from the first node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group.

In a possible implementation manner, the first information further includes the following type of information: group information of the original data.

In a possible implementation, the second node further receives at least one subset of the multiple synthetic data from the first node. The second node further sends fifth information to the first node, where the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset. The evaluation result indicates at least one synthetic data that needs to be eliminated from the at least one subset, or indicates at least one synthetic data that does not need to be eliminated from the at least one subset.

In a possible implementation manner, the evaluation result is determined based on a distance between at least one synthetic data in the at least one subset and the local data.

In a possible implementation, the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing.

Alternatively, an embodiment of the present application provides a model data acquisition method, which is executed by a second node or a circuit for a second node. The method includes: the second node sends sixth information to the first node. The sixth information includes the following types of information: original data.

The second node also sends ninth information to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data.

In a possible implementation, the second node also receives eighth information from the first node, where the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of the original data.

In a possible implementation manner, the second node further receives seventh information from the first node, where the seventh information indicates a reporting configuration of the sixth information.

In a possible implementation manner, the second node further receives fourth information from the first node, wherein the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group.

In a possible implementation, the second node further receives at least one subset of the multiple synthetic data from the first node. The second node further sends fifth information to the first node, where the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset. The evaluation result indicates at least one synthetic data that needs to be eliminated from the at least one subset, or indicates at least one synthetic data that does not need to be eliminated from the at least one subset.

In a possible implementation manner, the evaluation result is determined based on a distance between at least one synthetic data in the at least one subset and the local data.

In a possible implementation, the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing.

In a third aspect, an embodiment of the present application provides a model data acquisition method, which is performed by a third node or a circuit for a third node. The method includes: sending eleventh information to a first node, the eleventh information including the following types of information: characteristic information of the original data. In this way, the first node obtains synthetic data based on the characteristic information of the original data, that is, the characteristic information of the original data is used to generate the synthetic data.

Alternatively, an embodiment of the present application provides a model data acquisition method, which is executed by a third node or a circuit for a third node. The method includes: sending ninth information to a first node, wherein the ninth information indicates a method for the first node to obtain characteristic information of the original data. In this way, the first node processes the original data based on the ninth information to obtain characteristic information of the original data, that is, the method for obtaining characteristic information of the original data is used to obtain characteristic information of the original data.

In a fourth aspect, the present application provides a model data acquisition device, which may include a transceiver module and a processing module, as follows:

A transceiver module, configured to receive first information from a second node, wherein the first information includes the following types of information: original data and characteristic information of the original data;

A processing module is used to generate a plurality of synthetic data based on the first information, and the plurality of synthetic data are used for processing the model.

In a possible implementation, the transceiver module is further used to send second information to the second node, where the second information indicates a processing method for the second node to obtain the first information, and the processing method for the second node to obtain the first information includes a method for obtaining characteristic information of the original data.

In a possible implementation manner, the first information further includes the following types of information: a data label of the original data and/or a priority of the original data.

In a possible implementation manner, the processing method for the second node to obtain the first information also includes a method for obtaining the data label of the original data.

In a possible implementation manner, the transceiver module is further configured to send third information to the second node, where the third information indicates a reporting configuration of the first information.

In a possible implementation manner, the reporting configuration includes at least one of the following: a type of the first information, and a data volume of the first information.

In a possible implementation, the transceiver module is further configured to send fourth information to the second node, where the fourth information indicates that the device has the ability to jointly process feature information of multiple original data of the same group.

In a possible implementation manner, the first information further includes the following type of information: group information of the original data.

In a possible implementation manner, the transceiver module is further configured to send at least one subset of the plurality of synthesized data to the second node;

Receive fifth information from the second node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset, wherein the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset.

In a possible implementation, the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for processing the model.

In a possible implementation, the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information.

Alternatively, the present application provides a model data acquisition device, which may include a transceiver module and a processing module, as follows:

The transceiver module is used to receive sixth information from the second node, where the sixth information includes the following types of information: original data.

The processing module is used to generate a plurality of synthetic data based on the sixth information and the characteristic information of the original data. The plurality of synthetic data are used for processing the model.

In a first possible implementation manner, the transceiver module is further configured to receive eleventh information from the third node, where the eleventh information includes the following type of information: characteristic information of the original data.

In a second possible implementation manner, the transceiver module is further configured to receive ninth information from the second node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data.

In a third possible implementation manner, the transceiver module is further used to receive ninth information from the third node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data.

In a fourth possible implementation manner, the characteristic information of the original data is determined based on a method for acquiring the characteristic information of the original data, and the method for acquiring the characteristic information of the original data is determined by the first node.

In a possible implementation, the transceiver module is also used to send eighth information to the second node, where the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of the original data.

In a possible implementation manner, the transceiver module is further configured to send seventh information to the second node, where the seventh information indicates a reporting configuration of the sixth information.

Optionally, the reporting configuration includes at least one of the following: the type of the sixth information, and the data volume of the sixth information.

In a possible implementation, the transceiver module is further configured to send fourth information to the second node. The fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The joint processing may be, for example, weighted summing of feature information of multiple original data of the same group.

In a possible implementation manner, the sixth information further includes the following type of information: group information of the original data.

In a possible implementation, the transceiver module is further configured to send at least one subset of the plurality of synthesized data to the second node. The first node further receives fifth information from the second node. The fifth information indicates an evaluation result of at least one synthesized data in the at least one subset. The evaluation result indicates at least one synthesized data that needs to be eliminated in the at least one subset, or indicates at least one synthesized data that does not need to be eliminated in the at least one subset.

Optionally, the evaluation result is used to filter the plurality of synthetic data to obtain at least one filtered synthetic data, wherein the at least one filtered synthetic data is used for processing the model.

In a possible implementation, the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information.

In a fifth aspect, the present application provides a model data acquisition device, which may include a transceiver module, specifically as follows:

The transceiver module is used to send first information to the first node, where the first information includes the following types of information: original data and characteristic information of the original data.

In a possible implementation, the transceiver module is further configured to receive second information from the first node, where the second information indicates a processing method by which the device obtains the first information, and the processing method includes a method for acquiring characteristic information of the original data;

The method further includes a processing module, configured to obtain the first information based on the second information.

In a possible implementation manner, the first information further includes the following types of information: a data label of the original data and/or a priority of the original data.

In a possible implementation manner, the processing method also includes a method for acquiring data labels of the original data.

In a possible implementation manner, the transceiver module is further configured to receive third information from the first node, where the third information indicates a reporting configuration of the first information.

In a possible implementation manner, the reporting configuration includes at least one of the following: a type of the first information, and a data volume of the first information.

In a possible implementation, the transceiver module is further configured to receive fourth information from the first node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group.

In a possible implementation manner, the first information further includes the following type of information: group information of the original data.

In a possible implementation, the transceiver module is further configured to receive at least one subset of the plurality of synthesized data from the first node;

Send fifth information to the first node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset, and the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset.

In a possible implementation manner, the evaluation result is determined based on a distance between at least one synthetic data in the at least one subset and the local data.

In a possible implementation, the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing.

Alternatively, the present application provides a model data acquisition device, which may include a transceiver module, specifically as follows:

The transceiver module is used to send sixth information to the first node. The sixth information includes the following types of information: original data.

The transceiver module is further used to send ninth information to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data.

In a possible implementation, the transceiver module is used to receive eighth information from the first node, where the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of the original data.

In a possible implementation manner, the transceiver module is configured to receive seventh information from the first node, where the seventh information indicates a reporting configuration of the sixth information.

In a possible implementation, the transceiver module is configured to receive fourth information from the first node. The fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group.

In a possible implementation, the transceiver module is configured to receive at least one subset of the plurality of synthesized data from the first node. The second node further sends fifth information to the first node, wherein the fifth information indicates an evaluation result of at least one synthesized data in the at least one subset. The evaluation result indicates at least one synthesized data that needs to be eliminated from the at least one subset, or indicates at least one synthesized data that does not need to be eliminated from the at least one subset.

In a possible implementation manner, the evaluation result is determined based on a distance between at least one synthetic data in the at least one subset and the local data.

In a possible implementation, the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing.

In a sixth aspect, the present application provides a model data acquisition device, which may include a transceiver module, wherein:

The transceiver module is used to send eleventh information to the first node, where the eleventh information includes the following types of information: characteristic information of original data.

Alternatively, the present application provides a model data acquisition device, which may include a transceiver module, wherein:

The transceiver module is used to send ninth information to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data.

In a seventh aspect, the present application provides a model data acquisition device, comprising a processor and a memory; wherein the memory is used to store program code, and the processor is used to call the program code to execute a method provided in any possible implementation manner of the first aspect.

In an eighth aspect, the present application provides a model data acquisition device, comprising a processing circuit and a memory; wherein the memory is used to store program code, and the processing circuit is used to call the program code to execute a method provided in any possible implementation manner of the second aspect.

In a ninth aspect, the present application provides a model data acquisition device, comprising a processing circuit and a memory; wherein the memory is used to store program code, and the processing circuit is used to call the program code to execute a method provided in any possible implementation manner of the third aspect.

In a tenth aspect, the present application provides a model data acquisition system, comprising an apparatus provided in any possible implementation manner of the fourth aspect, and an apparatus provided in any possible implementation manner of the fifth aspect; or comprising an apparatus provided in any possible implementation manner of the seventh aspect, and an apparatus provided in any possible implementation manner of the eighth aspect; or comprising an apparatus provided in any possible implementation manner of the fourth aspect, and an apparatus provided in any possible implementation manner of the fifth aspect, and an apparatus provided in any possible implementation manner of the sixth aspect; or comprising an apparatus provided in any possible implementation manner of the seventh aspect, and an apparatus provided in any possible implementation manner of the eighth aspect, and an apparatus provided in any possible implementation manner of the ninth aspect.

In the eleventh aspect, the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement a method provided in any possible implementation of the first aspect, or a method provided in any possible implementation of the second aspect, or a method provided in any possible implementation of the third aspect.

In the twelfth aspect, the present application provides a computer program product, characterized in that when the computer program product runs on a computer, the computer executes a method provided in any possible implementation of the first aspect, or a method provided in any possible implementation of the second aspect, or a method provided in any possible implementation of the third aspect.

It can be understood that the apparatus described in the fourth aspect to the apparatus described in the ninth aspect, the system described in the tenth aspect, the computer-readable storage medium described in the eleventh aspect, or the computer program product described in the twelfth aspect are all used to execute any method provided in the first aspect, any method provided in the second aspect, or any method provided in the third aspect. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

BRIEF DESCRIPTION OF THE DRAWINGS

The following is an introduction to the drawings used in the embodiments of the present application.

FIG1 is a simplified schematic diagram of a wireless communication system provided by an embodiment of the present application;

FIG2a is a schematic diagram of a communication system provided by an embodiment of the present application;

FIG2b is a schematic diagram of another communication system provided in an embodiment of the present application;

FIG3a is a schematic diagram of a possible application framework in a communication system provided in an embodiment of the present application;

FIG3b is a schematic diagram of another possible application framework in the communication system provided in an embodiment of the present application;

FIG4 is a schematic diagram of an encoder and a decoder provided in an embodiment of the present application;

FIG5 is a schematic diagram of an AI application framework provided in an embodiment of the present application;

FIG6 is a schematic diagram of a flow chart of a method for acquiring model data provided in an embodiment of the present application;

FIG7 is a flow chart of another method for acquiring model data provided in an embodiment of the present application;

FIG8 is a flow chart of another model data acquisition method provided in an embodiment of the present application;

FIG9 is a flow chart of another model data acquisition method provided in an embodiment of the present application;

FIG10 is a flow chart of another model data acquisition method provided in an embodiment of the present application;

FIG11 is a schematic diagram of the structure of a model data acquisition device provided in an embodiment of the present application;

FIG. 12 is a schematic diagram of the structure of another model data acquisition device provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the present disclosure will be further described in detail below with reference to the accompanying drawings.

The present disclosure relates to at least one (item) as follows, indicating one (item) or more (items). More than one (item) refers to two (items) or more than two (items). "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the objects associated before and after are in an "or" relationship. In addition, it should be understood that although the terms first, second, etc. may be used to describe each object in the present disclosure, these objects should not be limited to these terms. These terms are only used to distinguish each object from each other.

The terms "including" and "having" and any variations thereof mentioned in the following description of the present disclosure are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes other steps or units that are not listed, or optionally includes other steps or units that are inherent to these processes, methods, products or devices. It should be noted that in the present disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any method or design described in the present disclosure as "exemplary" or "for example" should not be interpreted as being more preferred or more advantageous than other methods or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

The technology provided by the present disclosure can be applied to various communication systems, for example, the communication system can be a fifth generation (5G) or new radio (NR) system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a wireless local area network (WLAN) system, a satellite communication system, a future communication system such as a sixth generation (6G) mobile communication system, or a fusion system of multiple systems. The technical solution provided by the present application can also be applied to device to device (D2D) communication, vehicle to everything (V2X) communication, machine to machine (M2M) communication, machine type communication (MTC), and Internet of things (IoT) communication system or other communication systems.

A device in a communication system can send a signal to another device or receive a signal from another device. The signal may include information, signaling, or data, etc. The device may also be replaced by an entity, a network entity, a network element, a communication device, a communication module, a node, a communication node, etc. The present disclosure is described by taking the device as an example. For example, the communication system may include at least one terminal device and at least one access network device. The access network device may send a downlink signal to the terminal device, and/or the terminal device may send an uplink signal to the access network device. In addition, it can be understood that if the communication system includes multiple terminal devices, multiple terminal devices may also send signals to each other, that is, the signal sending device and the signal receiving device may both be terminal devices.

The information generation method provided in the embodiment of the present application can be applied to wireless communication systems such as 5G, 6G, and satellite communication. Referring to FIG1, FIG1 is a simplified schematic diagram of a wireless communication system provided in an embodiment of the present application. As shown in FIG1, the wireless communication system includes a wireless access network 100. The wireless access network 100 may be a next generation (e.g., 6G or higher) wireless access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more communication devices (120a-120j, collectively referred to as 120) may be interconnected or connected to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, FIG1 is only a schematic diagram, and other devices may also be included in the wireless communication system, such as core network devices, wireless relay devices, and/or wireless backhaul devices, which are not shown in FIG1.

Optionally, in practical applications, the wireless communication system may include multiple network devices (also referred to as access network devices) at the same time, or may include multiple communication devices at the same time. A network device may serve one or more communication devices at the same time. A communication device may also access one or more network devices at the same time. The embodiment of the present application does not limit the number of communication devices and network devices included in the wireless communication system.

Among them, the network device can be an entity on the network side for transmitting or receiving signals. The network device can be an access device for the communication device to access the wireless communication system by wireless means, such as the network device can be a base station. The base station can broadly cover the following various names, or be replaced with the following names, such as: Node B (NodeB), evolved NodeB (eNB), next generation NodeB (gNB), access network equipment in open radio access network (O-RAN), relay station, access point, transmission point (transmitting and receiving point, TRP), transmitting point (transmitting point, TP), master station (Master eNodeB, MeNB), secondary eNodeB (SeNB), multi-standard radio (Multi-standard radio o, MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), centralized unit control plane (CU-CP) node, centralized unit user plane (CU-UP) node, positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The network device can also refer to a communication module, a modem or a chip used to be set in the aforementioned device or apparatus. The network device may also be a mobile switching center and a device that performs base station functions in device-to-device (D2D), vehicle-to-everything (V2X), and machine-to-machine (M2M) communications, a network-side device in a 6G network, and a device that performs base station functions in future communication systems. The network device may support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form used by the network device.

The network equipment may be fixed or mobile. For example, base stations 110a, 110b are stationary and are responsible for wireless transmission and reception in one or more cells from the communication device 120. The helicopter or drone 120i shown in FIG. 1 may be configured to act as a mobile base station, and one or more cells may move according to the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) may be configured to act as a communication device that communicates with the base station 110b.

In the present disclosure, the communication device used to implement the above access network function can be an access network device, or a network device with some functions of accessing the network, or a device capable of supporting the implementation of the access network function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module, which can be installed in the access network device or used in combination with the access network device. In the method of the present disclosure, the communication device used to implement the access network device function is an access network device for description as an example.

The communication device can be an entity on the user side for receiving or transmitting signals, such as a mobile phone. The communication device can be used to connect people, objects and machines. The communication device can communicate with one or more core networks through a network device. The communication device includes a handheld device with a wireless connection function, other processing devices connected to a wireless modem, or a vehicle-mounted device. The communication device can be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device. The communication device 120 can be widely used in various scenarios, such as cellular communication, device-to-device D2D, vehicle-to-all V2X, peer-to-peer (P2P), machine-to-machine M2M, machine type communication MTC, Internet of Things IoT, virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, drone, robot, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc. Some examples of communication devices 120 are: 3GPP standard user equipment (UE), fixed equipment, mobile devices, handheld devices, wearable devices, cellular phones, smart phones, Session Initialization Protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, Global Positioning System (GPS) devices, target tracking devices, drones, helicopters, aircraft, ships, remote control devices, smart home devices, industrial equipment, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), etc. The communication device 120 may be a wireless device in the above-mentioned scenarios or a device used to be set in a wireless device, for example, a communication module, a modem or a chip in the above-mentioned device. The communication device may also be referred to as a terminal, a terminal device, a user equipment UE, a mobile station (MS), a mobile terminal (MT), or the like. The communication device may also be referred to as a terminal, a terminal device, a user equipment UE, a mobile station (MS), a mobile terminal (MT), or the like. The communication device may also be referred to as a communication device in a future wireless communication system. The communication device can be used in a dedicated network device or a general device. The embodiments of the present application do not limit the specific technology and specific device form used by the communication device.

Optionally, the communication device can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X, D2D, or P2P, etc. As shown in Figure 1, the cellular phone 120a and the car 120b communicate with each other using sidelink signals. The cellular phone 120a and the smart home device 120e communicate without relaying the communication signal through the base station 110b.

In the present disclosure, the communication device used to implement the functions of the communication device may be a terminal device, or a terminal device having some of the functions of the above communication device, or a device capable of supporting the implementation of the functions of the above communication device, such as a chip system, which may be installed in the terminal device or used in combination with the terminal device. In the present disclosure, the chip system may be composed of a chip, or may include a chip and other discrete devices. In the technical solution provided in the present disclosure, the communication device is described as a terminal device or UE as an example.

Optionally, a wireless communication system is usually composed of cells, and a base station provides management of the cell. The base station provides communication services to multiple mobile stations (MS) in the cell. The base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU can be placed in different places, for example: the RRU is remote and placed in an area with high traffic volume, and the BBU is placed in a central computer room. The BBU and RRU can also be placed in the same computer room. The BBU and RRU can also be different components under one rack. Optionally, a cell can correspond to one carrier or component carrier.

It can be understood that the present disclosure can be applied between a network device and a communication device, between a network device and a network device, or between a communication device and a communication device, that is, between a primary device and a secondary device. The primary device can be a network device or a communication device. When the primary device is a network device, the secondary device can be another network device or a communication device. When the primary device is a communication device, the secondary device can be another communication device.

The following describes the scheme by taking the master device as a network device, such as an access network device, and the slave device as a communication device, such as a terminal device, as an example. The communication direction corresponding to the downlink is the transmission from the master device to the slave device, and the communication direction corresponding to the uplink is the transmission from the slave device to the master device.

Protocol layer structure between access network equipment and terminal equipment

The communication between the access network equipment and the terminal equipment follows a certain protocol layer structure. The protocol layer structure may include a control plane protocol layer structure and a user plane protocol layer structure. For example, the control plane protocol layer structure may include the functions of the radio resource control (RRC) layer, the packet data convergence protocol (PDCP) layer, the radio link control (RLC) layer, the medium access control (MAC) layer and the physical layer. For example, the user plane protocol layer structure may include the functions of the PDCP layer, the RLC layer, the MAC layer and the physical layer. In a possible implementation, the service data adaptation protocol (SDAP) layer may also be included above the PDCP layer.

Optionally, the protocol layer structure between the access network device and the terminal may also include an artificial intelligence (AI) layer for transmitting data related to AI functions.

Taking data transmission between access network equipment and terminal equipment as an example, data transmission needs to pass through the user plane protocol layer, such as the SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer. Among them, the SDAP layer, PDCP layer, RLC layer, MAC layer, and physical layer can also be collectively referred to as the access layer. According to the transmission direction of data, it is divided into sending or receiving, and each of the above layers is divided into a sending part and a receiving part. Taking downlink data transmission as an example, after the PDCP layer obtains data from the upper layer, it transmits the data to the RLC layer and the MAC layer, and then the MAC layer generates a transmission block, which is then wirelessly transmitted through the physical layer. Data is encapsulated accordingly in each layer. For example, the data received by a layer from the upper layer of the layer is regarded as the service data unit (SDU) of the layer, which becomes a protocol data unit (PDU) after being encapsulated by the layer, and then passed to the next layer.

Exemplarily, the terminal device may also have an application layer and a non-access layer. The application layer may be used to provide services to applications installed in the terminal device. For example, downlink data received by the terminal device may be sequentially transmitted from the physical layer to the application layer, and then provided to the application by the application layer; for another example, the application layer may obtain data generated by the application, and sequentially transmit the data to the physical layer and send it to other communication devices. The non-access layer may be used to forward user data, such as forwarding uplink data received from the application layer to the SDAP layer or forwarding downlink data received from the SDAP layer to the application layer.

Structure of access network equipment

The access network equipment may include a centralized unit (CU) and a distributed unit (DU). Multiple DUs may be centrally controlled by one CU. As an example, the interface between the CU and the DU may be referred to as an F1 interface. Among them, the control plane (CP) interface may be F1-C, and the user plane (UP) interface may be F1-U. The CU and the DU may be divided according to the protocol layers of the wireless network: for example, the functions of the PDCP layer and above are set in the CU, and the functions of the protocol layers below the PDCP layer (such as the RLC layer and the MAC layer, etc.) are set in the DU; for another example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer are set in the DU.

It is understandable that the above-mentioned division of the processing functions of CU and DU according to the protocol layer is only an example, and it can also be divided in other ways, for example, CU or DU can be divided into functions with more protocol layers, and for example, CU or DU can also be divided into partial processing functions with protocol layers. In one design, some functions of the RLC layer and the functions of the protocol layers above the RLC layer are set in the CU, and the remaining functions of the RLC layer and the functions of the protocol layers below the RLC layer are set in the DU. In another design, the functions of the CU or DU can also be divided according to the service type or other system requirements, for example, divided by latency, and the functions whose processing time needs to meet the latency requirements are set in the DU, and the functions that do not need to meet the latency requirements are set in the CU. In another design, the CU can also have one or more functions of the core network. Exemplarily, the CU can be set on the network side to facilitate centralized management. In another design, the RU of the DU is set remotely. Among them, the RU has a radio frequency function.

Optionally, DU and RU can be divided at the physical layer (physical layer, PHY). For example, DU can implement high-level functions in the PHY layer, and RU can implement low-level functions in the PHY layer. Wherein, when used for transmission, the functions of the PHY layer may include adding cyclic redundancy check (CRC) code, channel coding, rate matching, scrambling, modulation, layer mapping, precoding, resource mapping, physical antenna mapping, and/or RF transmission functions. When used for reception, the functions of the PHY layer may include CRC, channel decoding, rate matching, descrambling, demodulation, layer mapping, channel detection, resource demapping, physical antenna demapping, and/or RF reception functions. Wherein, the high-level functions in the PHY layer may include a part of the functions of the PHY layer, such as the part of the functions closer to the MAC layer, and the low-level functions in the PHY layer may include another part of the functions of the PHY layer, such as the part of the functions closer to the RF functions. For example, the high-level functions in the PHY layer may include adding CRC code, channel coding, rate matching, scrambling, modulation, and layer mapping, and the low-level functions in the PHY layer may include precoding, resource mapping, physical antenna mapping, and RF transmission functions; or, the high-level functions in the PHY layer may include adding CRC code, channel coding, rate matching, scrambling, modulation, layer mapping and precoding, and the low-level functions in the PHY layer may include resource mapping, physical antenna mapping, and RF transmission functions.

Exemplarily, the functions of CU can be implemented by one entity, or can be implemented by different entities. For example, the functions of CU can be further divided, that is, the control plane and the user plane are separated and implemented by different entities, namely the control plane CU entity (i.e., CU-CP entity) and the user plane CU entity (i.e., CU-UP entity). The CU-CP entity and the CU-UP entity can be coupled with the DU to jointly complete the functions of the access network device.

In the above architecture, the signaling generated by the CU can be sent to the terminal device through the DU, or the signaling generated by the terminal device can be sent to the CU through the DU. For example, the signaling of the RRC or PDCP layer will eventually be processed into the signaling of the physical layer and sent to the terminal device, or converted from the received signaling of the physical layer. In this architecture, the signaling of the RRC or PDCP layer can be considered to be sent through the DU, or through the DU and the RU.

Optionally, any of the above DU, CU, CU-CP, CU-UP and RU can be a software module, a hardware structure, or a software module + hardware structure, without limitation. Among them, the existence forms of different entities can be different, without limitation. For example, DU, CU, CU-CP, CU-UP are software modules, and RU is a hardware structure. These modules and their execution methods are also within the scope of protection of the present disclosure.

The access network equipment may support one or more types of fronthaul interfaces, and different fronthaul interfaces correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more of the baseband functions, and the RU is configured to implement one or more of the radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, relative to the CPRI, part of the baseband functions of the downlink and/or uplink, such as, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT)/adding cyclic prefix (CP), are moved from the DU to the RU for implementation, and for the uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT)/removing cyclic prefix (CP), are moved from the DU to the RU for implementation. In a possible implementation, the interface may be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the division between DU and RU is different, corresponding to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, DU is configured to implement layer mapping and one or more functions before it (i.e., one or more of coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (for example, one or more of resource element (RE) mapping, digital beamforming (BF), or inverse fast Fourier transform (IFFT)/adding cyclic prefix (CP)) are moved to RU for implementation. For uplink transmission, with RE demapping as the division, DU is configured to implement demapping and one or more functions before it (i.e. decoding, rate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, one or more functions of RE demapping), while other functions after demapping (e.g., one or more of digital BF or fast Fourier transform (FFT)/CP removal) are moved to RU for implementation. It is understandable that the functional description of DU and RU corresponding to various types of eCPRI can be referred to the eCPRI protocol, which will not be repeated here.

In one possible design, the processing unit for implementing the baseband function in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing the baseband function in the RRU/AAU/RRH is called a baseband low layer (BBL) unit.

In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open radio access network (open RAN, ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. Any unit in the CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

In the embodiments of the present application, the device for realizing the function of the network device may be a network device; or it may be a device capable of supporting the network device to realize the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The device may be installed in the network device or used in combination with the network device. In the embodiments of the present application, only the device for realizing the function of the network device is a network device as an example for explanation, and the scheme of the embodiments of the present application is not limited.

The network device and/or terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on the water surface; it can also be deployed on aircraft, balloons and satellites in the air. The scenarios in which the network device and the terminal device are located are not limited in the embodiments of the present application. In addition, the terminal device and the network device can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. The present application does not limit the specific forms of the terminal device and the network device.

It should be understood that the number and type of each device in the communication system shown in FIG1 are only for illustration, and the present disclosure is not limited thereto. In actual applications, the communication system may also include more terminal devices, more access network devices, and other network elements, such as core network devices, and/or network elements for implementing artificial intelligence functions. Among them, the network element for implementing the artificial intelligence function may be a RAN intelligent controller (RAN intelligent controller, RIC). FIG2a is a schematic diagram of a communication system applicable to an embodiment of the present application. As shown in FIG2a, the communication system 200 may include at least one network device, such as the network device 210 shown in FIG2a; the communication system 200 may also include at least one terminal device, such as the terminal device 220 and the terminal device 230 shown in FIG2a. The network device 210 and the terminal device (such as the terminal device 220 and the terminal device 230) may communicate via a wireless link. The communication devices in the communication system, for example, the network device 210 and the terminal device 220, may communicate via a multi-antenna technology.

FIG2b is a schematic diagram of another communication system applicable to an embodiment of the present application. Compared with the communication system 200 shown in FIG2a, the communication system 300 shown in FIG2b also includes an AI network element 240. The AI network element 240 is used to perform AI-related operations, such as building a training data set or training an AI model.

In one possible implementation, the network device 210 may send data related to the training of the AI model to the AI network element 240, which constructs a training data set and trains the AI model. For example, the data related to the training of the AI model may include data reported by the terminal device. The AI network element 240 may send the results of the operations related to the AI model to the network device 210, and forward them to the terminal device through the network device 210. For example, the results of the operations related to the AI model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a part of the AI model that has completed training may be deployed on the network device 210, and another part may be deployed on the terminal device. Alternatively, the AI model that has completed training may be deployed on the network device 210. Alternatively, the AI model that has completed training may be deployed on the terminal device.

It should be understood that FIG. 2b only illustrates the example of the AI network element 240 being directly connected to the network device 210. In other scenarios, the AI network element 240 may also be connected to the terminal device. Alternatively, the AI network element 240 may be connected to the network device 210 and the terminal device at the same time. Alternatively, the AI network element 240 may also be connected to the network device 210 through a third-party network element (also referred to as a third-party device or a third-party entity). The embodiment of the present application does not limit the connection relationship between the AI network element and other network elements.

The AI network element 240 may also be provided as a module in a network device and/or a terminal device, for example, in the network device 210 or the terminal device shown in FIG. 2a .

It should be noted that FIG. 2a and FIG. 2b are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and/or wireless backhaul devices, which are not shown in FIG. 2a and FIG. 2b. In practical applications, the communication system may include multiple network devices and may also include multiple terminal devices. The embodiment of the present application does not limit the number of network devices and terminal devices included in the communication system.

In order to support AI technology in wireless networks, AI nodes may also be introduced into the network.

Optionally, the AI node can be deployed in one or more of the following locations in the communication system: access network equipment, terminal equipment, or core network equipment, etc., or the AI node can also be deployed separately, for example, deployed in a location other than any of the above devices, such as a host or cloud server in an over-the-top (OTT) system. The AI node can communicate with other devices in the communication system, and the other devices can be, for example, one or more of the following: network equipment, terminal equipment, or network elements of the core network, etc.

It is understood that the present application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes are responsible for different functions.

It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to implement different functions, or they can be network elements in hardware devices, or they can be software functions running on dedicated hardware, or they can be virtualized functions instantiated on a platform (for example, a cloud platform). This application does not limit the specific form of the above-mentioned AI nodes.

An AI node can be an AI network element or an AI module.

Figure 3a is a schematic diagram of a possible application framework in a communication system. As shown in Figure 3a, network elements in the communication system are connected through interfaces (e.g., NG, Xn) or air interfaces. One or more AI modules are provided in one or more devices of these network element nodes, such as core network equipment, access network (radio access network, RAN) nodes, terminals or OAM (for clarity, only one is shown in Figure 3a). The access network node can be a separate RAN node, or it can include multiple RAN nodes, for example, including CU and DU. The CU and/or DU can also be provided with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are provided in the CU-CP and/or CU-UP.

The AI module is used to implement the corresponding AI function. The AI modules deployed in different network elements may be the same or different. The model of the AI module can implement different functions according to different parameter configurations. The model of the AI module can be configured based on one or more of the following parameters: structural parameters (such as the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the biases in the activation function), input parameters (such as the type of input parameters and/or the dimension of input parameters), or output parameters (such as the type of output parameters and/or the dimension of output parameters). Among them, the bias in the activation function can also be called the bias of the neural network.

An AI module can have one or more models. A model can be inferred to obtain an output, which includes one parameter or multiple parameters. The learning process, training process, or inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

FIG3b is a schematic diagram of another possible application framework in a communication system. As shown in FIG3b, the communication system includes a RAN intelligent controller (RIC). For example, the RIC may be the AI module in FIG3a, which is used to implement AI-related functions. The RIC includes a near-real-time RIC (near-real-time RIC, near-RT RIC) and a non-real-time RIC (non-real-time RIC, Non-RT RIC). Among them, the non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to delay, and the delay of the data may be in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to delay, and the delay of the data is in the order of tens of milliseconds.

The near real-time RIC is used for model training and reasoning. For example, it is used to train an AI model and use the AI model for reasoning. The near real-time RIC can obtain information on the network side and/or the terminal side from a RAN node (e.g., CU, CU-CP, CU-UP, DU, and/or RU) and/or a terminal. This information can be used as training data or reasoning data. Optionally, the near real-time RIC can submit the reasoning results to the RAN node and/or the terminal. Optionally, the reasoning results can be exchanged between the CU and the DU, and/or between the DU and the RU. For example, the near real-time RIC submits the reasoning results to the DU, and the DU sends it to the RU.

The non-real-time RIC is also used for model training and reasoning. For example, it is used to train an AI model and use the model for reasoning. The non-real-time RIC can obtain information on the network side and/or the terminal side from a RAN node (such as a CU, CU-CP, CU-UP, DU and/or RU) and/or a terminal. The information can be used as training data or reasoning data, and the reasoning results can be submitted to the RAN node and/or the terminal. Optionally, the reasoning results can be exchanged between the CU and the DU, and/or between the DU and the RU. For example, the non-real-time RIC submits the reasoning results to the DU, and the DU sends it to the RU.

The near real-time RIC and the non-real-time RIC may also be separately set as a network element. Optionally, the near real-time RIC and the non-real-time RIC may also be part of other devices, for example, the near real-time RIC is set in a RAN node (for example, in a CU or DU), and the non-real-time RIC is set in an OAM, a server (such as a cloud server), a core network device, or other network devices.

It is understandable that all or part of the functions implemented by one or more of the terminal equipment, access network equipment, core network equipment, or network elements used to implement artificial intelligence functions can be virtualized, that is, implemented by one or more of the proprietary processors or general-purpose processors and corresponding software modules. Among them, the terminal equipment and access network equipment involve interfaces for air interface transmission, and the transceiver functions of the interfaces can be implemented by hardware. Core network equipment, such as operation administration and maintenance (OAM) network elements, can be virtualized. Optionally, one or more functions of the virtualized terminal equipment, access network equipment, core network equipment, or network elements used to implement artificial intelligence functions can be implemented by cloud devices, such as cloud devices in over the top (OTT) systems.

The method provided by the present invention can be used for communication between access network equipment and terminal equipment, and can also be used for communication between other communication equipment, such as communication between macro base stations and micro base stations in a wireless backhaul link, and communication between two terminal devices in a side link (SL), without limitation.

In order to facilitate understanding of the solutions of the embodiments of the present application, the terms that may be involved in the embodiments of the present application are explained below.

(1) AI Model:

An AI model is an algorithm or computer program that can implement AI functions. The AI model represents the mapping relationship between the input and output of the model. The type of AI model can be a neural network, linear regression model, decision tree model, support vector machine (SVM), Bayesian network, Q learning model or other machine learning (ML) model.

(2) Two-terminal model:

The two-end model can also be called a bilateral model, a collaborative model, a dual model, or a two-side model. The two-end model refers to a model composed of multiple sub-models. The multiple sub-models that constitute the model need to match each other. The multiple sub-models can be deployed in different nodes.

In one possible design, an embodiment of the present application relates to an encoder for compressing CSI and a decoder for recovering compressed CSI. The encoder and the decoder are used in matching manner, and it can be understood that the encoder and the decoder are matching AI models. An encoder may include one or more AI models, and the decoder matched by the encoder also includes one or more AI models. The number of AI models included in the matching encoder and decoder is the same and corresponds one to one.

In one possible design, a set of matched encoders and decoders can be specifically two parts of the same auto-encoder (AE), for example, as shown in Figure 4. The AE model in which the encoder and decoder are deployed on different nodes is a typical bilateral model. The encoder and decoder of the AE model are usually a jointly trained encoder and decoder used in matching. The encoder processes the input V to obtain the processed result z, and the decoder can decode the encoder output z into the desired output V'.

An autoencoder is a neural network for unsupervised learning. Its characteristic is that it uses input data as labels, so an autoencoder can also be understood as a neural network for self-supervised learning. An autoencoder can be used for data compression and recovery. For example, the encoder in an autoencoder can compress (encode) data A to obtain data B; the decoder in an autoencoder can decompress (decode) data B to recover data A. Alternatively, it can be understood that the decoder is the inverse operation of the encoder.

Exemplarily, the AI model in the embodiment of the present application may include an encoder and a decoder. The encoder and the decoder are used in combination, and it can be understood that the encoder and the decoder are matching AI models. The encoder and the decoder can be deployed on the terminal device and the network device respectively.

Alternatively, the AI model in the embodiment of the present application may be a single-end model, which may be deployed on a terminal device or a network device.

(3) Neural network (NN):

Neural networks are a specific implementation of AI or machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling neural networks to learn any mapping.

A neural network can be composed of neural units, and a neural unit can refer to an operation unit with xs and intercept 1 as input. A neural network is a network formed by connecting many of the above single neural units together, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract the features of the local receptive field, and the local receptive field can be an area composed of several neural units.

Taking the AI model type as a neural network as an example, the AI model involved in the present disclosure may be a deep neural network (DNN). Depending on the network construction method, DNN may include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

(4) Training data set and inference data:

In the field of machine learning, ground truth usually refers to data that is believed to be accurate or real.

The training data set is used for training the AI model. The training data set may include the input of the AI model, or the input and target output of the AI model. The training data set includes one or more training data. The training data may include training samples input to the AI model, or may include the target output of the AI model. The target output may also be referred to as a label, sample label, or label sample. The label is the true value.

In the field of communications, training data sets can include simulation data collected through simulation platforms, experimental data collected in experimental scenarios, or measured data collected in actual communication networks. Due to differences in the geographical environment and channel conditions in which the data is generated, such as indoor and outdoor conditions, mobile speeds, frequency bands, or antenna configurations, the collected data can be classified when acquiring the data. For example, data with the same channel propagation environment and antenna configuration can be grouped together.

Model training is essentially learning some of its features from the training data. In the process of training AI models (such as neural network models), because we hope that the output of the AI model is as close as possible to the value we really want to predict, we can compare the current network's predicted value with the target value we really want, and then update the weight vector of each layer of the AI model according to the difference between the two (of course, there is usually an initialization process before the first update, that is, pre-configuring parameters for each layer in the AI model). For example, if the network's predicted value is high, adjust the weight vector to make it predict lower, and keep adjusting until the AI model can predict the target value we really want or a value very close to the target value we really want. Therefore, it is necessary to predefine "how to compare the difference between the predicted value and the target value", which is the loss function or objective function, which are important equations used to measure the difference between the predicted value and the target value. Among them, taking the loss function as an example, the higher the output value (loss) of the loss function, the greater the difference, so the training of the AI model becomes a process of minimizing this loss as much as possible, so that the value of the loss function is less than the threshold, or the value of the loss function meets the target requirements. For example, the AI model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers, width, weights of neurons, or parameters in the activation function of the neurons of the neural network.

Inference data can be used as input to the trained AI model for inference of the AI model. During the model inference process, the inference data is input into the AI model, and the corresponding output is the inference result.

(5) Design of AI model:

The design of AI models mainly includes data collection (such as collecting training data and/or inference data), model training, and model inference. It can also include the application of inference results.

FIG5 shows an AI application framework.

In the aforementioned data collection link, the data source is used to provide training data sets and inference data. In the model training link, the AI model is obtained by analyzing or training the training data provided by the data source. Among them, the AI model represents the mapping relationship between the input and output of the model. Learning the AI model through the model training node is equivalent to using the training data to learn the mapping relationship between the input and output of the model. In the model inference link, the AI model trained in the model training link is used to perform inference based on the inference data provided by the data source to obtain the inference result. This link can also be understood as: inputting the inference data into the AI model, and obtaining the output through the AI model, which is the inference result. The inference result can indicate: the configuration parameters used (executed) by the execution object, and/or the operation performed by the execution object. The inference result is published in the inference result application link. For example, the inference result can be uniformly planned by the execution (actor) entity, for example, the execution entity can send the inference result to one or more execution objects (for example, network devices or terminal devices, etc.) for execution. For example, the execution entity can also feedback the performance of the model to the data source to facilitate the subsequent implementation of the model update training.

It is understandable that a network element with artificial intelligence function may be included in the communication system. The above-mentioned links related to the AI model design can be performed by one or more network elements with artificial intelligence function. In one possible design, an AI function (such as an AI module or an AI entity) can be configured in an existing network element in the communication system to implement AI-related operations, such as training and/or reasoning of an AI model. For example, the existing network element may be a network device or a terminal device. Or in another possible design, an independent network element may be introduced into the communication system to perform AI-related operations, such as training an AI model. The independent network element may be referred to as an AI network element, an AI node, an AI entity, etc., and the embodiments of the present application do not limit this name. Exemplarily, the AI network element may be directly connected to the network device in the communication system, or it may be indirectly connected through a third-party network element and a network device. Among them, the third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an operation administration and maintenance (OAM), a server (such as a cloud server), an over the top (OTT) device or other network element, without limitation. Exemplarily, the independent AI network element or AI entity or AI node can be deployed on one or more of the network device side, the terminal device side, or the core network side. Optionally, it can be deployed on a server, such as a cloud server, or an OTT device, or other device. Exemplarily, an AI network element 240 is introduced in the communication system shown in Figure 2b. It will be understood that the aforementioned AI modules, AI entities, AI network elements, or AI nodes can be used to perform one or more of the AI functions, where the AI functions may include: processing of AI models, such as training and/or updating of AI models, monitoring of AI models, management of AI models, such as registration and/or deregistration of AI models, or application reasoning of AI models.

The training process of different models can be deployed in different devices or nodes, or in the same device or node. The reasoning process of different models can be deployed in different devices or nodes, or in the same device or node. Taking the completion of the model training link of the terminal device as an example, the terminal device can train the matching encoder and decoder, and then send the model parameters of the decoder to the network device. Taking the completion of the model training link of the network device as an example, after the network device trains the matching encoder and decoder, it can indicate the model parameters of the encoder to the terminal device. Taking the completion of the model training link of an independent AI network element as an example, the AI network element can train the matching encoder and decoder, and then send the model parameters of the encoder to the terminal device, and send the model parameters of the decoder to the network device. Then, the model reasoning link corresponding to the encoder is performed in the terminal device, and the model reasoning link corresponding to the decoder is performed in the network device.

Among them, the model parameters may include one or more of the following structural parameters of the model (such as the number of layers of the model, and/or weights, etc.), the input parameters of the model (such as input dimension, number of input ports), or the output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension may refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data. Similarly, the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

(6) Channel information:

In a communication system (e.g., an LTE communication system or an NR communication system, etc.), a network device needs to determine the resource, MCS, and precoding configuration of a downlink data channel of a scheduling terminal device based on channel information. It can be understood that channel information can also be referred to as channel state information (CSI) or channel environment information, which is information that can reflect channel characteristics and channel quality. In this application, the meaning of CSI is broader than that of CSI in traditional schemes, and is not limited to channel quality indication (CQI), precoding matrix indication (PMI), rank indication (RI), or CSI-RS resource indication (CRI), which can also be channel response information (such as channel response matrix), weight information corresponding to channel response, reference signal receiving power (RSRP) or signal to interference plus noise ratio (SINR), etc. One or more of the above.

CSI measurement refers to the receiver solving the channel information based on the reference signal sent by the transmitter, that is, estimating the channel information using the channel estimation method. Exemplarily, the reference signal may include one or more of the channel state information reference signal (CSI-RS), synchronization signal/physical broadcast channel block (SSB), sounding reference signal (SRS) or demodulation reference signal (DMRS). CSI-RS, SSB and DMRS can be used to measure downlink CSI. SRS and DMRS can be used to measure uplink CSI.

Taking the FDD communication scenario as an example, in the FDD communication scenario, since the uplink and downlink channels are not reciprocal or cannot be guaranteed, the network equipment usually sends a downlink reference signal to the terminal device, and the terminal device performs channel measurement and interference measurement based on the received downlink reference signal to estimate the downlink CSI. The terminal device generates a CSI report according to the protocol predefined method or the network device configuration method, and feeds it back to the network device so that it can obtain the downlink CSI.

Exemplarily, CSI may include at least one of the following: channel quality indication (CQI), precoding matrix indicator (PMI), rank indicator (RI), CSI-RS resource indicator (CRI), layer indicator (LI), reference signal receiving power (RSRP) or signal to interference plus noise ratio (SINR), etc. The signal to interference plus noise ratio may also be called signal to interference plus noise ratio.

Among them, RI is used to indicate the number of downlink transmission layers recommended by the terminal device, CQI is used to indicate the modulation and coding mode supported by the current channel conditions determined by the terminal device, and PMI is used to indicate the precoding recommended by the terminal device. The number of precoding layers indicated by PMI corresponds to RI.

It should be understood that the RI, CQI and PMI indicated in the above CSI report are only recommended values for the terminal device, and the network device may perform downlink transmission according to part or all of the information indicated in the CSI report. Alternatively, the network device may not perform downlink transmission according to the information indicated in the CSI report.

The introduction of AI technology into wireless communication networks has resulted in a CSI feedback method based on the AI model. The terminal device uses the AI model to compress and feedback the CSI, and the network device uses the AI model to restore the compressed CSI. In the AI-based CSI feedback, a sequence (such as a bit sequence) is transmitted, and the overhead is lower than that of traditional CSI feedback.

Taking FIG. 4 as an example, the encoder in FIG. 4 may be a CSI generator, and the decoder may be a CSI reconstructor. The encoder may be deployed in a terminal device, and the decoder may be deployed in a network device. The terminal device may generate CSI feedback information z from the CSI original information V through the encoder. The terminal device reports a CSI report, which may include the CSI feedback information z. The network device may reconstruct the CSI information through the decoder, that is, obtain CSI recovery information V'.

The CSI original information V may be obtained by the terminal device through CSI measurement. For example, the CSI original information V may include the channel response of the downlink channel or the eigenvector matrix (a matrix composed of eigenvectors) of the downlink channel. The encoder processes the eigenvector matrix of the downlink channel to obtain CSI feedback information z. In other words, the compression and/or quantization operation of the characteristic matrix according to the codebook in the related scheme is replaced by the operation of processing the characteristic matrix by the encoder to obtain CSI feedback information z. The terminal device reports the CSI feedback information z. The network device processes the CSI feedback information z through a decoder to obtain CSI recovery information V'.

The following further illustrates the training process and reasoning process of the AI model in the embodiment of the present application.

The training data used to train the AI model includes training samples and sample labels. For example, the training samples are channel information determined by the terminal device, and the sample labels are real channel information, i.e., true CSI. In the case where the encoder and decoder belong to the same autoencoder, the training data may only include training samples, or the training samples are sample labels.

In the field of wireless communications, the true CSI may be a high-precision CSI.

The specific training process is as follows: the model training node uses an encoder to process channel information, that is, training samples, to obtain channel feedback information, such as CSI feedback information, and uses a decoder to process the feedback information to obtain recovered channel information, that is, channel recovery information, such as CSI recovery information. Then, the difference between the channel recovery information and the corresponding sample label is calculated, that is, the value of the loss function, and the parameters of the encoder and decoder are updated according to the value of the loss function, so that the difference between the recovered channel information and the corresponding sample label is minimized, that is, the loss function is minimized. Exemplarily, the loss function can be the minimum mean square error (MSE) or cosine similarity. Repeat the above operations to obtain an encoder and decoder that meet the target requirements. The above model training node can be a terminal device, a network device, or other network elements with AI functions in a communication system.

It should be understood that the above only uses the AI model for CSI compression as an example. In CSI feedback, the AI model can also be used in other scenarios. For example, the AI model can be used for CSI prediction, that is, predicting the channel information at one or more future moments based on the channel information measured at one or more historical moments. The embodiment of the present application does not limit the specific use of the AI model in the CSI feedback scenario.

It should be understood that in the present application, indication includes direct indication (also called explicit indication) and implicit indication. Wherein, direct indication of information A means including the information A; implicit indication of information A means indicating information A through the correspondence between information A and information B and direct indication of information B. Wherein, the correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured.

It should be understood that in the present application, information C is used to determine information D, which includes information D being determined based only on information C, and information D being determined based on information C and other information. In addition, information C is used to determine information D, and it can also be indirectly determined, for example, information D is determined based on information E, and information E is determined based on information C.

In addition, "network element A sends information A to network element B" in each embodiment of the present application can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, which may include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, etc., but the destination end can understand the valid information from the source end. Similar expressions in the present application can be understood similarly and will not be repeated here.

The method of the embodiment of the present application is described in detail below.

Referring to Figure 6, it is a flow chart of a model data acquisition method provided by an embodiment of the present application. Optionally, the method can be applied to the aforementioned model data acquisition system, such as the model data acquisition system shown in Figure 1. It can be understood that the example shown in Figure 6 is an example of the execution subject of the interaction diagram using the first node (data synthesis node, or it can be called a central node, a first entity, etc.) and the second node (data providing node, or it can be called a node to be served, a second entity, etc.) as an example. It can further include model processing (such as model training or updating, etc.) nodes and model use (such as model reasoning, etc.) nodes. Among them, the model processing node can be the first node, or it can be other nodes. The model use node can be the second node, or it can be other nodes, etc. The model data acquisition method shown in Figure 6 may include steps 601-602. Steps 601-602 are as follows:

601. A second node sends first information to a first node, where the first information includes the following types of information: original data and characteristic information of the original data. Correspondingly, the first node receives the first information.

Exemplarily, the first node may be a data synthesis node, or may be referred to as a data generation node, a central node, a first entity, etc. The second node may be a data providing node, or may be referred to as a node to be served, a second entity, etc. Optionally, the first node is an access network device, and the second node is a terminal device.

The original data is measured data that is not compressed or has no information loss. By providing the original data to the first node, the second node helps to make the synthetic data generated by the first node have the same dimension and accuracy as the original data. Among them, synthetic data is a kind of non-artificially created data that imitates real-world data. It is created by computing algorithms and simulations based on generative artificial intelligence technology. The synthetic data set has the same mathematical properties as the actual data on which it is based, but does not contain the same information.

The feature information of the original data is the information obtained by extracting the features of the original data. The feature information of the original data extracts the key information in the original data. By providing the feature information of the original data to the first node, the second node can make the generation model understand in which features the generated data should be similar to the original data.

The characteristic information of the original data may include characteristic quantity information. Alternatively, the characteristic information of the original data may include not only characteristic quantity information but also characteristic information corresponding to the characteristic quantity. For example, for channel information, the characteristic quantity information may be the angle distribution of the channel, the delay distribution of the channel, etc.; the characteristic information corresponding to the characteristic quantity may be the value of the angle distribution of the channel, the value of the delay distribution of the channel, etc.

The type of the first information is the type of information included in the first information. For example, when the type of the first information is A, the first information includes the original data and the characteristic information of the original data; when the type of the first information is B, the first information includes the original data; when the type of the first information is C, the first information includes the characteristic information of the original data.

Optionally, the first information further includes at least one of the following types of information: the priority of the original data, and a data label of the original data.

By reporting the priority of the original data, the first node determines the number of similar synthetic data to be generated according to the priority or probability of each original data, so that the distribution of the synthetic data is consistent with expectations, for example, the distribution of the synthetic data is consistent with the scenario to which the original data is applied. For example, taking channel information as an example, if the second node is in an outdoor environment and the obstruction from the base station to the user equipment is less than that indoors, the measured channel information based on the line-of-sight (LOS) path of the wireless signal can be given a higher priority, while the measured channel based on the non-line-of-sight (NLOS) path of the wireless signal can be given a lower priority, so that the synthesized channel information can be based on the sparse channel under the LOS path.

The data label of the original data can be understood as the category to which the original data belongs obtained by classifying the original data. For example, if the original data is classified according to the scene to which it belongs, the label is the scene classification label.

Data labeling (or data annotation) is part of the preprocessing phase when developing a machine learning (ML) model. It is responsible for identifying raw data (such as images, text files, videos), and then one or more data labels can be added to the raw data to specify the context of the model and help the machine learning model make accurate predictions. Data labeling supports a variety of different machine learning and deep learning use cases, including computer vision and natural language processing (NLP).

The priority of the original data can be taken from the predefined P level. For example, the meaning of each priority level can be predefined. For example, for the original data with priority level P1, the generation model generates N1 synthetic data similar to it, and for the original data with priority level P2, the generation model generates N2 synthetic data similar to it. Alternatively, the number of synthetic data generated by the generation model that is similar to the original data with priority level P1 is x1, and the number of synthetic data generated by the generation model that is similar to the original data with priority level P2 is x2, and the ratio of x1 to x2 is equal to the ratio of N1 to N2, where N1 and N2 are predefined values.

Exemplarily, the second node sends the first information to the first node, and the first information includes: original data, feature information of the original data, and a data label of the original data. For example, taking the original data as channel state information, the feature information of the original data can be an extracted channel feature vector (one or more feature vectors obtained after performing singular value decomposition (SVD) on the original channel), or compressed information after compressing the channel feature vector. The data label of the original data can be channel quality information (Channel Quality Indication (CQI), Rank Indication (RI), etc.), or scenario information (channel NLOS/LOS classification information, user channel high/medium/low speed classification information, or other scenario classification information), etc.

The second node sends the first information to the first node so that the first node generates multiple synthetic data based on the first information, wherein the second node uses the characteristic information of the original data as the condition for synthesizing the data, so that the synthetic data imitates the original data in some specific characteristic dimensions, which can improve the diversity of the synthetic data and also improve the accuracy of the synthetic data.

In a possible implementation, step 603 may also be included, in which the first node sends second information to the second node. The second information indicates a way in which the second node obtains the first information, such as one or more of an acquisition path, an acquisition source, or parameters involved in the acquisition path or source. For example, when the first information includes the characteristic information of the original data, the way in which the second node obtains the first information includes a way in which the characteristic information of the original data is acquired. That is, the first node indicates to the second node a way in which the characteristic information of the original data is acquired, so that the second node processes the original data based on the way in which the characteristic information of the original data is acquired to obtain the characteristic information of the original data.

For example, the method for acquiring the feature information of the original data may be D1: extracting N feature vectors of the original data, or D2: extracting N feature vectors from the feature vectors corresponding to the first M largest eigenvalues of the original data matrix. For another example, D3: the method for acquiring the feature information of the original data is compressed information obtained by using the R16/R17 codebook method. For another example, D4: the method for acquiring the feature information of the original data is compressed information obtained by using an AI-based encoder. For another example, the method for acquiring the feature information of the original data may also be a feature quantity to indicate that the second node calculates the feature information corresponding to the corresponding feature quantity. Specifically, the second information may include an acquisition method identifier (e.g., D1/D2/D3/D4), or may include parameters under a certain acquisition method (e.g., for method D1, it may indicate the number of feature vectors N; for method D4, it may indicate the identifier of the AI encoder).

Optionally, when the first information includes the raw data, the way in which the second node obtains the first information includes the way in which the raw data is acquired. For example, the way in which the raw data is acquired may be to acquire specific data within a specified time, where the specified time may be, for example, from 9:00 to 15:00 on a certain day, and the specific data may be, for example, temperature data, humidity data, text data, etc. For another example, the way in which the raw data is acquired may also be to acquire raw data of a preset area using a preset sensor, etc. The preset sensor may be, for example, a camera, a laser radar, etc.

Optionally, when the first information includes the data label of the original data, the way in which the second node obtains the first information also includes a way to obtain the data label of the original data. For example, the second information indicates the way or standard by which the second node determines the data label of the data. Taking channel information as an example, E1: indicates that the data label is determined according to sparsity (for example, according to a threshold of the sparsity of the channel, the channel label is determined to be LOS or NLOS); or, E2: determines the data label according to the delay distribution, etc.; E3: or directly indicates the label, for example, indicates that the data label is the CQI value of the channel, E4: or indicates that the data label is the RI of the channel, etc. Specifically, the second information may include an acquisition method identifier (for example, E1/E2/E3/E4).

The first node sends the second information to the second node, so that the second node processes the original data based on the second information, thereby obtaining the first information.

In a possible implementation, step 604 may also be included, the first node also sends third information to the second node, and the third information indicates the reporting configuration of the first information. Exemplarily, the reporting configuration includes at least one of the following: the type of the first information, the respective quantity or total quantity of one or more types of the first information. That is, the first node sends the third information to the second node so that the second node reports based on the type of the first information indicated in the third information and/or the quantity of the first information. For example, multiple types of first information are predefined. Furthermore, when the third information indicates that the first information is a type identifier, the second node can determine the type of information included in the first information. For example, according to the type identifier of the first information predefined in the previous text, the third information indicates that the type of the first information is A, then it can be known that the first information includes the above-mentioned original data and the characteristic information of the original data; or, the information type identifier contained in the first information is predefined, and the identifier of the original data type is defined as A_1, the identifier of the original data characteristic information type is defined as A_2, the identifier of the tag type is defined as A_3, and the identifier of the priority type is defined as A_4, then the third information indicates the identifier of the information type contained in the first information, for example, the third information indicates that the type of the first information is {A_1, A_2, A_3}, then the second node can determine that the first information includes the above-mentioned original data and the characteristic information of the original data. information and labels of the original data; for another example, when the third information also indicates that the data volume of the first information is 1000, it means that the second node needs to report that the data volume of the above original data and the characteristic information of the original data are 1000 respectively; or it can also indicate that the sum of the data volume of the original data and the characteristic information of the original data is 1000, etc., wherein, for example, the second node reports that the data volume of the above original data is 500, and the data volume of the characteristic information of the original data is 500, or the data volume of the characteristic information of the original data is more or less than the data volume of the original data, etc., for example, the ratio of the two is 6:4, and the ratio can be predefined or configured, and this scheme does not limit this. For another example, multiple values of a quantity can be predefined, and the third information indicates an identifier of the predefined quantity value so that the second node determines the respective quantity or total quantity of one or more types of first information.

Optionally, the reporting configuration may also include one or more of the time domain resource information or the frequency domain resource information of the first information. This solution does not impose any limitation on this.

The first node sends the third information to the second node so that the second node reports based on the reporting configuration of the first information indicated in the third information, so that the data provider and the synthesized data end separated from each other can align their understanding of the data and improve the quality of the synthesized data.

In a possible implementation, at least two of the first information, the second information, and the third information may be different contents included in the same information sent by the first node, or the first information, the second information, and the third information may be contents in different messages sent by the first node. This solution does not limit this.

It can be understood that the content not indicated in the above first information, second information and third information, such as reported time domain resource information or frequency domain resource information, etc., may be predefined by the protocol, or pre-set or stored in the first node and the second node, such as a default configuration, or determined based on other information.

Based on the indication of the above-mentioned second information and/or third information, the second node processes the original data, and then sends the first information to the first node. For example, the third information indicates that the type of the first information is A, that is, the first information includes the above-mentioned original data and the characteristic information of the original data; the second information indicates that the method for obtaining the characteristic information of the original data is to extract N characteristic vectors of the original data, then the second node determines the original data and the characteristic information of the original data, and reports it according to the indication of the third information. Optionally, if the third information indicates that the first information contains the characteristic information of the original data, but the second node does not receive an indication of the method for obtaining the characteristic information, a predefined method for obtaining the characteristic information can be used. Similarly, if the third information indicates that the first information contains the data label of the original data, but the second node does not receive an indication of the method for obtaining the data label of the original data, a predefined method for obtaining can be used.

In a possible implementation, the method may further include step 605, wherein the first node further sends fourth information to the second node, wherein the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The joint processing may, for example, be weighted summation of feature information of multiple original data of the same group.

The first node sends the fourth information to the second node so that the second node can divide the original data that can be jointly processed into the same group, while dividing the original data that cannot be jointly processed into the same group, thereby avoiding the fusion of original data from different groups to generate unexpected synthetic data.

Optionally, the first information further includes the following types of information: group information of the original data. The second node reports the group information of the original data to the first node, and then the first node can jointly process feature information of multiple original data in the same group.

It can be understood that in this example, the raw data that can be jointly processed are divided into the same group. The group information may be different from the data label of the raw data. For example, the division criteria of the raw data group and the division criteria of the data label of the raw data may be different. Of course, the division criteria of the two may also be the same, that is, the group information is the data label, and this solution does not limit this.

It can be understood that step 603, step 604 and step 605 in FIG. 6 are one or more optional steps. The steps shown in FIG. 6 are only examples, and there is no limitation on the order and time of their execution.

602. The first node generates a plurality of synthetic data based on the first information, and the plurality of synthetic data are used as input of the model.

The first node generates a plurality of synthetic data based on the original data and the characteristic information of the original data. For example, the first node inputs the original data and the characteristic information of the original data into the generation model, thereby generating a plurality of synthetic data. Alternatively, the first node preprocesses the first information, and then inputs the processed information into the generation model, thereby generating a plurality of synthetic data. The preprocessing may, for example, be normalizing the first information, or splicing the original data in the first information and the characteristic information of the original data.

Among them, in the traditional data generation method, the original measured data is used as the condition for synthesizing data, and the generation model will imitate the original data, including aspects such as data dimension accuracy and content, so that the synthesized data is highly similar to the measured data. However, using only the original data as input can easily lead to insufficient diversity of the synthesized data. In the scheme of the present application, the feature information of the original data, or the feature information of the original data and the data label are used as the condition for synthesizing data, and the generation model imitates the original data in some specific feature dimensions, which can improve the diversity of the synthesized data. Therefore, further, based on the joint representation of the original data and the feature information of the original data, or the joint representation of the original data, the feature information of the original data and the data label of the original data as the condition for synthesizing data, data generation is performed with a small amount of data, which can simultaneously meet the requirements of high diversity of the synthesized data and close feature distribution of the synthesized data to the feature distribution of the original data.

For example, taking channel generation as an example, the original data is the channel information, and the characteristic information of the original data may include channel angle power spectrum characteristics, channel delay spectrum characteristics, etc. Then the generation model can know that the generated channel should be similar to the original channel information in angle and delay power spectrum distribution, while other characteristic values, such as the randomness of Doppler frequency deviation, can be higher.

For another example, weighted summing of feature information of multiple original data of the same group can improve the diversity of synthesized data. For example, taking channel generation as an example, the original data is channel information, and the feature information of the original data may include channel angle power spectrum features, channel delay spectrum features, etc. By weighted summing of feature information of multiple channel information with different angle power spectrum distributions and inputting them into the generation model, channels with more diverse angle power spectrum distributions can be synthesized, thereby further improving the diversity of synthesized data and the accuracy of synthesized data.

Among them, the above-mentioned multiple synthetic data can be used as the input of the model, and the input can be used for model processing, such as model training, model updating, model reasoning or model performance monitoring.

The model training, updating, etc. can be performed at the first node, or the synthesized data can be transmitted to the model processing node, etc., for model training, updating, etc. After the model training or updating is completed, the model can be inferred, or the model can be transmitted to the model using node for model inference. For example, it can be transmitted to the second node for model inference.

In a possible implementation manner, the first node further sends at least a subset of the plurality of synthesized data to the second node.

At least one subset of the multiple synthetic data may be, for example, a subset of the synthetic data obtained by randomly sampling multiple samples from the multiple synthetic data. For another example, multiple samples are randomly sampled from the synthetic data obtained according to at least one of the multiple data labels to obtain at least one subset of the synthetic data. A possible implementation is that the data label of the original data has three possible values (for example, data label a, data label b and data label c), one subset of the synthetic data is a subset obtained by randomly sampling multiple samples from the synthetic data obtained by inputting data label a as a condition, another subset of the synthetic data is a subset obtained by randomly sampling multiple samples from the synthetic data obtained by inputting data label b (or data label c) as a condition, and so on. Alternatively, a subset of the synthetic data may be obtained by randomly sampling multiple samples S1 from the synthetic data obtained by inputting data label a as a condition, randomly sampling multiple samples S2 from the synthetic data obtained by inputting data label b as a condition, and randomly sampling multiple samples S3 from the synthetic data obtained by inputting data label c as a condition, and then obtaining one of the subsets based on the multiple samples S1, multiple samples S2, and multiple samples S3, and obtaining another subset by repeating the sampling method again. Of course, the subset may also be obtained in other ways, and this solution does not limit this.

The second node sends fifth information to the first node based on the received at least one subset. The fifth information indicates an evaluation result of at least one synthetic data in the at least one subset. The evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset. Accordingly, the first node receives the fifth information.

The synthetic data to be eliminated may be, for example, synthetic data having a different characteristic distribution from the original data of the second node. In a possible implementation, the second node obtains the evaluation result by determining whether the received synthetic data is similar to the actual measured data. For example, the second node calculates the distance between a synthetic data in at least one subset and its local data. If there is a local data such that the distance between the synthetic data and the local data satisfies the first condition, the synthetic data is determined to have the same characteristic distribution as the original data, i.e., it is not eliminated; if the distance between the synthetic data and all local data does not meet the first condition, the synthetic data is determined to have a different characteristic distribution from the original data, i.e., it needs to be eliminated. Among them, the first condition may be a certain specific range, or a certain threshold, etc. Optionally, the calculation of the distance between the synthetic data and the local data may be to use the norm of the difference between the synthetic data and the local data as the distance between the synthetic data and the local data. Alternatively, the data feature information of the synthetic data and the local data is extracted respectively, and the norm of the difference between the data feature information of the synthetic data and the local data is used as the distance between the synthetic data and the local data.

Optionally, the fifth information may indicate whether each synthesized data is to be removed by using 0 or 1, etc. Alternatively, the second node indicates the synthesized data to be removed from the synthesized data subset by using a list of synthesized data identifiers or indicates the synthesized data not to be removed from the synthesized data subset by using a list of synthesized data identifiers. Each synthesized data in the synthesized data subset has a unique identifier.

The second node may also obtain the above evaluation result based on other methods, which is not limited in this solution.

The first node filters the multiple synthetic data based on the received evaluation result, and thereby obtains at least one synthetic data after filtering.

Optionally, the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the plurality of synthesized data. The first synthesized data to be eliminated is the synthesized data to be eliminated determined according to the fifth information.

For example, the first node calculates the distance between the synthetic data to be removed and other synthetic data in the first node. If there is a synthetic data such that the distance between the synthetic data and the synthetic data to be removed is not greater than a certain threshold, it is determined that the synthetic data is also removed.

By screening the synthetic data through the synthetic data evaluation process, the quality of the synthetic data can be effectively improved.

The at least one synthetic data after screening is used as the input of the model. For the introduction of this part, please refer to the description of step 601, which will not be repeated here.

Optionally, the first node is a third-party device that performs the aforementioned actions related to the first node. For example, the above steps 601 and 602 are both performed by a third-party device.

Optionally, the second node is a third-party device that performs the aforementioned second node-related actions. For example, the above step 601 is performed by a third-party device.

Optionally, the first node is a network device. For example, the above steps 601 and 602 are both performed by the network device.

Optionally, the second node is a terminal device. For example, the above step 601 is performed by the terminal device.

Optionally, the first node includes a network device and a third-party device. In one example, the above step 602 can be performed by a third-party device, such as an OTT, or a cloud server, and the above step 601 can be performed by the network device. In addition, the network device and the third-party device can also communicate with each other to transmit the content transmitted in the above step 601.

In an embodiment of the present application, the first node receives the original data from the second node and the feature information of the original data; and then generates multiple synthetic data based on the original data and the feature information of the original data. The multiple synthetic data are used for model processing. In this example, synthetic data that meets the diversity requirements and has the same data distribution as the measured data distribution can be generated based on the original data and the feature information of the original data, thereby improving the quality of the synthetic data, and thus helping to improve the performance of model training or updating.

The above example is introduced by taking the first node sending the second information to the second node, and the second information indicates the method for obtaining the characteristic information of the original data as an example. Alternatively, the second information can also be sent by the third node to the second node (the embodiment shown in Figure 7). That is to say, the third node indicates the method for obtaining the characteristic information of the original data to the second node, so that the second node processes based on the method for obtaining the characteristic information of the original data to obtain the characteristic information of the original data. Among them, the third node can be a model training node or a model use node, and can also be called a third entity, such as a model training entity or a model use entity.

The above is introduced by taking the second node processing the original data to obtain the characteristic information of the original data as an example. Alternatively, the first node may process the original data to obtain the characteristic information of the original data (the embodiment shown in FIG8 and the embodiment shown in FIG9 ) or the third node may process the original data to obtain the characteristic information of the original data (the embodiment shown in FIG10 ).

The above alternative solutions are described in detail below based on Figures 7, 8, 9 and 10 respectively.

Referring to Figure 7, it is a flow chart of another model data acquisition method provided by an embodiment of the present application. The example shown in Figure 7 is illustrated by taking the first node (data synthesis node, or it can be called a central node, a first entity, etc.), the second node (data providing node, or it can be called a node to be served, a second entity, etc.) and the third node (model training node or model use node, or it can be called a third entity, such as a model training entity or a model use entity) as an example of the execution subject of the interaction diagram. The difference between this example and the example shown in Figure 6 is that the second information is sent by the third node to the second node, rather than by the first node to the second node. The model data acquisition method shown in Figure 7 may include steps 701-705. Steps 701-705 are as follows:

701. A first node sends third information to a second node, where the third information indicates a reporting configuration of the first information. Correspondingly, the second node receives the third information.

For the introduction of this part, please refer to the description of step 601 in the embodiment shown in FIG6 , which will not be repeated here.

702. The third node sends second information to the second node, where the second information indicates a method for acquiring characteristic information of the original data. Correspondingly, the second node receives the second information.

The third node determines a feature extraction method for the original data according to the model requirements, and then sends a method for obtaining feature information of the original data to the second node.

703. The second node processes the original data based on the second information to obtain characteristic information of the original data.

For the introduction of this part, please refer to the description of step 601 in the embodiment shown in FIG. 6 , which will not be repeated here.

Optionally, in a first possible implementation manner, the third node further sends a method for obtaining the data label of the original data to the second node. For example, the second information further indicates a method for obtaining the data label of the original data.

In a second possible implementation, the first node sends second information to the second node, where the second information indicates a processing method for the second node to obtain the first information, and the processing method for the second node to obtain the first information includes a method for obtaining a data label of the original data.

704. The second node sends first information to the first node, where the first information includes the following types of information: original data and characteristic information of the original data. Correspondingly, the first node receives the first information.

Optionally, the first information also includes the following types of information: data labels of original data.

For the introduction of this part, please refer to the description of step 601 in the embodiment shown in FIG6 , which will not be repeated here.

705. The first node generates a plurality of synthetic data based on the first information, and the plurality of synthetic data are used for processing the model.

For the introduction of this part, please refer to the description of step 602 in the embodiment shown in FIG. 6 , which will not be repeated here.

Optionally, the first node is a third-party device that performs the aforementioned actions related to the first node. For example, the above steps 701, 704, and 705 are all performed by a third-party device.

Optionally, the second node is a third-party device that performs the aforementioned actions related to the second node. For example, the above steps 701, 702, 703, and 704 are performed by a third-party device.

Optionally, the third node is a third-party device that performs the aforementioned third-node related actions. For example, the above step 702 is performed by a third-party device.

Optionally, the first node is a network device. For example, the above steps 701, 704, and 705 are all performed by the network device.

Optionally, the second node is a terminal device. For example, the above steps 701, 702, 703, and 704 are performed by the terminal device.

Optionally, the third node is an AI node. For example, the above step 702 is performed by the AI node.

Optionally, the first node includes a network device and a third-party device. In one example, the above step 705 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 701 and 703 can be performed by the network device. In addition, the network device and the third-party device can also communicate with each other to transmit the content transmitted in the above steps 701 and 703.

Optionally, the second node includes a terminal device and a third-party device. In one example, the above step 703 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 701, 702, and 704 can be performed by the terminal device. In addition, the terminal device and the third-party device can also communicate with each other to transmit the content transmitted in the above steps 701, 702, and 704.

Optionally, the third node is an AI entity. In one example, the above step 702 can be performed by a third-party device, such as OTT, or a cloud server.

In an embodiment of the present application, the second node processes the original data based on the method for obtaining the characteristic information of the original data from the third node to obtain the characteristic information of the original data. Furthermore, the second node sends the original data and the characteristic information of the original data to the first node. The first node generates multiple synthetic data based on the original data from the second node and the characteristic information of the original data. In this example, the model training node or the model use node (third node) determines the method for obtaining the characteristic information of the original data (or the method for obtaining the data label of the original data) according to the model requirements, which can improve the quality of the synthetic data, thereby helping to improve the performance of model training or updating.

Referring to FIG8 , it is a flowchart of another model data acquisition method provided by an embodiment of the present application. The example shown in FIG8 is illustrated by taking the first node (data synthesis node, or can be called a central node, a first entity, etc.) and the second node (data providing node, or can be called a node to be served, a second entity, etc.) as an example of the execution subject of the interaction. The model data acquisition method shown in FIG8 may include steps 801-805. Steps 801-805 are as follows:

801. A first node sends seventh information to a second node, where the seventh information indicates a reporting configuration of sixth information. Correspondingly, the second node receives the seventh information.

For example, the seventh information indicates that the sixth information includes original data; or the seventh information indicates that the sixth information includes original data and a data tag of the original data; or the seventh information indicates that the sixth information includes original data, a data tag of the original data, a priority of the original data, etc.

Optionally, when the seventh information indicates that the sixth information includes the data label of the original data, the first node also sends eighth information to the second node, and the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining the data label of the original data.

Of course, the method for obtaining the data label of the original data can also be determined by the second node according to the model processing requirements, and this solution does not limit this.

Furthermore, the second node obtains the data label of the original data based on the method for obtaining the data label of the original data and the original data.

The second node also reports the data label of the original data to the first node.

For the introduction of this part, please refer to the description of step 601 in the embodiment shown in FIG6 , which will not be repeated here.

802. The second node sends sixth information to the first node, where the sixth information includes the following type of information: original data. Correspondingly, the first node receives the sixth information.

803. The second node further sends ninth information to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data. Correspondingly, the first node receives the ninth information.

That is to say, in this example, the method for acquiring the characteristic information of the original data is determined by the second node. The second node can determine the method for acquiring the characteristic information of the original data according to the model requirements.

804. The first node obtains characteristic information of the original data based on the sixth information and the ninth information.

The first node can obtain the characteristic information of the original data based on the received original data and the method for obtaining the characteristic information of the original data.

805. The first node generates a plurality of synthetic data based on the feature information of the original data and the sixth information, and the plurality of synthetic data are used for processing the model.

For the introduction of this step, please refer to the description of step 602 in the embodiment shown in FIG. 6 , which will not be repeated here.

Optionally, the first node is a third-party device that performs the aforementioned actions related to the first node. For example, the above steps 801-805 are all performed by a third-party device.

Optionally, the second node is a third-party device that performs the aforementioned actions related to the second node. For example, the above steps 801-803 are performed by a third-party device.

Optionally, the first node is a network device. For example, the above steps 801-805 are all performed by the network device.

Optionally, the second node is a terminal device. For example, the above steps 801-803 are performed by the terminal device.

Optionally, the first node includes a network device and a third-party device. In one example, the above steps 804 and 805 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 801-803 can be performed by the network device. In addition, the network device and the third-party device can also communicate with each other to transmit the content transmitted in the above steps 801-803.

In the embodiment of the present application, the first node processes the original data from the second node and the method for obtaining the characteristic information of the original data to obtain the characteristic information of the original data. Then the first node generates multiple synthetic data based on the characteristic information of the original data and the original data. The second node determines the method for obtaining the characteristic information of the original data according to the model requirements, improves the quality of the synthetic data, and then helps to improve the performance of model training or updating.

For another example, the following is an introduction to the method of obtaining characteristic information of the original data indicated by the third node to the first node. Referring to Figure 9, it is a flow chart of another model data acquisition method provided by an embodiment of the present application. The example shown in Figure 9 is illustrated by taking the first node (data synthesis node, or it can be called a central node, a first entity, etc.), the second node (data providing node, or it can be called a node to be served, a second entity, etc.) and the third node (model training node or model using node, also referred to as a third entity, such as a model training entity or a model using entity) as an example of the execution subject of the interaction. The model data acquisition method shown in Figure 9 may include steps 901-905. Steps 901-905 are as follows:

901. A first node sends seventh information to a second node, where the seventh information indicates a reporting configuration of sixth information. Correspondingly, the second node receives the seventh information.

For example, the seventh information indicates that the sixth information includes original data; or the seventh information indicates that the sixth information includes original data and a data tag of the original data; or the seventh information indicates that the sixth information includes original data, a data tag of the original data, a priority of the original data, etc.

Optionally, when the seventh information indicates that the sixth information includes the data label of the original data, the first node also sends eighth information to the second node, and the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining the data label of the original data.

Of course, the method for obtaining the data label of the original data can also be determined by the second node according to the model processing requirements, and this solution does not limit this.

Furthermore, the second node obtains the data label of the original data based on the method for obtaining the data label of the original data and the original data.

For the introduction of this part, please refer to the record of step 601 in the embodiment shown in FIG6 , which will not be repeated here.

902. The second node sends sixth information to the first node, where the sixth information includes the following type of information: original data. Correspondingly, the first node receives the sixth information.

903. The third node sends ninth information to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data. Correspondingly, the first node receives the ninth information.

The third node may be a model training node or a model use node. The model training node or the model use node determines a feature extraction method of the original data according to the model requirements, and then sends the feature extraction method of the original data to the first node.

In a possible implementation, when the seventh information indicates that the sixth information includes the data label of the original data, the third node also sends the second node a method for obtaining the data label of the original data. That is, the method for obtaining the data label of the original data may be indicated by the third node to the second node.

In a possible implementation, the third node also sends the first node a method for obtaining the data label of the original data. That is, the method for obtaining the data label of the original data may be indicated by the third node to the first node.

904. The first node obtains characteristic information of the original data based on the ninth information and the sixth information.

905. The first node generates a plurality of synthetic data based on the feature information of the original data and the sixth information, and the plurality of synthetic data are used for processing the model.

For the introduction of this part, please refer to the record of step 602 in the embodiment shown in FIG6 , which will not be repeated here.

Optionally, the first node is a third-party device that performs the aforementioned actions related to the first node. For example, the above steps 901-905 are all performed by a third-party device.

Optionally, the second node is a third-party device that performs the aforementioned second-node-related actions. For example, the above steps 901 and 902 are performed by a third-party device.

Optionally, the third node is a third-party device that performs the aforementioned third-node related actions. For example, the above step 903 is performed by a third-party device.

Optionally, the first node is a network device. For example, the above steps 901-905 are all performed by the network device.

Optionally, the second node is a terminal device. For example, the above steps 901 and 902 are performed by the terminal device.

Optionally, the third node is an AI node. For example, the above step 903 is performed by the AI node.

Optionally, the first node includes a network device and a third-party device. In one example, the above steps 904 and 905 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 901-903 can be performed by the network device. In addition, the network device and the third-party device can also communicate with each other to transmit the content transmitted in the above steps 901 and 903.

Optionally, the third node is an AI entity. In one example, the above step 903 can be performed by a third-party device, such as OTT, or a cloud server.

In an embodiment of the present application, the first node obtains characteristic information of the original data based on the original data from the second node and the characteristic information of the original data from the third node. Then the first node generates multiple synthetic data based on the characteristic information of the original data and the original data. In this example, synthetic data that meets the diversity requirements and has the same data distribution as the measured data distribution can be generated based on the original data and the characteristic information of the original data, thereby improving the quality of the synthetic data and thus helping to improve the performance of model training or updating.

The following is an introduction using the example of a third node processing the original data to obtain the characteristic information of the original data. Referring to Figure 10, it is a flow chart of another model data acquisition method provided in an embodiment of the present application. Optionally, the method can be applied to the aforementioned model data acquisition system, such as the model data acquisition system shown in Figure 1. The example shown in Figure 10 is illustrated by taking the first node (data synthesis node, or it can be called a central node, a first entity, etc.), the second node (data providing node, or it can be called a node to be served, a second entity, etc.) and the third node (model training node or model using node, also referred to as a third entity, such as a model training entity or a model using entity) as an example of the execution subject of the interaction diagram. The model data acquisition method shown in Figure 10 may include steps 1001-1007. Steps 1001-1007 are as follows:

1001. A first node sends seventh information to a second node, where the seventh information indicates a reporting configuration of sixth information. Correspondingly, the second node receives the seventh information.

For example, the seventh information indicates that the sixth information includes original data; or the seventh information indicates that the sixth information includes original data and a data tag of the original data; or the seventh information indicates that the sixth information includes original data, a data tag of the original data, a priority of the original data, etc.

For the introduction of this part, please refer to the description of step 601 in the embodiment shown in FIG. 6 , which will not be repeated here.

1002. The second node sends sixth information to the first node, where the sixth information includes the following type of information: original data. Correspondingly, the first node receives the sixth information.

1003. The second node sends original data to the third node. Correspondingly, the third node receives the original data.

The second node not only sends the original data to the first node, but also sends the original data to the third node.

The third node may be a model training node or a model use node.

1004. The third node processes the original data to obtain feature information of the original data.

The third node (model training node or model use node) determines a method for obtaining the characteristic information of the original data according to the model requirements, and then processes the received original data based on the method for obtaining the characteristic information of the original data to obtain the characteristic information of the original data.

1005. The first node sends tenth information to the third node, where the tenth information indicates a reporting configuration of the eleventh information. Correspondingly, the third node receives the tenth information.

The eleventh information includes the following types of information: characteristic information of the original data. The reporting configuration may include, for example, the characteristic information of the original data and the data volume of the characteristic information of the original data.

In a possible implementation manner, the tenth information further indicates a reporting configuration of a data tag of the original data.

That is to say, the data label of the original data may be reported by the third node or by the second node, and this solution does not impose any restriction on this.

1006. The third node sends eleventh information to the first node, where the eleventh information includes the following type of information: characteristic information of original data. Correspondingly, the first node receives the eleventh information.

That is, the third node reports the characteristic information of the original data to the first node.

1007. The first node generates a plurality of synthetic data based on the sixth information and the eleventh information, and the plurality of synthetic data are used for processing the model.

For the introduction of the above steps, please refer to the description of step 602 in the embodiment shown in FIG. 6 , which will not be repeated here.

Exemplarily, when the eleventh information includes the data label of the original data, in a possible implementation, the first node also sends an eighth information to the second node, and the eighth information indicates a processing method for the second node to obtain the sixth information, and the processing method for the second node to obtain the sixth information includes a method for obtaining the data label of the original data.

Alternatively, the third node sends the eighth information to the second node, the eighth information indicating the processing method of the second node to obtain the sixth information, and the processing method of the second node to obtain the sixth information includes the method of obtaining the data label of the original data. This solution does not limit this.

This example takes the data label of the original data reported by the second node as an example.

Alternatively, the data label of the original data may also be reported by the third node. Exemplarily, when the eleventh information includes the data label of the original data, the third node determines the method for obtaining the data label of the original data according to the model requirements. The third node processes the original data based on the method for obtaining the data label of the original data to obtain the data label of the original data. Then, the third node sends the data label of the original data to the first node.

Optionally, the first node is a third-party device that performs the aforementioned actions related to the first node. For example, the above steps 1001, 1002, 1005-1007 are all performed by a third-party device.

Optionally, the second node is a third-party device that performs the aforementioned actions related to the second node. For example, the above steps 1001-1003 are performed by a third-party device.

Optionally, the third node is a third-party device that performs the aforementioned actions related to the third node. For example, the above steps 1003-1006 are performed by a third-party device.

Optionally, the first node is a network device. For example, the above steps 1001, 1002, 1005-1007 are all performed by the network device.

Optionally, the second node is a terminal device. For example, the above steps 1001-1003 are performed by the terminal device.

Optionally, the third node is an AI node. For example, the above steps 1003-1006 are performed by the AI node.

Optionally, the first node includes a network device and a third-party device. In one example, the above step 1007 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 1001, 1002, 1005, and 1006 can be performed by the network device. In addition, the network device and the third-party device can also communicate with each other to transmit the content transmitted in the above steps 1001, 1002, 1005, and 1006.

Optionally, the third node includes a third-party device, and a network device or a terminal device. In one example, the above step 1004 can be performed by a third-party device, such as an OTT, or a cloud server, and the above steps 1003, 1005, and 1006 can be performed by a network device or a terminal device. In addition, the network device or the terminal device can also communicate with the third-party device to transmit the content transmitted in the above steps 1003, 1005, and 1006.

In the embodiment of the present application, the first node generates multiple synthetic data based on the original data from the second node and the feature information of the original data from the third node. In this example, synthetic data that meets the diversity requirements and has the same data distribution as the measured data distribution can be generated based on the original data and the feature information of the original data, thereby improving the quality of the synthetic data and further helping to improve the performance of model training or updating.

It should be noted that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and/or descriptions between the various embodiments are consistent and can be referenced mutually, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

It should be noted that the second node and the third node in the embodiment of the present application may be located in the same node or in two different nodes, and this solution does not impose any restrictions on this.

The above detailed description of the method of the embodiment of the present application, the following provides a device of the embodiment of the present application. It can be understood that in the various device embodiments of the present application, the division of multiple units or modules is only a logical division according to function, and is not used as a limitation on the specific structure of the device. In a specific implementation, some functional modules may be subdivided into more small functional modules, and some functional modules may also be combined into one functional module, but no matter whether these functional modules are subdivided or combined, the general process performed by the device is the same. For example, some devices contain a receiving unit and a sending unit. In some designs, the sending unit and the receiving unit can also be integrated into a communication unit, which can implement the functions implemented by the receiving unit and the sending unit. Usually, each unit corresponds to its own program code (or program instruction), and when the program code corresponding to each of these units is run on the processor, the unit is controlled by the processing unit to execute the corresponding process to implement the corresponding function.

The embodiments of the present application also provide a device for implementing any of the above methods, for example, a model data acquisition device is provided, including a module (or means) for implementing each step performed by the first node in any of the above methods. For another example, a model data acquisition device is provided, including a module (or means) for implementing each step performed by the second node in any of the above methods. For another example, a model data acquisition device is provided, including a module (or means) for implementing each step performed by the third node in any of the above methods.

For example, referring to FIG11 , which is a schematic diagram of a structure of a model data acquisition device provided in an embodiment of the present application. As shown in FIG11 , the device may include a transceiver module 1101 and a processing module 1102, wherein:

When the model data acquisition device is used to implement the function of the first node in the above method embodiment, the transceiver module 1101 is used to perform the operation of the first node in step 601 of the embodiment shown in FIG6, and the processing module 1102 is used to perform step 602 of the embodiment shown in FIG6; or, the transceiver module 1101 is used to perform one or more of the operations of the first node in steps 701 and 704 of the embodiment shown in FIG7, and the processing module 1102 is used to perform step 705 of the embodiment shown in FIG7; or, the transceiver module 1101 is used to perform one or more of the operations of the first node in steps 801, 802, and 803 of the embodiment shown in FIG8, And the processing module 1102 is used to execute one or more of steps 804 and 805 of the embodiment shown in Figure 8; or, the transceiver module 1101 is used to execute one or more of the operations of the first node in steps 901, 902, and 903 of the embodiment shown in Figure 9, and the processing module 1102 is used to execute one or more of the steps 904 and 905 of the embodiment shown in Figure 9; or, the transceiver module 1101 is used to execute one or more of the operations of the first node in steps 1001, 1002, 1005, and 1006 of the embodiment shown in Figure 10, and the processing module 1102 is used to execute step 1007 of the embodiment shown in Figure 10.

When the model data acquisition device is used to implement the function of the second node in the above method embodiment, the transceiver module 1101 is used to execute the operation of the second node in step 601 of the embodiment as shown in Figure 6; or, the transceiver module 1101 is used to execute one or more of the operations of the second node in steps 701, 702, and 704 of the embodiment as shown in Figure 7, and the processing module 1102 is used to execute step 703 of the embodiment as shown in Figure 7; or, the transceiver module 1101 is used to execute one or more of the operations of the second node in steps 801, 802, and 803 of the embodiment as shown in Figure 8; or, the transceiver module 1101 is used to execute one or more of the operations of the second node in steps 901 and 902 of the embodiment as shown in Figure 9; or, the transceiver module 1101 is used to execute one or more of the operations of the second node in steps 1001, 1002, and 1003 of the embodiment as shown in Figure 10.

When the model data acquisition device is used to implement the function of the third node in the above method embodiment, the transceiver module 1101 is used to execute the operation of the third node in step 702 of the embodiment as shown in Figure 7; or, the transceiver module 1101 is used to execute the operation of the third node in step 903 of the embodiment as shown in Figure 9; or, the transceiver module 1101 is used to execute one or more of the operations of the third node in steps 1003, 1005, and 1006 of the embodiment as shown in Figure 10, and the processing module 1102 is used to execute step 1004 of the embodiment as shown in Figure 10.

The specific description of the operations involved in the above modules can be found in the records of the above embodiments, which will not be repeated here.

It should be understood that the division of each module in each of the above devices is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. In addition, the modules in the model data acquisition device can be implemented in the form of a processor calling software; for example, the model data acquisition device includes a processing circuit, the processing circuit is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each module of the device, wherein the processing circuit is a processor or a part of the processing circuit in the processor, and the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory inside the device or a memory outside the device. Alternatively, the modules in the device may be implemented in the form of hardware circuits, and the functions of some or all units may be implemented by designing the hardware circuits, and the hardware circuits may be understood as one or more processors; for example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units may be implemented by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit may be implemented by a programmable logic device (PLD), and a field programmable gate array (FPGA) may be used as an example, which may include a large number of logic gate circuits, and the connection relationship between the logic gate circuits may be configured by a configuration file, so as to implement the functions of some or all of the above units. All modules of the above devices may be implemented in the form of a processor calling software, or in the form of hardware circuits, or in part by a processor calling software, and the rest by hardware circuits.

Referring to FIG. 12 , it is a schematic diagram of the hardware structure of another model data acquisition device provided in an embodiment of the present application. The model data acquisition device 1200 shown in FIG. 12 includes one or more processing circuits 1201 (one processing circuit is illustrated in the figure). The processing circuit 1201 can be one or more processors, or a circuit used for processing in one or more processors.

Optionally, the model data acquisition device 1200 may further include a transceiver circuit 1202 (indicated by a dotted line in the figure). The processing circuit 1201 and the transceiver circuit 1202 are coupled to each other. Among them, the transceiver circuit 1202 may be a transceiver or an interface circuit. For example, when the device 1200 is a network device or a terminal device or a core network device or an AI entity, the transceiver circuit 1202 may be a transceiver or an interface circuit; when the device 1200 is a chip for a network device or a terminal device or a core network device or an AI entity, the transceiver circuit 1202 may be an interface circuit. For example, the AI entity may be a third-party device, such as an OTT, or a cloud server, etc.

Optionally, the model data acquisition device 1200 may further include a memory 1203 (indicated by dotted lines in the figure). The memory 1203 is used to store instructions executed by the processing circuit 1201, or to store input data required by the processing circuit 1201 to run instructions, or to store data generated after the processing circuit 1201 runs instructions.

Optionally, the memory 1203 may be located in the one or more processors, or located outside the one or more processors, or may include a storage part located in the one or more processors and a storage part located outside the one or more processors.

Memory 1203 can be a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM).

The memory 1203 can store programs. When the program stored in the memory 1203 is executed by the processing circuit 1201, the processing circuit 1201 and the transceiver circuit 1202 are used to execute the various steps of the model data acquisition method of the embodiment of the present application.

The processing circuit 1201 is a circuit with signal processing capability. In one implementation, the processing circuit 1201 may be a circuit with instruction reading and execution capability, such as one or more of the following processors: a central processing unit CPU, a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP), etc., or a processing circuit in the aforementioned processors; in another implementation, the processing circuit 1201 may realize certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, for example, the processing circuit 1201 is one or more of the following processors: a hardware circuit implemented by an ASIC or a programmable logic device PLD, such as an FPGA or a processing circuit in the aforementioned processor. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration may be understood as the process of the processor loading instructions to implement the functions of some or all of the above modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC or a processing circuit in an ASIC, such as one or more of the following processors: a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. or a processing circuit in the aforementioned processor. The processing circuit 1201 is used to execute relevant programs to implement the functions required to be performed by the units in the model data acquisition device of the embodiment of the present application, or to execute the model data acquisition method of the method embodiment of the present application.

It can be seen that each module in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms or some processing circuits in these processors.

In addition, the modules in the above device can be fully or partially integrated together, or can be implemented independently. In one implementation, these modules are integrated together and implemented in the form of a system-on-a-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or implementing the functions of the modules of the device. The type of the at least one processor may be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

The transceiver circuit 1202 uses a transceiver device such as, but not limited to, a transceiver to implement communication between the device 1200 and other devices or a communication network. For example, information can be obtained through the transceiver circuit 1202.

It should be noted that although the device 1200 shown in FIG. 12 only shows a processing circuit, a transceiver circuit and a memory, in the specific implementation process, those skilled in the art should understand that the device 1200 also includes other devices necessary for normal operation. At the same time, according to specific needs, those skilled in the art should understand that the device 1200 may also include hardware devices for implementing other additional functions. In addition, those skilled in the art should understand that the device 1200 may also only include the devices necessary for implementing the embodiments of the present application, and does not necessarily include all the devices shown in FIG. 12.

An embodiment of the present application also provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

The embodiment of the present application further provides a computer program product including instructions. When the computer program product is executed on a computer or a processor, the computer or the processor executes one or more steps in any of the above methods.

It should be understood that in the description of the present application, unless otherwise specified, "/" indicates that the objects associated before and after are in an "or" relationship, for example, A/B can represent A or B; wherein A and B can be singular or plural. Also, in the description of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be necessarily different. Meanwhile, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the division of the unit is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling, direct coupling, or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a read-only memory (ROM), or a random access memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a tape, a disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

The above is only a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed in the embodiment of the present application should be included in the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiment of the present application should be based on the protection scope of the claims.

Claims (48)

A model data acquisition method, characterized in that it is performed by a first node or a circuit for the first node, and the method comprises: Receive first information from a second node, wherein the first information includes the following types of information: original data and feature information of the original data; generate a plurality of synthetic data based on the first information, and the plurality of synthetic data are used for processing the model. The method according to claim 1, characterized in that the method further comprises: Second information is sent to the second node, where the second information indicates a processing method for the second node to obtain the first information, and the processing method for the second node to obtain the first information includes a method for acquiring characteristic information of the original data. The method according to claim 2 is characterized in that the first information also includes the following types of information: a data label of the original data and/or a priority of the original data. The method according to claim 3 is characterized in that the processing method for the second node to obtain the first information also includes a method for obtaining the data label of the original data. The method according to any one of claims 1 to 4, characterized in that the method further comprises: Send third information to the second node, where the third information indicates a reporting configuration of the first information. The method according to claim 5 is characterized in that the reporting configuration includes at least one of the following: the type of the first information, the data volume of the first information. The method according to any one of claims 1 to 6, characterized in that the method further comprises: Fourth information is sent to the second node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The method according to any one of claims 1 to 7 is characterized in that the first information also includes the following type of information: group information of the original data. The method according to any one of claims 1 to 8, characterized in that the method further comprises: sending at least a subset of the plurality of synthesized data to the second node; Receive fifth information from the second node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset, wherein the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset. The method according to claim 9 is characterized in that the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for processing the model. The method according to claim 10 is characterized in that the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information. A model data acquisition method, characterized in that it is performed by a first node or a circuit for the first node, and the method comprises: receiving sixth information from the second node, the sixth information comprising the following types of information: raw data; Based on the sixth information and the feature information of the original data, a plurality of synthetic data are generated, and the plurality of synthetic data are used for processing the model. The method according to claim 12, characterized in that the method further comprises: Receive eleventh information from the third node, the eleventh information including the following type of information: characteristic information of the original data. The method according to claim 12, characterized in that the method further comprises: receiving ninth information from the second node, the ninth information indicating a method for the first node to obtain the characteristic information of the original data; The original data is processed based on the ninth information to obtain characteristic information of the original data. The method according to claim 12, characterized in that the method further comprises: Receive ninth information from the third node, the ninth information indicating a method for the first node to obtain characteristic information of the original data; and process the original data based on the ninth information to obtain the characteristic information of the original data. The method according to claim 12 is characterized in that the characteristic information of the original data is determined based on a method for obtaining the characteristic information of the original data, and the method for obtaining the characteristic information of the original data is determined by the first node. The method according to any one of claims 12 to 16 is characterized in that the sixth information also includes at least one of the following types of information: the priority of the original data, or the data label of the original data. The method according to any one of claims 12 to 17, characterized in that the method further comprises: Sending eighth information to the second node, the eighth information indicating a processing method for the second node to obtain the sixth information, wherein the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of original data. The method according to any one of claims 12 to 18, characterized in that the method further comprises: Send seventh information to the second node, where the seventh information indicates a reporting configuration of the sixth information. The method according to any one of claims 12 to 19 is characterized in that the reporting configuration includes at least one of the following: the type of the sixth information, or the data volume of the sixth information. The method according to any one of claims 12 to 20, characterized in that the method further comprises: Fourth information is sent to the second node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The method according to any one of claims 12 to 21 is characterized in that the sixth information also includes the following type of information: group information of the original data. The method according to any one of claims 12 to 22, characterized in that the method further comprises: sending at least a subset of the plurality of synthesized data to the second node; Receive fifth information from the second node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset, wherein the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset. The method according to claim 23 is characterized in that the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for processing the model. The method according to claim 24 is characterized in that the at least one synthesized data after screening is determined based on the distance between the first synthesized data to be eliminated and other synthesized data in the multiple synthesized data, and the first synthesized data to be eliminated is the synthesized data that needs to be eliminated determined based on the fifth information. A model data acquisition method, characterized in that it is performed by a second node or a circuit for a second node, and the method comprises: First information is sent to a first node, where the first information includes the following types of information: original data and characteristic information of the original data. The method according to claim 26, characterized in that the method further comprises: receiving second information from the first node, where the second information indicates a processing method of the second node obtaining the first information, where the processing method includes a method of acquiring characteristic information of the original data; The first information is obtained based on the second information. The method according to claim 27 is characterized in that the first information also includes the following types of information: a data label of the original data and/or a priority of the original data. The method according to any one of claims 26 to 28, characterized in that the method further comprises: Receive third information from the first node, where the third information indicates a reporting configuration of the first information. The method according to claim 29 is characterized in that the reporting configuration includes at least one of the following: the type of the first information, the data volume of the first information. The method according to any one of claims 26 to 30, characterized in that the method further comprises: Fourth information is received from the first node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The method according to any one of claims 26 to 31 is characterized in that the first information also includes the following type of information: group information of the original data. The method according to any one of claims 26 to 32, characterized in that the method further comprises: receiving at least a subset of the plurality of synthesized data from the first node; Fifth information is sent to the first node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset; the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset. The method according to claim 33 is characterized in that the evaluation result is determined based on the distance between at least one synthetic data in the at least one subset and the local data. The method according to claim 33 or 34 is characterized in that the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing. A model data acquisition method, characterized in that it is performed by a second node or a circuit for a second node, and the method comprises: Sending sixth information to the first node, the sixth information including the following types of information: original data; Ninth information is sent to the first node, where the ninth information indicates a method for the first node to obtain the characteristic information of the original data. The method according to claim 36, characterized in that the method further comprises: Receive eighth information from the first node, where the eighth information indicates a processing method for the second node to obtain the sixth information, where the processing method for the second node to obtain the sixth information includes a method for obtaining a data label of original data. The method according to claim 36 or 37, characterized in that the method further comprises: Receive seventh information from the first node, where the seventh information indicates a reporting configuration of the sixth information. The method according to any one of claims 36 to 38, characterized in that the method further comprises: Fourth information is received from the first node, where the fourth information indicates that the first node has the ability to jointly process feature information of multiple original data of the same group. The method according to any one of claims 36 to 39, characterized in that the method further comprises: receiving at least a subset of the plurality of synthesized data from the first node; Send fifth information to the first node, wherein the fifth information indicates an evaluation result of at least one synthetic data in the at least one subset, and the evaluation result indicates at least one synthetic data that needs to be eliminated in the at least one subset, or indicates at least one synthetic data that does not need to be eliminated in the at least one subset. The method according to claim 40 is characterized in that the evaluation result is determined based on the distance between at least one synthetic data in the at least one subset and the local data. The method according to claim 40 or 41 is characterized in that the evaluation result is used to filter the multiple synthetic data to obtain at least one filtered synthetic data; wherein the at least one filtered synthetic data is used for model processing. A model data acquisition method, characterized in that it is performed by a third node or a circuit for a third node, and the method comprises: Eleventh information is sent to the first node, where the eleventh information includes the following types of information: characteristic information of original data. A model data acquisition method, characterized in that it is performed by a third node or a circuit for a third node, and the method comprises: Ninth information is sent to the first node, where the ninth information indicates a method for the first node to obtain characteristic information of original data. A model data acquisition device, characterized in that it includes a module for implementing the method described in any one of claims 1-44. A communication device, characterized in that the device includes a processor and a memory, the memory is used to store program code, and the processor is used to call the program code to execute the method described in any one of claims 1-44. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method as described in any one of claims 1 to 44 is executed. A computer program product, characterized in that the computer program product contains relevant program instructions, and when the relevant program instructions are executed, the method according to any one of claims 1 to 44 is implemented.