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WO2024235043A1 - Procédé et appareil d'acquisition d'informations, et dispositif de communication - Google Patents

Procédé et appareil d'acquisition d'informations, et dispositif de communication Download PDF

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Publication number
WO2024235043A1
WO2024235043A1 PCT/CN2024/091307 CN2024091307W WO2024235043A1 WO 2024235043 A1 WO2024235043 A1 WO 2024235043A1 CN 2024091307 W CN2024091307 W CN 2024091307W WO 2024235043 A1 WO2024235043 A1 WO 2024235043A1
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WO
WIPO (PCT)
Prior art keywords
information
model
inference result
accuracy
request
Prior art date
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Application number
PCT/CN2024/091307
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English (en)
Chinese (zh)
Inventor
金辉
程思涵
崇卫微
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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Filing date
Publication date
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Publication of WO2024235043A1 publication Critical patent/WO2024235043A1/fr
Anticipated expiration legal-status Critical
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to an information acquisition method, device and communication equipment.
  • some network elements can assist other devices in making policy decisions through intelligent data analysis, thereby using artificial intelligence (AI) or machine learning (ML) to improve the intelligence of device policy decisions.
  • AI artificial intelligence
  • ML machine learning
  • some devices involved in intelligent data analysis cannot know the accuracy of the model, resulting in poor results of intelligent data analysis.
  • the embodiments of the present application provide an information acquisition method, apparatus and communication equipment, which can solve the problem of poor intelligent data analysis effect.
  • a method for obtaining information comprising:
  • the first device acquires first information, where the first information is used to determine the accuracy of the first model.
  • a method for obtaining information comprising:
  • the second device sends first information to the first device, where the first information is used to determine the accuracy of the first model.
  • a method for obtaining information comprising:
  • the third device receives the first information or the second information sent by the first device, where the first information or the second information is used to determine the accuracy of the requested first model.
  • a first device includes the information acquisition device, including:
  • the acquisition module is used to acquire first information, where the first information is used to determine the accuracy of the first model.
  • an information acquisition device is provided, and the second device includes the information acquisition device, including:
  • the sending module is used to send first information to the first device, where the first information is used to determine the accuracy of the first model.
  • an information acquisition device includes the information acquisition device, including:
  • the first receiving module is used to receive first information or second information sent by a first device, where the first information or the second information is used to determine the accuracy of the requested first model.
  • a communication device comprising a processor and a memory, the memory storing a program or instruction that can be run on the processor, the program or instruction being executed by the processor to implement the first aspect Or the steps of the method described in the second aspect or the third aspect.
  • a first device comprising a processor and a communication interface, wherein the processor is used to: obtain first information, and the first information is used to determine the accuracy of a first model.
  • a second device comprising a processor and a communication interface, wherein the communication interface is used to: send first information to the first device, the first information being used to determine the accuracy of the first model.
  • a third device comprising a processor and a communication interface, wherein the communication interface is used to: receive first information or second information sent by a first device, wherein the first information or second information is used to determine the accuracy of a requested first model.
  • an information acquisition system comprising: a first device, a second device and a third device, wherein the first device can be used to execute the steps of the method described in the first aspect, the second device can be used to execute the steps of the method described in the second aspect, and the third device can be used to execute the steps of the method described in the third aspect.
  • a readable storage medium on which a program or instruction is stored.
  • the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented, or the steps of the method described in the third aspect are implemented.
  • a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instructions to implement the method described in the first aspect, or the method described in the second aspect, or the steps of the method described in the third aspect.
  • a computer program/program product is provided, wherein the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect, or the steps of the method described in the third aspect.
  • a first device obtains first information, and the first information is used to determine the accuracy of a first model, which can support the first device to more accurately obtain the accuracy of the first model, thereby improving the effect of intelligent data analysis; information used to determine the accuracy of the first model is exchanged among the first, second, and third devices.
  • the information used to determine the accuracy of the first model can be used to support the devices participating in the intelligent data analysis to unify the method of determining the accuracy of the model, thereby further improving the effect of intelligent data analysis.
  • FIG1 is a block diagram of a wireless communication system to which an embodiment of the present application can be applied;
  • FIG2 is a schematic diagram of a data analysis process in the related art
  • FIG3 is a flow chart of a method for obtaining information provided by an embodiment of the present application.
  • FIG4 is a second flowchart of an information acquisition method provided in an embodiment of the present application.
  • FIG5 is a third flowchart of an information acquisition method provided in an embodiment of the present application.
  • FIG6 is a fourth flowchart of an information acquisition method provided in an embodiment of the present application.
  • FIG7 is a schematic diagram of a structure of an information acquisition device provided in an embodiment of the present application.
  • FIG8 is a second structural diagram of an information acquisition device provided in an embodiment of the present application.
  • FIG9 is a third structural diagram of an information acquisition device provided in an embodiment of the present application.
  • FIG10 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application.
  • FIG11 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application.
  • FIG12 is a schematic diagram of a structure of a network side device provided in an embodiment of the present application.
  • FIG. 13 is a second schematic diagram of the structure of a network side device provided in an embodiment of the present application.
  • first, second, etc. of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by “first” and “second” are generally of one type, and the number of objects is not limited, for example, the first object can be one or more.
  • “or” in the present application represents at least one of the connected objects.
  • “A or B” covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B.
  • the character "/" generally indicates that the objects associated with each other are in an "or” relationship.
  • indication in this application can be a direct indication (or explicit indication) or an indirect indication (or implicit indication).
  • a direct indication can be understood as the sender explicitly informing the receiver of specific information, operations to be performed, or request results in the sent indication;
  • an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency Division Multiple Access
  • NR New Radio
  • 6G 6th Generation
  • FIG1 is a block diagram of a wireless communication system applicable to the embodiment of the present application.
  • the wireless communication system includes a terminal 11 and a network side device 12.
  • the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop Laptop Computer, Notebook Computer, Personal Digital Assistant (PDA), PDA, Netbook, Ultra-mobile Personal Computer (UMPC), Mobile Internet Device (MID), Augmented Reality (AR), Virtual Reality (VR) equipment, Robot, Wearable Device, Flight Vehicle, Vehicle User Equipment (VUE), Shipborne Equipment, Pedestrian User Equipment (PUE), Smart Home (home equipment with wireless communication function, such as refrigerator, TV, washing machine or furniture, etc.), Game Console, Personal Computer (PC), ATM or self-service machine and other terminal side equipment.
  • PDA Personal Digital Assistant
  • UMPC Ultra-mobile Personal Computer
  • MID Mobile Internet Device
  • AR Augmented Reality
  • VR Virtual Reality
  • Robot Wearable Device
  • Flight Vehicle Vehicle User Equipment
  • VUE Vehicle User Equipment
  • PUE Shipborne Equipment
  • Smart Home home equipment
  • the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc.
  • a chip in the terminal such as a modem chip, a system-on-chip (System on Chip, SoC). It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application.
  • the network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit.
  • the access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AS) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.
  • WLAN wireless Local Area Network
  • AS Access Point
  • WiFi wireless Fidelity
  • the base station may be referred to as a Node B (NB), an evolved Node B (eNB), a next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a radio base station, a radio transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a Home Node B (HNB), a Home Evolved Node B, a Transmission Reception Point (TRP) or other appropriate terms in the field.
  • NB Node B
  • eNB evolved Node B
  • gNB next generation Node B
  • NR Node B New Radio Node B
  • an access point a Relay Base Station
  • SBS Serving Base Station
  • BTS Base Transceiver Station
  • a radio base station a radio transceiver
  • BSS Basic Service Set
  • ESS Extended Service Set
  • HNB Home No
  • the core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home user server (Home Subscriber Server, HSS), centralized network configuration (CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), binding support function (Binding Support Function, BSF), application function (Application It should be noted that in the embodiment of the present application,
  • the Network Data Analytics Function (NWDAF) can be functionally divided into two network elements, as follows:
  • Model Training Logical Function (MTLF) is used to generate models and perform model training.
  • Analytics Logical Function (AnLF) is used to perform reasoning to generate predictive information or generate statistical information.
  • MTLF and AnLF can be deployed independently as different network element devices, or deployed together in the same network element device, such as NWDAF.
  • NWDAF can provide both AI model training and model reasoning functions.
  • AI model can also be described as ML model or analytics model (Analytics Model).
  • MTLF collects training data from the training data source device, and the MTLF is NWDAF with MTLF function (NWDAF containing MTLF).
  • MTLF trains the first model based on the training data.
  • the AnLF is NWDAF containing AnLF.
  • AnLF requests the model from MTLF according to the request message.
  • MTLF sends the model information and model accuracy to AnLF.
  • AnLF determines the source device of the inference input data, the type information of the input data, the type information of the output data, etc. based on the received request message.
  • AnLF performs reasoning based on the acquired first model and reasoning input data to obtain reasoning result data.
  • AnLF sends a second instruction to MTLF.
  • (115).MTLF may perform corresponding operations according to the second instruction.
  • FIG. 3 is a flow chart of an information acquisition method provided in an embodiment of the present application. As shown in FIG. 3 , the information acquisition method includes the following steps:
  • Step 101 A first device obtains first information, where the first information is used to determine the accuracy of a first model.
  • the first device may be used to perform reasoning to generate prediction information or generate statistical information.
  • the first device may be an AnLF network element device, and the AnLF may be an NWDAF (NWDAF containing AnLF) with AnLF function.
  • NWDAF NWDAF containing AnLF
  • the first model may be an AI model, an ML model, or an analysis model, etc., which have prediction or statistical functions.
  • Accuracy can also be described as accuracy information, machine learning accuracy information (ML accuracy information), machine learning model accuracy information (ML Model accuracy information) or analytics accuracy information (Analytics Accuracy Information). The following is the same and will not be repeated.
  • the first device may receive a first request sent by a second device, where the first request is used to request the accuracy of the first model, and the first request carries the first information, so that the first device can obtain the first information from the second device; or, the first device may determine the first information. For example, the first device may determine the first information according to its own needs.
  • the first request may also be described as a first request message.
  • the accuracy of the first model may also be described as the accuracy corresponding to the first model.
  • the first information may be used to indicate information used to obtain the accuracy of the first model, and the first information may be used to indicate an accuracy deviation, an allowable error deviation, an allowable error percentage, or an allowable value range used to obtain the accuracy of the first model.
  • the first information may include at least one of the following:
  • the accuracy deviation can also be described as an acceptable accuracy deviation.
  • the allowed error value can also be described as an allowed prediction error deviation.
  • the first information may be used to indicate information used to obtain the accuracy of the first model, and may also be described as the first information may be used to help the first device determine or judge what is a correct prediction.
  • determining the accuracy of the first model may include: determining that the inference result of the first model is correct when the difference between the inference result of the first model and the true value corresponding to the inference result satisfies the first information; or determining that the inference result of the first model is wrong when the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the first information.
  • the regression problem can be converted into a classification problem through the first information.
  • the first information includes information for indicating the accuracy difference
  • the difference between the inference result of the first model and the true value corresponding to the inference result is less than or equal to the accuracy difference
  • the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the first information
  • the difference in true value is greater than the precision difference.
  • the first information includes information for indicating the allowable error
  • the difference between the inference result of the first model and the true value corresponding to the inference result satisfies the first information
  • it can be considered that the difference between the inference result of the first model and the true value corresponding to the inference result is less than or equal to the allowable error
  • the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the first information
  • it can be considered that the difference between the inference result of the first model and the true value corresponding to the inference result is greater than the allowable error.
  • the difference between the inference result of the first model and the true value corresponding to the inference result satisfies the first information, it can be considered that the difference between the inference result of the first model and the true value corresponding to the inference result is within the allowed value range; if the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the first information, it can be considered that the difference between the inference result of the first model and the true value corresponding to the inference result is not within the allowed value range.
  • some network elements are introduced to perform intelligent data analysis and generate data analysis results (analytics) (or inference data results) of some tasks.
  • the data analysis results can assist devices inside and outside the network to make policy decisions.
  • the purpose is to use artificial intelligence (AI) or machine learning (ML) methods to improve the intelligence level of device policy decisions.
  • AI artificial intelligence
  • ML machine learning
  • NWDAF can train AI models or machine learning (ML) models based on training data to obtain a model suitable for a certain AI task. Based on the AI model or ML model, the inference input data of a certain AI task is inferred to obtain the inference result data corresponding to a specific AI task.
  • ML machine learning
  • the Policy Control Function performs intelligent policy control and charging (PCC) based on the inference result data, such as formulating intelligent user retention strategies based on the inference result data of user business behavior to improve the user's business experience; or, the Access and Mobility Management Function (AMF) performs intelligent mobility management operations based on the inference result data of a certain AI task, such as intelligently paging users based on the inference result data of the user's mobile trajectory to improve the paging reachability.
  • PCC policy control and charging
  • AMF Access and Mobility Management Function
  • the devices inside and outside the network make correct and optimized policy decisions based on the AI data analysis results, which is based on the correct data analysis results. If the accuracy of the data analysis results is relatively low, it will be provided as error information to the devices inside and outside the network for reference, which will eventually lead to wrong policy decisions or inappropriate operations. Therefore, it is necessary to ensure the accuracy of the data analysis results.
  • the accuracy requirement information is used to indicate the accuracy that the requested model must meet.
  • the accuracy can refer to the accuracy of the correct prediction, or it can refer to the error value of the prediction, such as the Mean Absolute Error (MAE).
  • the accuracy requirement information can be a specific value, or a percentage, or it can include a value/percentage and its corresponding calculation method. For example, the accuracy requirement information: 70%.
  • the accuracy of the correct prediction can be the ratio of the number of accurate predictions of the model to the total number of model predictions, that is, the number of correct model inference results divided by the total number of inferences to get the accuracy.
  • This method can also be called classification, which is suitable for classification methods, such as determining whether the destination has been reached, whether it is raining, etc.
  • the following formula can be used to calculate the accuracy:
  • the number of correct predictions refers to the number of accurate predictions
  • all predictions refers to the total number of predictions.
  • the predicted error value can be the mean square error between the model predicted value and the actual value (i.e., the true value).
  • This method can also be called regression, which is used to calculate the accuracy of the model prediction. For example, in navigation software, the difference between the predicted arrival time and the actual arrival time is calculated. This method can be used to see whether the model prediction is accurate.
  • the predicted error value can be calculated using the following formula:
  • yi -predicted refers to the model prediction value
  • yi -truth refers to the actual value
  • n refers to the total number of predictions
  • i is a positive integer.
  • This embodiment unifies the calculation method of accuracy requirements into a classification method.
  • the regression problem is converted into a classification problem by sending the first information, such as the acceptable accuracy deviation or the allowable prediction error deviation.
  • the first information such as the acceptable accuracy deviation or the allowable prediction error deviation.
  • consumer NF second device
  • AnLF first device
  • consumer NF second device
  • AnLF first device
  • it carries the precision difference for the regression problem
  • AnLF calculates the accuracy, it considers the inference result with an error value less than or equal to the precision difference to be correct, and considers the inference result with an error value greater than the precision difference to be wrong.
  • the first device obtains first information, and the first information is used to determine the accuracy of the first model, which can support the first device to more accurately obtain the accuracy of the first model, thereby improving the effect of intelligent data analysis.
  • the first device acquires the first information, including:
  • the first device receives a first request sent by the second device, where the first request is used to request the accuracy of the first model, and the first request carries the first information.
  • the second device may be a Consumer NF network element device.
  • the first request may also carry at least one of the following:
  • Analytics filter information (Analytics Filter Information);
  • the first request may be a Nnwdaf_AnalyticsSubscription_Subscribe message.
  • the first device receives a first request sent by the second device, where the first request is used to request the accuracy of the first model.
  • the first request carries the first information, so that the first information can be transmitted by the second device when sending the accuracy requirement, thereby facilitating the first device and the second device to unify the method of determining the accuracy of the model.
  • the method further comprises:
  • the first device sends a second request to a third device, where the second request is used to request the first model
  • the second request carries the first information or the second information, the second information is used to determine the accuracy of the requested first model, and the second information is determined based on the first information.
  • the second request may also be described as a second request message.
  • the third device may be used to generate a model and perform model training.
  • the third device may be a MTLF network element device, and the MTLF may be a NWDAF (NWDAF containing MTLF) with MTLF function.
  • NWDAF NWDAF containing MTLF
  • the first device and the third device may be deployed as different devices respectively, or the first device and the third device may be deployed as one device in combination.
  • the first device may send the second request to the third device based on the received first request; or, the first device may directly send the second request to the third device. For example, the first device may directly send the second request to the third device according to its own needs.
  • the second request may carry the first information, or the second request may carry the second information.
  • the second information may be used to indicate information used to obtain the accuracy of the first model requested.
  • the second information may be used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used to obtain the accuracy of the first model requested.
  • the second information may include at least one of the following:
  • the first device may determine the second information based on the first information, and the second information may be the same as or different from the first information.
  • the information types of the first information and the second information may be the same, for example, the first information and the second information may both include information for indicating the accuracy difference; or the information types of the first information and the second information may be different, for example, the first information may include information for indicating the accuracy difference, and the second information may include information for indicating the allowable error.
  • the value of the second information may be the same as or different from the value of the first information.
  • the precision difference value in the second information may be the precision difference value in the first information, or the precision difference value in the second information may be greater than the precision difference value in the first information, or the precision difference value in the second information may be less than the precision difference value in the first information.
  • the information type of the second information may be converted into the information type of the first information, and the value of the converted second information may be the same as or different from the value of the first information.
  • the first information may include information for indicating a precision difference
  • the second information may include information for indicating an allowable error
  • the second information may be converted into a precision difference
  • the converted second information may be greater than, less than, or equal to the first information.
  • the second request may be a Nnwdaf_MLModelProvision_Subscribe message or a Nnwdaf_MLModelInfo_Request message.
  • the second request may also carry at least one of the following:
  • AnLF when AnLF sends the second request to MTLF, it carries the precision difference for the regression problem; when MTLF calculates the accuracy, it considers the inference result with an error value less than or equal to the precision difference to be correct, and considers the inference result with an error value greater than the precision difference to be wrong.
  • MTLF when MTLF sends accuracy information to AnLF, it carries the accuracy difference for obtaining the accuracy.
  • the first device sends a second request to the third device, the second request is used to request the first model; wherein the second request carries the first information or the second information, the second information is used to determine the accuracy of the requested first model, and the second information is determined based on the first information.
  • the first device can transmit the first information or the second information when sending a model request, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • the method further comprises:
  • the first device receives the accuracy of the first model and third information sent by the third device, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • the third information may be the same as or different from the second information.
  • the third information may be used to indicate information used to obtain the accuracy of the first model.
  • the third information may be used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used to obtain the accuracy of the first model.
  • the third information may include at least one of the following:
  • the information types of the second information and the third information may be the same, for example, the second information and the third information may both include information for indicating the accuracy difference; or, the information types of the second information and the third information may be different, for example, the second information may include information for indicating the accuracy difference, and the third information may include information for indicating the allowable error.
  • the value of the third information may be the same as or different from the value of the second information.
  • the precision difference value in the third information may be the precision difference value in the second information, or the precision difference value in the third information may be greater than the precision difference value in the second information, or the precision difference value in the third information may be less than the precision difference value in the second information.
  • the information type of the third information may be converted into the information type of the second information, and the value of the converted third information may be the same as or different from the value of the second information.
  • the second information may include information for indicating a precision difference
  • the third information may include information for indicating an allowable error
  • the third information may be converted into a precision difference
  • the converted third information may be greater than, less than, or equal to the second information.
  • the first device after the first device sends the second request to the third device, the first device receives the accuracy of the first model and the third information sent by the third device.
  • the first model may be sent to the first device, so that the first device may receive the first model, the accuracy of the first model, and third information sent by the third device.
  • the first device receives the accuracy of the first model and the third information sent by the third device, and the third information is used to indicate the information used to obtain the accuracy of the first model.
  • the third device can transmit the third information when sending the accuracy of the model, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • the method further comprises:
  • the first device determines the accuracy of the first model based on at least one of the first information and fourth information, an inference result of the first model, and a true value corresponding to the inference result, wherein the fourth information is determined based on the first information.
  • the first device determines the accuracy of the first model based on an inference result of the first model, a true value corresponding to the inference result, and fourth information.
  • the inference results can also be understood as prediction results.
  • the fourth information may be used to indicate information used to obtain the accuracy of the first model.
  • the fourth information may be used to indicate the accuracy difference, the allowable error value, the allowable error percentage or the allowable value range used to obtain the accuracy of the first model.
  • the fourth information may include at least one of the following:
  • the first device may determine the fourth information based on the first information, and the fourth information may be the same as or different from the first information.
  • the information type of the first information and the fourth information may be the same, for example, the first information and the fourth information may both include information for indicating the accuracy difference; or the information type of the first information and the fourth information may be different, for example, the first information may include information for indicating the accuracy difference, and the fourth information may include information for indicating the allowable error.
  • the value of the fourth information may be the same as or different from the value of the first information.
  • the precision difference value in the fourth information may be the precision difference value in the first information, or the precision difference value in the fourth information may be greater than the precision difference value in the first information, or the precision difference value in the fourth information may be less than the precision difference value in the first information.
  • the information type of the fourth information may be converted into the information type of the first information, and the value of the converted fourth information may be the same as or different from the value of the first information.
  • the first information may include information for indicating a precision difference
  • the fourth information may include information for indicating an allowable error
  • the fourth information may be converted into a precision difference
  • the converted fourth information may be greater than, less than, or equal to the first information.
  • the first device can determine the accuracy of the first model based on at least one of the first information and the fourth information, the inference result of the first model, and the true value corresponding to the inference result.
  • the first device determines the accuracy of the first model according to at least one of the first information and the fourth information, the inference result of the first model, and the truth value corresponding to the inference result, wherein the fourth information is determined based on the first information.
  • the accuracy of the first model can be determined by the determination method of the accuracy of the acquired first model.
  • the first device determines the accuracy of the first model according to at least one of the first information and the fourth information, the inference result of the first model, and a true value corresponding to the inference result, including any one of the following:
  • the inference result of the first model is determined to be correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is determined to be incorrect.
  • the difference between the inference result and the true value corresponding to the inference result can be the difference obtained by directly subtracting the inference result from the true value, or can be the Euler distance between the inference result and the true value, or the Euclidean metric (also known as the Euclidean distance), or the Manhattan distance, or the Minkowski distance, or the adjacent space cosine similarity (cosine similarity), or the adjusted cosine similarity (Pearson correlation coefficient), or the Jaccard similarity coefficient, etc.
  • the first device determines the accuracy of the first model based on the inference result of the first model, the true value corresponding to the inference result, and the first information, including: when the difference between the inference result of the first model and the true value corresponding to the inference result satisfies the first information, determining that the inference result of the first model is correct; or, when the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the first information, determining that the inference result of the first model is incorrect.
  • the first device determines the accuracy of the first model based on the inference result of the first model, the true value corresponding to the inference result, and fourth information, including: when the difference between the inference result of the first model and the true value corresponding to the inference result satisfies the fourth information, determining that the inference result of the first model is correct; or, when the difference between the inference result of the first model and the true value corresponding to the inference result does not satisfy the fourth information, determining that the inference result of the first model is incorrect.
  • the method further comprises at least one of the following:
  • the first device sends at least one of the first information and fourth information and the accuracy of the first model to the second device;
  • the first device sends at least one of the first information and fourth information and the accuracy of the first model to a third device.
  • the first device sends the accuracy of the first model and the first signal to the second device. For example, when the first device determines the accuracy of the first model based on the inference result of the first model, the truth value corresponding to the inference result, and the first information, the first device sends the accuracy of the first model and the first information to the second device.
  • the first device can transmit the first information when sending the accuracy of the model, which facilitates the first device and the second device to unify the method of determining the accuracy of the model.
  • the first device sends the accuracy of the first model and the fourth information to the second device. For example, when the first device determines the accuracy of the first model based on the inference result of the first model, the truth value corresponding to the inference result, and the fourth information, the first device sends the accuracy of the first model and the fourth information to the second device.
  • the first device can transmit the fourth information when sending the accuracy of the model, so that the first device and the second device can unify the method of determining the accuracy of the model.
  • the first device sends the accuracy of the first model and the first information to the third device. For example, when the first device determines the accuracy of the first model based on the inference result of the first model, the truth value corresponding to the inference result, and the first information, the first device sends the accuracy of the first model and the first information to the third device.
  • the first device can transmit the first information when sending the accuracy of the model, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • the first device sends the accuracy of the first model and the fourth information to the third device. For example, when the first device determines the accuracy of the first model based on the inference result of the first model, the true value corresponding to the inference result, and the fourth information, the first device sends the accuracy of the first model and the fourth information to the third device.
  • the first device can transmit the fourth information when sending the accuracy of the model, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • AnLF when AnLF sends accuracy information to consumer NF, it carries the accuracy difference used to obtain the accuracy.
  • AnLF can use 2 minutes to calculate the accuracy and inform the accuracy difference used to obtain the accuracy when sending the accuracy information to consumer NF.
  • the method further comprises:
  • the first device receives the second model and the first information sent by the third device; or,
  • the first device receives the second model and fifth information sent by the third device, where the fifth information is used to indicate information used to determine the accuracy of the second model;
  • the second model is a model obtained by retraining the first model.
  • the third device when the third device uses the first information to determine the accuracy of the second model, the third device sends the second model and the first information to the first device.
  • the first device receives the second model and the first information sent by the third device.
  • the third device can transmit the first information when sending the retrained model, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • the third device when the third device uses the fifth information to determine the accuracy of the second model, the third device sends the second model and the fifth information to the first device.
  • the fifth information can be transmitted by the third device when sending the retrained model, so that the first device and the third device can unify the method of determining the accuracy of the model.
  • the fifth information may be used to indicate information used to determine the accuracy of the second model.
  • the fifth information may be used to indicate the accuracy difference, the allowable error value, the allowable error percentage, or the allowable value range used to determine the accuracy of the second model.
  • the fifth information may include at least one of the following:
  • the first device after the first device sends at least one of the first information and the fourth information and the accuracy of the first model to the third device, the first device receives the second model and the first information sent by the third device; or, the first device receives the second model and the fifth information sent by the third device, and the fifth information is used to indicate the information used to determine the accuracy of the second model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • the allowable error may include an allowable error value or an allowable error percentage.
  • the allowable value may refer to a specific value or a range of values.
  • the first model includes at least one of the following:
  • the first device is an analysis logic function; or,
  • the second device is a consumer network function; or,
  • the third device is a model training logic function.
  • FIG. 4 is a flow chart of an information acquisition method provided in an embodiment of the present application. As shown in FIG. 4 , the information acquisition method includes the following steps:
  • Step 201 The second device sends first information to the first device, where the first information is used to determine the accuracy of the first model.
  • the first information is carried in a first request, and the first request is used to request the accuracy of the first model; or, the first request is used to request the first device to perform inference.
  • the method further comprises:
  • the second device receives at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or
  • the inference result of the first model is correct; or, in the case where the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is wrong.
  • the first information or the fourth information includes at least one of the following:
  • this embodiment is an implementation of the second device corresponding to the embodiment shown in FIG3 . Its specific implementation can refer to the relevant description of the embodiment shown in FIG3 . To avoid repeated description, this embodiment will not be described again.
  • FIG. 5 is a flow chart of an information acquisition method provided in an embodiment of the present application. As shown in FIG. 5 , the information acquisition method includes the following steps:
  • Step 301 A third device receives first information or second information sent by a first device, where the first information or second information is used to determine the accuracy of a requested first model.
  • the first information or the second information is carried in a second request, and the second request is used to request the first model.
  • the method further comprises:
  • the third device sends the accuracy of the first model and third information to the first device, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • the method further comprises:
  • the third device receives at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or,
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is incorrect.
  • the method further comprises:
  • the third device sends the second model and the first information to the first device;
  • the third device sends the second model and fifth information to the first device, where the fifth information is used to indicate information used to determine the accuracy of the second model;
  • the second model is a model obtained by retraining the first model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • this embodiment is an implementation of the third device corresponding to the embodiment shown in Figure 3. Its specific implementation can refer to the relevant description of the embodiment shown in Figure 3. To avoid repeated description, this embodiment will not be repeated.
  • the accuracy of the model can be represented by the ratio of the number of accurate model predictions to the total number of model predictions, or the mean square error between the model prediction value and the true value can be used to represent the accuracy of the model. Due to the inconsistent methods for determining the accuracy of the model, multiple devices participating in the intelligent data analysis cannot accurately know the accuracy of the model, resulting in poor results of the intelligent data analysis.
  • This embodiment can support the first device, the second device, and the third device to accurately obtain the accuracy of the model through the interaction between the first device, the second device, and the third device.
  • the first information, the second information, the third information, the fourth information, or the fifth device can support the devices participating in the intelligent data analysis to unify the method for determining the accuracy of the model, thereby improving the effect of the intelligent data analysis.
  • device A (Consumer NF or AnLF) sends first information corresponding to a first model to device B (AnLF or MTLF), where the first information is used to obtain accuracy information corresponding to the first model; and device A receives the accuracy information sent by device B.
  • the first information includes at least one of the following: indication information indicating an accuracy difference, an allowable error value, an allowable error percentage, or an allowable error range.
  • the accuracy information received by the device A from the device B may include:
  • the second information sent by receiving device B is used to indicate the accuracy difference, the allowable error value, the allowable error percentage or the allowable error range used to obtain the accuracy information.
  • the second information may include at least one of the following: indication information indicating an accuracy difference, an allowable error value, an allowable error percentage, or an allowable error range.
  • device A is Consumer NF and device B is AnLF; or, device A is AnLF and device B is MTLF.
  • device A When device A is Consumer NF and device B is AnLF, device A sends a first message to device B, the first message is used to obtain accuracy information corresponding to the first model, and the first message includes the first information.
  • device A When device A is AnLF and device B is MTLF, device A sends a second message to device B, the second message is used to obtain the first model, and the second message includes the first information.
  • MTLF i.e., the third device collects training data from the training data source device.
  • the MTLF is NWDAF with MTLF function (NWDAF containing MTLF).
  • MTLF trains the first model based on the training data.
  • Consumer NF i.e., the second device
  • AnLF i.e., the first device
  • the AnLF is a NWDAF containing AnLF.
  • the first request is used to request AnLF to analyze or reason about a specific task, and the first request includes:
  • Analytics filter information (Analytics Filter Information);
  • the first information may be used to indicate an accuracy difference, an allowable error value, an allowable error percentage, an allowable error range, etc.
  • the first request may be a Nnwdaf_AnalyticsSubscription_Subscribe message or a Nnwdaf_AnalyticsInfo_Request message.
  • AnLF requests the model (Model) from MTLF through a second request according to the first request;
  • the second request may be a Nnwdaf_MLModelProvision_Subscribe message or a Nnwdaf_MLModelInfo_Request message.
  • the second request includes:
  • the second information is used to indicate the accuracy difference, the allowable error value, the allowable error percentage or the allowable value range, etc.
  • the second information may be the same as or different from the first information.
  • MTLF sends the information of the first model and the accuracy of the first model to AnLF.
  • the message sent may be Nnwdaf_MLModelProvision_Notify or Nnwdaf_MLModelInfo_Request Response message.
  • steps (21) and (22) can also be performed after step (24).
  • the MTLF sends third information corresponding to the accuracy of the first model to the AnLF, where the third information is used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used to obtain the accuracy.
  • the third information may be the same as or different from the second information.
  • AnLF determines the source device of the inference input data, the type information of the input data, the type information of the output data, etc. according to the received first request.
  • AnLF obtains the reasoning input data corresponding to the task. Specifically, AnLF may send a request message for the reasoning input data to the source device of the reasoning input data determined in step (6) to collect the reasoning input data corresponding to the task.
  • AnLF performs reasoning based on the acquired first model and reasoning input data to obtain reasoning result data.
  • UE user equipment
  • the inference result data can inform the consumer NF that the model corresponding to the analytics ID has obtained the statistics through inference.
  • the statistical or predicted values are used to assist the consumer NF in executing corresponding policy decisions.
  • the statistical or predicted values corresponding to UE mobility can be used to assist the AMF in optimizing user paging.
  • AnLF obtains label data corresponding to the inference result data.
  • the message sent by AnLF to request to obtain label data may be a Nnf_EventExposure_Subscribe message.
  • AnLF can send a request message for label data to the source device of the label data determined in step (6), which includes type information of the label data, object information corresponding to the label data, time information (such as timestamp, time period), etc., to determine which label data to feed back to the source device of the label data.
  • the label data can also be described as the ground truth.
  • AnLF When calculating accuracy, AnLF considers the result whose error value conforms to the first information to be a correct result, such as the result whose error value is less than or equal to the first information; and considers the result whose error value does not conform to the first information, such as the result whose error value is greater than the first information to be an error.
  • AnLF needs to send a first accuracy to consumer NF (for example, when it is determined that the first accuracy does not meet the accuracy requirement or the accuracy decreases, or when the periodic sending time arrives), AnLF sends a first indication to consumer NF, and the first indication is used to notify consumer NF that the accuracy of the first model does not meet the accuracy requirement or the accuracy of the first model decreases, or the first indication is used to notify consumer NF of the accuracy of the first model.
  • AnLF also sends fourth information corresponding to the first accuracy to consumer NF, where the fourth information is used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used when obtaining the first accuracy.
  • the fourth information may be the same as or different from the first information.
  • the first accuracy and the fourth information can be carried in the Nnwdaf_AnalyticsSubscription_Notify or Nnwdaf_AnalyticsInfo_Request response message.
  • AnLF needs to send the first accuracy to MTLF (for example, when it is determined that the first accuracy does not meet the accuracy requirement or the accuracy has decreased, or when the periodic sending time arrives), AnLF sends a second indication to MTLF, and the second indication is used to notify MTLF that the accuracy of the first model does not meet the accuracy requirement or the accuracy of the first model has decreased, or the second indication is used to notify the consumer NF of the accuracy of the first model.
  • the second indication may be the same as or different from the first indication.
  • AnLF when AnLF needs to send the first accuracy to MTLF, it can send fourth information corresponding to the first accuracy to MTLF, where the fourth information is used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used when obtaining the first accuracy.
  • the fourth information may be the same as or different from the second information or the third information.
  • MTLF can perform corresponding operations based on the accuracy of the first model, for example, retraining the model.
  • the fourth information may be used for training, that is, the accuracy of the model during training is determined using the fourth information.
  • MTLF sends fifth information corresponding to the accuracy of the second model to AnLF, where the fifth information is used to indicate the accuracy difference, allowable error value, allowable error percentage or allowable value range used when obtaining the accuracy of the second model.
  • the information acquisition method provided in the embodiment of the present application can be executed by an information acquisition device.
  • an information acquisition device executing the information acquisition method is taken as an example to illustrate the information acquisition device provided in the embodiment of the present application.
  • FIG. 7 is a structural diagram of an information acquisition device provided in an embodiment of the present application.
  • the first device includes the information acquisition device.
  • the information acquisition device 400 includes:
  • the acquisition module 401 is used to acquire first information, where the first information is used to determine the accuracy of the first model.
  • the acquisition module is specifically used for:
  • a first request sent by a second device is received, where the first request is used to request the accuracy of the first model, and the first request carries the first information.
  • a first sending module configured to send a second request to a third device, wherein the second request is used to request the first model
  • the second request carries the first information or the second information, the second information is used to determine the accuracy of the requested first model, and the second information is determined based on the first information.
  • the device further comprises:
  • the first receiving module is used to receive the accuracy of the first model and third information sent by the third device, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • a determination module is used to determine the accuracy of the first model based on at least one of the first information and the fourth information, the inference result of the first model and the true value corresponding to the inference result, wherein the fourth information is determined based on the first information.
  • the determination module is specifically used for any of the following:
  • the inference result of the first model is determined to be correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is determined to be incorrect.
  • the device further includes a second sending module, and the second sending module is used for at least one of the following:
  • At least one of the first information and the fourth information and the accuracy of the first model are sent to a third device.
  • the device further includes a second receiving module, configured to:
  • the second model is a model obtained by retraining the first model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • the first model includes at least one of the following:
  • the first device is an analysis logic function; or,
  • the second device is a consumer network function; or,
  • the third device is a model training logic function.
  • the information acquisition device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal, or it can be other devices other than a terminal.
  • the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the information acquisition device provided in the embodiment of the present application can implement each process implemented by the method embodiment of Figure 3 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the information acquisition method provided in the embodiment of the present application can be executed by an information acquisition device.
  • an information acquisition device executing the information acquisition method is taken as an example to illustrate the information acquisition device provided in the embodiment of the present application.
  • FIG. 8 is a structural diagram of an information acquisition device provided in an embodiment of the present application.
  • the second device includes the information acquisition device.
  • the information acquisition device 500 includes:
  • the sending module 501 is used to send first information to the first device, where the first information is used to determine the accuracy of the first model.
  • the first information is carried in a first request, and the first request is used to request the accuracy of the first model; or, the first request is used to request the first device to perform inference.
  • the device further comprises:
  • a receiving module is used to receive at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or
  • the inference result of the first model is correct; or, in the case where the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is wrong.
  • the first information or the fourth information includes at least one of the following:
  • the information acquisition device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal, or it can be other devices other than a terminal.
  • the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the information acquisition device provided in the embodiment of the present application can implement each process implemented by the method embodiment of Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the information acquisition method provided in the embodiment of the present application can be executed by an information acquisition device.
  • an information acquisition device executing the information acquisition method is taken as an example to illustrate the information acquisition device provided in the embodiment of the present application.
  • FIG. 9 is a structural diagram of an information acquisition device provided in an embodiment of the present application.
  • the third device includes the information acquisition device.
  • the information acquisition device 600 includes:
  • the first receiving module 601 is used to receive first information or second information sent by a first device, where the first information or the second information is used to determine the accuracy of the requested first model.
  • the first information or the second information is carried in a second request, and the second request is used to request the first model.
  • the device further comprises:
  • the first sending module is used to send the accuracy of the first model and third information to the first device, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • the device further comprises:
  • the second receiving module is used to receive at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or,
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is incorrect.
  • the device further includes a second sending module, configured to:
  • the second model is a model obtained by retraining the first model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • the information acquisition device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal, or it can be other devices other than a terminal.
  • the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the information acquisition device provided in the embodiment of the present application can implement each process implemented by the method embodiment of Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • an embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702, and the memory 702 stores a program or instruction that can be run on the processor 701.
  • the communication device 700 is a first device
  • the program or instruction is executed by the processor 701 to implement the various steps of the information acquisition method embodiment applied to the first device, and can achieve the same technical effect. To avoid repetition, it is not repeated here.
  • the communication device 700 is a second device
  • the program or instruction is executed by the processor 701 to implement the various steps of the information acquisition method embodiment applied to the second device, and can achieve the same technical effect. To avoid repetition, it is not repeated here.
  • the communication device 700 is a third device
  • the program or instruction is executed by the processor 701 to implement the various steps of the information acquisition method embodiment applied to the third device, and can achieve the same technical effect. To avoid repetition, it is not repeated here.
  • An embodiment of the present application also provides a first device, including a processor and a communication interface, wherein the processor is used to: obtain first information, and the first information is used to determine the accuracy of a first model.
  • An embodiment of the present application also provides a second device, including a processor and a communication interface, wherein the communication interface is used to: send first information to the first device, and the first information is used to determine the accuracy of the first model.
  • An embodiment of the present application also provides a third device, including a processor and a communication interface, wherein the communication interface is used to: receive first information or second information sent by a first device, and the first information or the second information is used to determine the accuracy of the requested first model.
  • the first device, the second device or the third device in the embodiment of the present application may be a terminal.
  • FIG11 is a schematic diagram of the hardware structure of a terminal implementing the embodiment of the present application.
  • the terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of a processor 810.
  • the terminal 800 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 810 through a power management system, so as to implement functions such as charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG11 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.
  • the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode.
  • the display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072.
  • the touch panel 8071 is also called a touch screen.
  • the touch panel 8071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the radio frequency unit 801 after receiving downlink data from the network side device, can transmit the data to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device.
  • the radio frequency unit 801 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 809 can be used to store software programs or instructions and various data.
  • the memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc.
  • the memory 809 may include a volatile memory or a non-volatile memory, or the memory 809 may include both volatile and non-volatile memories.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM).
  • the memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memories.
  • the processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 810.
  • the processor 810 is used for:
  • First information is obtained, where the first information is used to determine the accuracy of the first model.
  • the radio frequency unit 801 is used for:
  • a first request sent by a second device is received, where the first request is used to request the accuracy of the first model, and the first request carries the first information.
  • the radio frequency unit 801 is used to: send a second request to a third device, where the second request is used to request the first model;
  • the second request carries the first information or the second information, the second information is used to determine the accuracy of the requested first model, and the second information is determined based on the first information.
  • the radio frequency unit 801 is used for:
  • the accuracy of the first model and third information sent by the third device are received, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • the processor 810 is used to determine the accuracy of the first model based on at least one of the first information and fourth information, the inference result of the first model, and a true value corresponding to the inference result, wherein the fourth information is determined based on the first information.
  • processor 810 is specifically configured to perform any of the following:
  • the inference result of the first model is determined to be correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is determined to be incorrect.
  • the radio frequency unit 801 is used for at least one of the following:
  • At least one of the first information and the fourth information and the accuracy of the first model are sent to a third device.
  • the radio frequency unit 801 is further used for:
  • the second model is a model obtained by retraining the first model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • the first model includes at least one of the following:
  • the first device is an analysis logic function; or,
  • the second device is a consumer network function; or,
  • the third device is a model training logic function.
  • the radio frequency unit 801 is used to send first information to the first device, where the first information is used to determine the accuracy of the first model.
  • the first information is carried in a first request, and the first request is used to request the accuracy of the first model; or, the first request is used to request the first device to perform inference.
  • the radio frequency unit 801 is further used to: receive at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is incorrect.
  • the first information or the fourth information includes at least one of the following:
  • the radio frequency unit 801 is used to receive first information or second information sent by a first device, where the first information or the second information is used to determine the accuracy of the requested first model.
  • the first information or the second information is carried in a second request, and the second request is used to request the first model.
  • the radio frequency unit 801 is further used to: send the accuracy of the first model and third information to the first device, where the third information is used to indicate information used to obtain the accuracy of the first model.
  • the radio frequency unit 801 is further used to: receive at least one of the first information and fourth information sent by the first device and the accuracy of the first model, wherein the fourth information is determined based on the first information.
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the first information, the inference result of the first model is wrong; or,
  • the inference result of the first model is correct; or, when the difference between the inference result and the true value corresponding to the inference result does not satisfy the fourth information, the inference result of the first model is incorrect.
  • the radio frequency unit 801 is further used for:
  • the second model is a model obtained by retraining the first model.
  • the first information, the second information, the third information, the fourth information or the fifth information includes at least one of the following:
  • the embodiments of the present application can improve the effect of intelligent data analysis.
  • the terminal of the embodiment of the present application also includes: instructions or programs stored in the memory 809 and executable on the processor 810.
  • the processor 810 calls the instructions or programs in the memory 809 to execute the methods executed by the modules shown in Figures 7, 8 or 9, and achieves the same technical effect. To avoid repetition, it will not be repeated here.
  • the embodiment of the present application also provides a network side device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method embodiment shown in Figures 3, 4 and 5.
  • the network side device embodiment corresponds to the above method embodiment, and each implementation process and implementation method of the above method embodiment can be applied to the network side device embodiment, and can achieve the same technical effect.
  • an embodiment of the present application also provides a network side device.
  • the network side device can be a first device, a second device, or a third device.
  • the network side device 900 includes: an antenna 901, a radio frequency device 902, a baseband device 903, a processor 904 and a memory 905.
  • the antenna 901 is connected to the radio frequency device 902.
  • the radio frequency device 902 receives information through the antenna 901 and sends the received information to the baseband device 903 for processing.
  • the baseband device 903 processes the information to be sent and sends it to the radio frequency device 902.
  • the radio frequency device 902 processes the received information and sends it out through the antenna 901.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 903, which includes a baseband processor.
  • the baseband device 903 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 12, one of which is, for example, a baseband processor, which is connected to the memory 905 through a bus interface to call the program in the memory 905 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 906, which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 900 of the embodiment of the present invention also includes: instructions or programs stored in the memory 905 and executable on the processor 904.
  • the processor 904 calls the instructions or programs in the memory 905 to execute the methods executed by the modules shown in Figures 7, 8 or 9, and achieves the same technical effect. To avoid repetition, it will not be described here.
  • the embodiment of the present application further provides a network side device.
  • the network side device 1000 includes: a processor 1001, a network interface 1002, and a memory 1003.
  • the network interface 1002 is, for example, a common public radio interface (CPRI).
  • CPRI common public radio interface
  • the network side device 1000 of the embodiment of the present invention also includes: instructions or programs stored in the memory 1003 and executable on the processor 1001.
  • the processor 1001 calls the instructions or programs in the memory 1003 to execute the methods executed by the modules shown in Figures 7, 8 or 9, and achieves the same technical effect. To avoid repetition, it will not be described here.
  • An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored.
  • a program or instruction is stored.
  • the various processes of the above-mentioned information acquisition method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the processor is the processor in the terminal described in the above embodiment.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.
  • the readable storage medium may be a non-transient readable storage medium.
  • An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned information acquisition method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • the embodiments of the present application further provide a computer program/program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the above-mentioned information acquisition method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides an information acquisition system, including: a first device, a second device and a third device, wherein the first device can be used to execute the steps of the information acquisition method applied to the first device as described above, the second device can be used to execute the steps of the information acquisition method applied to the second device as described above, and the third device can be used to execute the steps of the information acquisition method applied to the third device as described above.
  • the above embodiment method can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.), and includes a number of instructions for enabling a terminal or a network-side device to execute the methods described in the various embodiments of the present application.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
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Abstract

La présente demande appartient au domaine technique des communications. Sont divulgués un procédé et un appareil de transmission d'informations, et un dispositif de communication. Le procédé d'acquisition d'informations dans les modes de réalisation de la présente demande comprend les étapes suivantes : un premier dispositif acquiert des premières informations, les premières informations étant utilisées pour déterminer la précision d'un premier modèle.
PCT/CN2024/091307 2023-05-12 2024-05-07 Procédé et appareil d'acquisition d'informations, et dispositif de communication Pending WO2024235043A1 (fr)

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CN202310536698.8 2023-05-12

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023016653A1 (fr) * 2021-08-13 2023-02-16 Nokia Solutions And Networks Oy Procédé, appareil et programme d'ordinateur

Patent Citations (1)

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Publication number Priority date Publication date Assignee Title
WO2023016653A1 (fr) * 2021-08-13 2023-02-16 Nokia Solutions And Networks Oy Procédé, appareil et programme d'ordinateur

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