CN120034818A

CN120034818A - Method, terminal and network side equipment for obtaining positioning performance

Info

Publication number: CN120034818A
Application number: CN202311567606.9A
Authority: CN
Inventors: 崇卫微
Original assignee: Vivo Mobile Communication Co Ltd
Current assignee: Vivo Mobile Communication Co Ltd
Priority date: 2023-11-22
Filing date: 2023-11-22
Publication date: 2025-05-23

Abstract

The present application discloses a method for obtaining positioning performance, a terminal and a network-side device, and belongs to the field of wireless communication technology. The method for obtaining positioning performance of an embodiment of the present application includes: a target network-side device obtains target positioning data corresponding to at least one first terminal, wherein the target positioning data includes at least one of the following: AI positioning information and tag positioning information, wherein the AI positioning information is positioning information generated by reasoning based on an AI positioning model; positioning information deviation, used to indicate the deviation between the AI positioning information and the tag positioning information; the target network-side device obtains AI positioning performance information based on the target positioning data.

Description

Acquisition method of positioning performance, terminal and network side equipment

Technical Field

The application belongs to the technical field of wireless communication, and particularly relates to a positioning performance acquisition method, a terminal and network side equipment.

Background

The artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) location model (or machine learning (MACHINE LEARNING, ML) location model) can be used to locate directly or with assistance by reasoning, thus saving significant amounts of measurement resources and reducing computing power consumption.

In the related art, an AI-positioning model may be set in a terminal, and used for performing model reasoning to obtain AI-positioning information, where the AI-positioning information may be positioning result data, or the AI-positioning information may be positioning intermediate data, and the positioning intermediate data is reported to a positioning control function network element, for example, a location management function (Location Management Function, LMF), and the positioning control function network element may perform location estimation based on the received positioning intermediate data to obtain location information of the terminal. In addition, the AI positioning model can also be arranged in the LMF for model reasoning so as to obtain AI positioning information.

However, in practical applications, the performance of the AI-positioning model is unknown, so that the accuracy and precision of AI-positioning information obtained by reasoning using the AI-positioning model cannot be determined, and whether the acquired position information is accurate cannot be obtained.

Disclosure of Invention

The embodiment of the application provides a positioning performance acquisition method, a terminal and network side equipment, which can solve the problem that the accuracy and precision of AI positioning information obtained by using AI positioning model reasoning cannot be determined due to unknown performance of an AI positioning model.

The first aspect provides a method for acquiring positioning performance, which comprises the steps that target network side equipment acquires target positioning data corresponding to at least one first terminal, wherein the target positioning data comprise at least one of AI positioning information and tag positioning information, the AI positioning information is positioning information generated by reasoning based on an AI positioning model, positioning information deviation is used for indicating deviation between the AI positioning information and the tag positioning information, and the target network side equipment acquires the AI positioning performance information based on the target positioning data.

The first terminal receives a first request message sent by target network side equipment, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated based on an AI positioning model, label positioning information request indication used for indicating to report label positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the label positioning information, positioning performance monitoring indication used for indicating to-be-monitored AI positioning performance information of the target network side equipment, type information of positioning information used for indicating that the AI positioning information or specific type of the label positioning information required to be reported is positioning result data or positioning intermediate data, and the first terminal sends a first response message to the target network side equipment, wherein the first response message comprises at least one of AI positioning information, label positioning information and positioning information deviation between the AI positioning information and the label positioning information, wherein the AI positioning information is AI positioning information generated by the first terminal based on the AI positioning model.

In a third aspect, a method for acquiring positioning performance is provided, where the method includes that a target device acquires AI positioning performance information or first information corresponding to an AI positioning model sent by a target network side device, where the first information is used to indicate that performance of an AI-based positioning method is reduced or insufficient.

The fourth aspect provides a positioning performance acquisition device, which comprises a first acquisition module and a second acquisition module, wherein the first acquisition module is used for acquiring target positioning data corresponding to at least one first terminal, the target positioning data comprise at least one of artificial intelligence AI positioning information and tag positioning information, the AI positioning information is positioning information generated by reasoning based on an AI positioning model, positioning information deviation is used for indicating deviation between the AI positioning information and the tag positioning information, and the second acquisition module is used for acquiring AI positioning performance information based on the target positioning data.

In a fifth aspect, a reporting device of positioning data is provided, which includes a first receiving module, configured to receive a first request message sent by a target network side device, where the first request message includes at least one of AI positioning information request indication, configured to instruct reporting of positioning information generated based on an AI positioning model, tag positioning information request indication, configured to instruct reporting of tag positioning information, positioning deviation request indication, configured to instruct reporting of positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication, configured to instruct the target network side device to monitor AI positioning performance information, type information of positioning information, configured to instruct that the AI positioning information or a specific type of the tag positioning information to be reported is positioning result data or positioning intermediate data, and a sending module, configured to send a first response message to the target network side device, where the first response message includes at least one of AI positioning information, tag positioning information, positioning information deviation between the AI positioning information and the tag positioning information, and positioning information deviation between the tag positioning information, where the AI positioning information is generated by the first terminal based on the AI positioning model.

In a sixth aspect, an acquisition device of positioning performance is provided, which includes a second receiving module, configured to receive AI positioning performance information or first information corresponding to an AI positioning model sent by a target network side device, where the first information is used to indicate performance degradation or insufficiency of an AI-based positioning method, and a third acquisition module is configured to acquire the AI positioning performance information or the first information.

In a seventh aspect, there is provided a terminal comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, performs the steps of the method as described in the second aspect, or performs the steps of the method as described in the third aspect.

In an eighth aspect, a terminal is provided, comprising a processor for implementing the steps of the method according to the second aspect or for implementing the steps of the method according to the third aspect, and a communication interface for coupling with the processor.

In a ninth aspect, a network side device is provided, comprising a processor and a memory storing a program or instructions executable on the processor, the program or instructions implementing the steps of the method according to the first aspect or the steps of the method according to the third aspect when executed by the processor.

In a tenth aspect, a network side device is provided, which comprises a processor for implementing the steps of the method according to the first aspect or implementing the steps of the method according to the third aspect, and a communication interface for coupling with the processor.

In an eleventh aspect, there is provided a readable storage medium having stored thereon a program or instructions which when executed by a processor, performs the steps of the method according to the first aspect, or performs the steps of the method according to the second aspect, or performs the steps of the method according to the third aspect.

A twelfth aspect provides a communication system comprising a terminal operable to perform the steps of the method as described in the second aspect, a network side device operable to perform the steps of the method as described in the first aspect, and a target device operable to perform the steps of the method as described in the third aspect.

In a thirteenth aspect, there is provided a chip comprising a processor and a communication interface, the communication interface and the processor being coupled, the processor being adapted to run a program or instructions, to carry out the steps of the method according to the first aspect, or to carry out the steps of the method according to the second aspect, or to carry out the steps of the method according to the third aspect.

In a fourteenth aspect, there is provided a computer program/program product stored in a storage medium, the program/program product being executable by at least one processor to perform the steps of the method as described in the first aspect, or to perform the steps of the method as described in the second aspect, or to perform the steps of the method as described in the third aspect.

In the embodiment of the application, target network side equipment acquires target positioning data corresponding to at least one first terminal, wherein the target positioning data comprises at least one of AI positioning information and tag positioning information, wherein the AI positioning information is positioning information generated by reasoning based on an AI positioning model, positioning information deviation is used for indicating deviation between the AI positioning information and the tag positioning information, and the target network side equipment acquires AI positioning performance information based on the target positioning data. Therefore, the target network side equipment can acquire the AI positioning performance information based on the target positioning data reported by at least one first terminal, and the problem that the accuracy and precision of the AI positioning information obtained by using the AI positioning model reasoning can not be determined due to the unknown performance of the AI positioning model is solved.

Drawings

Fig. 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable;

FIG. 2a is a schematic diagram of a layout scenario of an AI positioning model in accordance with an embodiment of the application;

FIG. 2b is a schematic diagram of another layout scenario of an AI positioning model in an embodiment of the application;

FIG. 2c illustrates yet another layout scenario of an AI positioning model in accordance with an embodiment of the application;

FIG. 2d illustrates yet another layout scenario of an AI positioning model in accordance with an embodiment of the application;

fig. 3 is a schematic flow chart of a method for obtaining positioning performance according to an embodiment of the present application;

fig. 4 is a schematic flow chart of a method for reporting positioning data according to an embodiment of the present application;

Fig. 5 is a schematic flow chart of a method for obtaining positioning performance according to an embodiment of the present application;

fig. 6 is a schematic flow chart of another method for obtaining positioning performance according to an embodiment of the present application;

Fig. 7 is a schematic flow chart of a method for obtaining positioning performance according to an embodiment of the present application;

fig. 8 is a schematic flow chart of a method for obtaining positioning performance according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a positioning performance obtaining device according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of a reporting device for positioning data according to an embodiment of the present application;

FIG. 11 is a schematic structural diagram of a positioning performance obtaining device according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of a communication device according to an embodiment of the present application;

fig. 13 shows a schematic hardware structure of a terminal according to an embodiment of the present application;

Fig. 14 is a schematic hardware structure diagram of a network side device according to an embodiment of the present application;

Fig. 15 is a schematic hardware structure of a network side device according to an embodiment of the present application.

Detailed Description

The technical solutions of the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the application, fall within the scope of protection of the application.

The terms "first," "second," and the like, herein, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the "first" and "second" distinguishing between objects generally are not limited in number to the extent that the first object may, for example, be one or more. Furthermore, the "or" in the present application means at least one of the connected objects. For example, "A or B" encompasses three schemes, namely scheme one including A and excluding B, scheme two including B and excluding A, scheme three including both A and B. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The term "indication" according to the application may be either a direct indication (or an explicit indication) or an indirect indication (or an implicit indication). The direct indication may be understood that the sender explicitly informs the specific information of the receiver, the operation to be executed, the request result, and the like in the sent indication, and the indirect indication may be understood that the receiver determines the corresponding information according to the indication sent by the sender, or determines the operation to be executed, the request result, and the like according to the determination result.

It should be noted that the techniques described in the embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single-carrier frequency division multiple access (Single-carrier Frequency-Division Multiple Access, SC-FDMA), or other systems. The terms "system" and "network" in embodiments of the application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes a New Radio (NR) system for exemplary purposes and NR terminology is used in much of the following description, but the techniques may also be applied to systems other than NR systems, such as the 6 th Generation (6G) communication system.

Fig. 1 shows a block diagram of a wireless communication system to which an embodiment of the present application is applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may be a Mobile phone, a tablet Computer (Tablet Personal Computer), a Laptop (Laptop Computer), a notebook (Personal DIGITAL ASSISTANT, PDA), a palm Computer, a netbook, an Ultra-Mobile Personal Computer (Ultra-Mobile Personal Computer, UMPC), a Mobile internet device (Mobile INTERNET DEVICE, MID), a Personal Digital Assistant (PDA), Augmented Reality (Augmented Reality, AR), virtual Reality (VR) devices, robots, wearable devices (Wearable Device), aircraft (FLIGHT VEHICLE), in-vehicle devices (Vehicle User Equipment, VUE), on-board equipment, pedestrian terminals (PEDESTRIAN USER EQUIPMENT, PUE), smart home (home appliances having wireless communication function, such as refrigerator, television, Washing machine or furniture, etc.), game machine, personal computer (Personal Computer, PC), teller machine or self-service machine, etc. The wearable device comprises an intelligent watch, an intelligent bracelet, an intelligent earphone, intelligent glasses, intelligent jewelry (intelligent bracelets, intelligent rings, intelligent necklaces, intelligent anklets, intelligent footchains and the like), an intelligent wristband, intelligent clothing and the like. The in-vehicle apparatus may also be referred to as an in-vehicle terminal, an in-vehicle controller, an in-vehicle module, an in-vehicle component, an in-vehicle chip, an in-vehicle unit, or the like. 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 core network device, where the access network device may also be referred to as a radio access network (Radio Access Network, RAN) device, a radio access network function, or a radio access network element. The Access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) Access Point (AS), or a wireless fidelity (WIRELESS FIDELITY, WIFI) node, etc. wherein the base station may be referred to as Node B (NB), evolved Node B (eNB), next generation Node B (the next generation Node B, gNB), new air interface Node B (NR Node B), access point, relay station (Relay Base Station, RBS), serving base station (Serving Base Station, SBS), base transceiver station (Base Transceiver Station, BTS), A radio base station, a radio transceiver, a Basic service set (Basic SERVICE SET, BSS), an Extended service set (Extended SERVICE SET, ESS), a Home Node B (HNB), a home evolved Node B (home evolved Node B), a transmission and reception point (Transmission Reception Point, TRP), or some other suitable terminology in the field, so long as the same technical effect is achieved, the base station is not limited to a specific technical vocabulary, and it should be noted that, in the embodiment of the present application, only a base station in an NR system is described by way of example, and the specific type of the base station is not limited.

The core Network device may include, but is not limited to, at least one of a core Network node, a core Network Function, a Mobility management entity (Mobility MANAGEMENT ENTITY, MME), an access Mobility management Function (ACCESS AND Mobility Management Function, AMF), a session management Function (Session Management Function, SMF), a user plane Function (User Plane Function, UPF), a Policy control Function (Policy Control Function, PCF), a Policy AND CHARGING Rules Function (PCRF), an edge application service discovery Function (Edge Application Server Discovery Function, EASDF), a Unified data management (Unified DATA MANAGEMENT, UDM), a Unified data repository (Unified Data Repository, UDR), a home subscriber server (Home Subscriber Server, HSS), a centralized Network configuration (Centralized Network configuration, CNC), a Network storage Function (Network Repository Function, NRF), a Network opening Function (Network Exposure Function, NEF), a Local NEF (or L-NEF), a binding support Function (Binding Support Function, BSF), an application Function (Application Function, AF), a location management Function (Location Management Function, LMF), a gateway's mobile location center (Gateway Mobile Location Centre, GMLC), a Network data analysis Function (NWDAF), and the like. It should be noted that, in the embodiment of the present application, only the core network device in the NR system is described as an example, and the specific type of the core network device is not limited.

In the related art, as shown in fig. 2a and 2b, an AI Positioning model is set at a terminal, a UE may measure a Positioning reference signal (Positioning REFERENCE SIGNAL, PRS) transmitted by a base station, and then, based on measurement information, perform reasoning through the AI Positioning model to obtain AI Positioning information, where the AI Positioning information may be Positioning result data as shown in fig. 2a, or the AI Positioning information may be Positioning intermediate data (for example, measurement information based on PRS) as shown in fig. 2b, and the terminal feeds back the AI Positioning information obtained by reasoning to an LMF, where the LMF may perform position calculation based on the Positioning intermediate data in the case that the AI Positioning information is the Positioning intermediate data. As shown in fig. 2c and fig. 2d, the AI positioning model is set in the LMF, as shown in fig. 2c, the UE reports measurement information based on PRS to the LMF, the LMF inputs the measurement information reported by the UE to the AI positioning model to obtain an AI positioning result, or as shown in fig. 2d, the base station reports measurement information based on SRS to the LMF, and the LMF inputs the measurement information reported by the base station to the AI positioning model to obtain an AI positioning result.

However, the accuracy of the model of the AI positioning model may not be high in actual use, the positioning accuracy may be poor, and the performance of the AI positioning model is unknown, so that the accuracy and precision of AI positioning information obtained by reasoning using the AI positioning model cannot be determined, and whether the obtained positioning result is accurate cannot be obtained.

Aiming at the problem, the embodiment of the application provides a positioning performance acquisition method, a terminal and network side equipment, which can monitor information such as positioning precision, model accuracy and the like of an AI positioning model to acquire AI positioning performance information of the AI positioning model.

The technical scheme provided by the embodiment of the application is described in detail through some embodiments and application scenes thereof by combining the attached drawings.

Fig. 3 shows a schematic flow chart of a transmission method in an embodiment of the present application, and the method 30 may be performed by a target network side device. In other words, the method may be performed by software or hardware installed on the target network-side device. In an embodiment of the present application, the target network side device may include, but is not limited to, a positioning control function network element, for example, a location management function (Location Management Function, LMF), AMF, GMLC, NWDAF, and the like.

As shown in fig. 3, the method may include the following steps.

S310, the target network side equipment acquires target positioning data corresponding to at least one first terminal.

In the embodiment of the application, the target positioning data comprises at least one of (1) AI positioning information and tag positioning information, wherein the AI positioning information is positioning information generated by reasoning based on an AI positioning model, and (2) positioning information deviation is used for indicating deviation between the AI positioning information and the tag positioning information.

In an embodiment of the present application, optionally, the tag positioning information may include positioning information of the at least one first terminal acquired by a non-AI positioning method. For example, the tag location information may be the actual location information of the first terminal. Among other non-AI positioning methods, there are no limitations to existing legacy positioning methods, such as those described in 3GPP protocol 23.273.

In a first implementation manner, in a case that the target positioning data includes the AI positioning information and the tag positioning information, the target network side device may acquire the AI positioning information and the tag positioning information from the at least one first terminal, where the AI positioning information is positioning information generated by the at least one first terminal by reasoning based on the AI positioning model, and the tag positioning information is positioning information acquired by the at least one first terminal based on a non-AI positioning method.

For example, the target network side device may send a first request message to the at least one first terminal, where the first request message includes at least one information of an AI location information request indication for indicating to report location information generated by reasoning based on the AI location model, a tag location information request indication for indicating to report tag location information, a location performance monitoring indication for indicating that the target network side device is to monitor AI location performance information, location information type information for indicating that the AI location information or a specific type of the tag location information to be reported is location result data or location intermediate data, and after receiving the first request message, the at least one first terminal may send a first response message to the target network side device, where the target network side device receives a first response message from the at least one first terminal, where the first response message includes the AI location information and the tag location information.

Wherein, when the type information of the positioning information is carried in the first request message, the first response message includes the AI positioning information and the tag positioning information as positioning information of a type corresponding to the type information of the positioning information, for example, when the type indicated by the type information of the positioning information is positioning result data, the AI positioning information carried in the first response message is AI positioning result data (for example, position data of the first terminal inferred by an AI positioning model), the tag positioning information is real positioning result data (for example, real position data of the first terminal), and when the type indicated by the type information of the positioning information is positioning intermediate data (for example, PRS measurement data of the first terminal inferred by an AI positioning model), the AI positioning information carried in the first response message is AI positioning intermediate data (for example, PRS measurement data of the first terminal) and the tag positioning information is real positioning intermediate data (for example, PRS measurement data measured by the first terminal).

Optionally, the first response message may further include at least one of the following information:

(1) The first indication information is used for indicating that the AI positioning information is positioning information obtained based on the AI positioning model reasoning; the target network side device can acquire the AI-positioning information included in the first response message through the first indication information.

(2) The second indication information is used for indicating that the tag positioning information is real positioning information or positioning information acquired based on a non-AI positioning method; and the target network side equipment can acquire the tag positioning information included in the first response through the second indication information.

(3) Model identification information of the AI positioning model;

(4) Function identification information corresponding to the AI positioning model;

(5) Characteristic group identification information corresponding to the AI positioning model;

through any one of the above (3) - (5), the target network side device may learn an AI positioning model corresponding to the AI positioning information carried in the first response message.

(6) Identification information of the first terminal;

(7) -a location service (LoCation Services, LCS) association identity corresponding to the first terminal;

Through any one of the above (6) and (7), the target network side device can learn the terminal corresponding to the AI location information and the identification location information.

(8) And the time information is used for indicating the acquisition time corresponding to the AI positioning information or the tag positioning information.

And the target network side equipment can acquire the AI positioning information or the acquisition time corresponding to the tag positioning information through the time information.

In a second implementation manner, in a case that the target positioning data includes the AI positioning information and the tag positioning information, the target network side device may acquire the AI positioning information from the at least one first terminal, and the target network side device may acquire the tag positioning information from an access network device, where the AI positioning information is positioning information generated by the at least one first terminal by reasoning based on the AI positioning model, and the tag positioning information is positioning information for the at least one first terminal acquired by the access network device based on a non-AI positioning method.

For example, the target network side device may send a first request message to at least one first terminal, which may be different from the first implementation manner in that the first request message does not carry the tag positioning information request indication, and the target network side device receives a first response message sent by at least one first terminal, which may be different from the first implementation manner in that the first response message does not carry the tag positioning information and the second indication information. In addition, the target network side device may send a second request message to the access network device, where the second request message includes identification information of the at least one first terminal or LCS associated identification information, and after receiving the second request message, the access network device may send a second response message to the target network side device, where the target network side device receives the second response message from the access network device, and the second response message includes tag positioning information of the at least one first terminal. And the target network side equipment generates the positioning information deviation based on the acquired AI positioning information and the tag positioning information.

Optionally, the second response message may further carry time information, which is used to indicate the acquisition time corresponding to the tag positioning information. The target network side device may associate AI-location information and tag-location information of the same first terminal in the same time period based on the time information in the first response message and the time information in the second response message.

In the above implementation, optionally, the tag positioning information may include at least one of positioning result data or positioning intermediate data.

In the above implementation, optionally, the positioning intermediate data includes at least one of PRS measurement data and positioning reference signal (Sounding REFERENCE SIGNAL, SRS) measurement data.

In the above implementation manner, when the tag positioning information includes the positioning intermediate data and the AI positioning information acquired by the target network side device from the first terminal is AI positioning result data, the target network side device may perform position estimation based on the positioning intermediate data to obtain real position information of the at least one terminal, so as to generate the positioning information deviation.

In a third implementation manner, in a case where the target positioning data includes the positioning information deviation, the target network side device may acquire the positioning information deviation from the at least one first terminal.

For example, the target network side device may send a first request message to at least one first terminal, where, unlike the first implementation manner, the first request message further carries a positioning deviation request indication, which is used to indicate reporting of a positioning deviation between the AI positioning information and the tag positioning information, and may not carry the AI positioning information request indication, the tag positioning information request indication, and type information of the positioning information, and the first response message received from the first terminal carries a positioning information deviation between the AI positioning information and the tag positioning information, and may not carry the AI positioning information and the tag positioning information.

S312, the target network side equipment acquires AI positioning performance information based on the target positioning data.

In one implementation manner, when the target positioning data includes the AI positioning information and the tag positioning information, the target network side device may calculate the AI positioning performance information by using the AI positioning information and the tag positioning information corresponding to each first terminal in association time as input information, for example, the target network side device may compare the AI positioning information and the tag positioning information of the same first terminal, calculate the AI positioning information and the tag positioning information of each first terminal based on a preset built-in algorithm (for example, variance, mean Square Error (MSE), mean Absolute Error (MAE), accuracy (accuracy), and the like), and obtain the AI positioning performance information according to a calculation result.

In one implementation manner, when the target positioning data includes a positioning information deviation value, the target network side device calculates the AI positioning performance information by using the positioning information deviation value corresponding to each first terminal as input information. For example, the target network side device may calculate the positioning information deviation value corresponding to each first terminal based on a preset built-in algorithm (for example, a Mean Square Error (MSE) algorithm, a Mean Absolute Error (MAE), accuracy (correctness), precision (precision), recall (Recall)), and the like, and obtain the AI positioning performance according to a calculation result.

In one implementation mode, the method can further comprise the step that the target network side device sends the AI positioning performance information to target devices, wherein the target devices comprise at least one of provider devices, first terminals, second terminals and positioning control function network elements of the AI positioning model. In this implementation manner, the target network side device may send the AI-positioning performance to at least one of the provider device, the first terminal, the second terminal, and the positioning control function network element of the AI-positioning model, so that at least one of the provider device, the first terminal, the second terminal, and the positioning control function network element of the AI-positioning model may learn the AI-positioning performance information of the AI-positioning model.

In the above implementation manner, optionally, the method may further include that the target network side device receives a request message that the target device requests or subscribes to the AI-location capability information, where the request message includes at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

characteristic group identification information corresponding to the AI positioning model;

and the first performance threshold can be used for indicating the target network side equipment to report the AI positioning performance information to the target equipment when the AI positioning performance information reaches the first performance threshold.

A type of positioning performance metric (metric), a category for indicating the desired monitored positioning performance, such as correctness (correctness), precision (precision), recall (Recall), mean Square Error (MSE), mean Absolute Error (MAE), root mean square error (Root Mean Squared Error, RMSE), etc.;

terminal identification information for indicating a first terminal using the AI-positioning model;

In S310, the target network side device may acquire target positioning data of at least one first terminal corresponding to the region information.

And the time period information is used for indicating the time period for monitoring the positioning performance.

In the above implementation manner, when the target network side device receives the request message that the target device requests the AI-positioning performance information, the target network side device may send the AI-positioning performance information to the target device once, for example, when the request message includes the first performance threshold, and when the positioning performance value indicated by the obtained AI-positioning performance information reaches the first performance threshold, the AI-positioning performance information is sent to the target device, and then the AI-positioning performance information may not be sent to the target device any more. Or the target network side device may also send the AI-positioning performance information to the target device multiple times, for example, in the case that the request message includes the first performance threshold and the time period information, the target network side device may continuously monitor the AI-positioning performance information of the AI-positioning module in the time period indicated by the time period information, and send the AI-positioning performance information to the target device each time the positioning performance value indicated by the AI-positioning performance information reaches the first performance threshold.

In the above implementation manner, when the target network side device receives the request message that the target device subscribes to the AI-location capability information, the target network side device may send the AI-location capability information to the target device multiple times, for example, the target network side device may periodically obtain the AI-location capability information, and when the obtained AI-location capability information changes relative to the AI-location capability information obtained last time, the target network side device sends the current AI-location capability information to the target device. Or the target network side equipment can continuously monitor the AI positioning performance information of the AI positioning module in the time period indicated by the time period information, and each time the positioning performance value indicated by the AI positioning performance information reaches a first performance threshold value, the AI positioning performance information is sent to the target equipment.

In one or more implementations of the foregoing disclosure, the AI-positioning performance information acquired by the target network device may include at least one of:

A metric type indicating the acquired AI positioning performance information, such as correctness (correctness), precision (precision), recall (Recall), mean Square Error (MSE), mean Absolute Error (MAE), or root mean square error (Root Mean Squared Error, RMSE);

and the specific value is used for indicating the acquired AI positioning performance information.

In one implementation, after S212, the method may further include, if the positioning performance value indicated by the AI positioning performance information is lower than a second performance threshold, sending, by the target network side device, first information to a target device, where the first information is used to indicate that performance of the AI-based positioning method is reduced or insufficient, where the target device includes at least one of a provider device, a first terminal, a second terminal, and a positioning control function network element of the AI positioning model. Alternatively, the second performance threshold may be the same as the first performance threshold described above, or may be different, e.g., the second performance threshold may be greater than the first performance threshold. In this implementation manner, the target network side device may send the first information to the target device when the positioning performance value indicated by the acquired AI positioning performance information is lower than the second performance threshold, so that the target device may learn that the AI-based positioning method is degraded or insufficient, and may further adjust or switch the positioning method to the AI positioning model, so as to improve the positioning accuracy.

According to the technical scheme provided by the embodiment of the application, the target network side equipment acquires target positioning data corresponding to at least one first terminal, wherein the target positioning data comprises at least one of AI positioning information and tag positioning information, the AI positioning information is positioning information generated by reasoning based on an AI positioning model, positioning information deviation is used for indicating deviation between the AI positioning information and the tag positioning information, and the target network side equipment acquires AI positioning performance information based on the target positioning data. Therefore, the target network side equipment can acquire the AI positioning performance information based on the target positioning data reported by at least one first terminal, and the problem that the accuracy and precision of the AI positioning information obtained by using the AI positioning model reasoning can not be determined due to the unknown performance of the AI positioning model is solved.

Based on the same technical conception, the embodiment of the application also provides a method for reporting the positioning data.

Fig. 4 is a schematic flow chart of a method for reporting positioning data according to an embodiment of the present application, where the method 400 may be performed by the first terminal, and as shown in fig. 4, the method mainly includes the following steps.

S410, the first terminal receives a first request message sent by the target network side device.

Wherein the first request message includes at least one of the following information:

(1) The AI positioning information request indication is used for indicating and reporting positioning information generated by reasoning based on an AI positioning model;

(2) The label positioning information request indication is used for indicating and reporting label positioning information;

(3) The positioning deviation request indication is used for indicating and reporting the positioning deviation between the AI positioning information and the tag positioning information;

(4) The first terminal can acquire that the target network side equipment needs to monitor the positioning performance of the AI positioning module, so that at least one of the AI positioning information, the tag positioning information and the positioning information deviation between the AI positioning information and the tag positioning information needs to be reported can be determined.

(5) The type information of the positioning information is used for indicating that the specific type of the AI positioning information or the tag positioning information to be reported is positioning result data or positioning intermediate data. Through the type information, the first terminal can know which type of AI positioning information or tag positioning information needs to be reported.

The first request message is the same as the first request message in the method 300, and specific reference may be made to the description related to the method 300.

In one implementation, the method may further comprise, prior to performing the subsequent S412, the step of at least one of:

Step 1, the first terminal performs reasoning to acquire the AI positioning information based on the AI positioning model;

step 2, the first terminal obtains the tag positioning information based on a non-AI positioning method;

and step 3, the first terminal acquires the deviation of the positioning information according to the AI positioning information and the tag positioning information.

In one implementation, the first terminal performs reasoning to obtain the AI-positioning information based on the AI-positioning model, including one of:

Under the condition that the first request message comprises the AI positioning information request indication, the first terminal performs reasoning to acquire the AI positioning information based on the AI positioning model;

under the condition that the first request message comprises the positioning deviation request indication, the first terminal performs reasoning to acquire the AI positioning information based on the AI positioning model;

And under the condition that the first request message comprises the positioning performance monitoring instruction, the first terminal performs reasoning to acquire the AI positioning information based on the AI positioning model.

For example, in the case that the first request message carries an AI-positioning information request instruction, the first terminal may generate AI-positioning information by reasoning based on the AI-positioning model. Under the condition that the first request message carries the label positioning information request indication, the first terminal can execute positioning measurement to acquire the label positioning information. Under the condition that the first request message carries a positioning deviation request indication, the first terminal can generate AI positioning information based on the AI positioning model in an inference mode, perform positioning measurement, acquire tag positioning information, and then compare the AI positioning information based on the AI positioning model in an inference mode to generate AI positioning information and the tag positioning information acquired through the positioning measurement, so as to generate positioning deviation information between the AI positioning information and the tag positioning information.

In one or more implementations described above, the method further includes the first terminal obtaining the AI-positioning model before the first terminal performs reasoning based on the AI-positioning model to obtain the AI-positioning information. For example, the first terminal may acquire the AI positioning model by training itself, or the first terminal acquires the AI positioning model from a Network data analysis Function (Network DATA ANALYTICS Function, NWDAF), or the first terminal acquires the AI positioning model by training from a positioning control Function Network element, or the first terminal acquires the AI positioning model from an OTT server.

And S412, the first terminal sends a first response message to the target network side equipment.

Wherein the first response message includes at least one of the following information:

(1) And the AI positioning information is positioning information generated by the first terminal based on the AI positioning model in an inference way.

(2) And the label positioning information comprises the positioning information of the first terminal acquired by a non-AI positioning method.

(3) Positioning information deviation between the AI positioning information and the tag positioning information;

The first response message is identical to the first response message in method 300 and may be referred to in particular in the description of method 300.

Optionally, the first response message further includes at least one of the following information:

(3) Model identification information of the AI positioning model;

(6) Identification information of the first terminal;

(7) An LCS associated identifier corresponding to the first terminal;

In one implementation, S412 may include that, in a case where the first request message includes the positioning performance monitoring indication, the first terminal determines to report the AI-positioning information and the tag-positioning information, and the first terminal sends the first response message carrying the AI-positioning information and the tag-positioning information to the target network side device. In this implementation manner, the first terminal may determine and report the AI location information and the tag location information based on the location performance monitoring indication in the first request message.

In another implementation, S412 may include that, in a case where the first request message includes the positioning performance monitoring indication, the first terminal determines to report a positioning information deviation between the AI positioning information and the tag positioning information, and the first terminal sends the first response message carrying the positioning information deviation to the target network side device. In this implementation manner, the first terminal may determine and report a positioning information deviation between the AI positioning information and the tag positioning information based on the positioning performance monitoring indication in the first request message.

In yet another implementation, S412 may include that, in a case where the first request message includes the positioning performance monitoring indication, the first terminal determines to report the AI-positioning information, and the first terminal sends the first response message carrying the AI-positioning information to the target network side device. In this implementation manner, the first terminal may determine and report the AI-location information based on the location performance monitoring indication in the first request message.

In one or more implementations described above, the type information of the AI location information and the tag location information includes at least one of location result data or location intermediate data. Wherein the positioning intermediate data may include at least one of PRS measurement data and SRS measurement data.

Based on the same technical conception, the embodiment of the application also provides a method for acquiring the positioning performance.

Fig. 5 is a schematic flow chart of a method for obtaining positioning performance according to an embodiment of the present application, where the method 500 may be performed by the above-mentioned target device, and as shown in fig. 5, the method mainly includes the following steps.

S510, the target device acquires AI positioning performance information or first information corresponding to an AI positioning model sent by the target network side device, wherein the first information is used for indicating that the performance of the positioning method based on AI is reduced or insufficient.

Alternatively, as described in the method 300, the first information may be sent by the target network side device to the target device if the positioning performance value indicated by the AI-positioning performance information is below a second performance threshold.

In one implementation, before S510, the method may further include the target device sending a request message to the target network side device, where the request message requests or subscribes to the AI-location capability information, and the request message includes at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

A first performance threshold;

locating the performance metric type;

the area information is used for indicating the use range of the AI positioning model;

The request message for requesting or subscribing to the AI-positioning capability information is the same as the request message for requesting or subscribing to the AI-positioning capability information in method 300, and may be referred to in the relevant description of method 300.

Based on the above implementation manner, optionally, the obtaining, by the target device, AI positioning performance information or first information corresponding to the AI positioning model sent by the target network side device includes:

and the target equipment receives a response message sent by the target network side equipment, wherein the response message carries the AI positioning performance information or the first information.

Optionally, the response message sent by the target network side device may further carry at least one of the following:

Model identification information of the AI positioning model, for example, model ID (model ID);

function identification information (e.g., functionality ID or feature group ID) corresponding to the AI-positioning model.

Through the model identification information or the function identification information, the target device can acquire the received AI positioning performance information or the AI positioning model corresponding to the first information.

Optionally, the AI-positioning performance information includes at least one of:

The system comprises a positioning performance measurement type, a measurement type corresponding to a positioning performance value, a positioning performance value and an AI positioning performance information indication;

And the specific value corresponding to the positioning performance indicated by the AI positioning performance information is used for indicating, and optionally, the larger the positioning performance value is, the worse the indicated positioning performance is.

In the embodiment of the application, the target equipment comprises at least one of provider equipment of the AI positioning model, a first terminal, a second terminal and a positioning control function network element, wherein the first terminal is a terminal for positioning reasoning by using the AI positioning model, and the second terminal is a terminal for requesting or subscribing the AI positioning performance information or the first information from the target equipment.

In one implementation, in case the target device includes the first terminal, the second terminal or the positioning control function network element, the method may further include:

The target device performs a first target operation according to the AI-positioning performance information or the first information, wherein the first target operation includes one of:

For example, in the case where the AI positioning performance information indicates poor positioning performance or the first information is received, the AI positioning method may be switched to the non-AI positioning method;

the non-AI-location method may be switched to the AI-location method, for example, in the case where the AI-location performance information indicates that the positioning performance is good.

Optionally, the target device performs a first target operation according to the AI-positioning performance information or the first information, which may include:

in the event that it is determined that a target condition is met, the target device performs the first target operation, wherein the target condition includes at least one of:

The AI performance value indicated by the AI positioning performance information reaches a second performance threshold;

and receiving the first information sent by the target network side equipment.

In one implementation, in the case where the target device includes the AI positioning model provider device, the method may further include:

the target device performs a second target operation according to the AI-positioning performance information or the first information, the second target operation including at least one of:

retraining the AI-positioning model;

the new AI-positioning model is reselected.

For example, in the event that the AI-positioning performance information indicates poor positioning performance or the first information is received, the AI-positioning-model provider device may retrain the AI positioning model to improve the positioning performance of the AI positioning model, or the AI-positioning-model provider device may reselect a new AI positioning model, e.g., select an AI positioning model that has better positioning performance. By the implementation mode, under the condition that the positioning performance of the AI positioning model is poor, the AI positioning model can be retrained or a new AI positioning model can be reselected, so that the positioning performance can be improved.

With The technical solution provided by The embodiment of The present application, for The scenario shown in fig. 2a, on The Network upper layer (OTT) server or Network (NW) (e.g., core Network (CN) or RAN) side, the UE performs model reasoning, where The reasoning result is a target location (i.e., AI positioning result data), the positioning performance monitoring may be an LMF or a model provider device, and The performance monitoring triggering condition may be one of when a new model (model ID, UE, region of interest (area of interested, AOI)) is transferred to The UE, the model provider device (e.g., OTT) triggers The LMF, and when The new model is used, the UE triggers The LMF, the LMF monitors The new model by default. The data to be collected for performance monitoring may include one of the following, i.e. the AI positioning information from the UE based on the AI positioning model, the real positioning information of the UE from the UE or LMF (i.e. the tag positioning information), the target positioning information and the real positioning informationA location information offset (optional), a model ID or function ID, a UE ID (optional), a timestamp (optional). Subsequent operations of performance monitoring may include at least one of notifying the model provider device to retrain or reselect the AI location model, notifying the UE to fall back (i.e., disabling AI location based on the model, and changing to conventional location methods).

By adopting the technical scheme provided by the embodiment of the application, for the scene shown in fig. 2b, on the OTT server or Network (NW) side (for example, core Network (CN) or RAN), the UE performs model reasoning, the reasoning result is positioning measurement information (namely positioning intermediate data), positioning performance monitoring can be LMF or model provider equipment, and the performance monitoring triggering condition can be one of triggering LMF by the model provider equipment (for example, OTT server) when a new model (model ID, UE, AOI) is transferred to the UE, triggering LMF by the UE when the new model is used, and default monitoring of the new model by the LMF. And the data required for performance monitoring may include one of the following: model ID or function ID, UE ID (optional), timestamp (optional), scheme one, LMF based on the difference between PRS measurement data from UE (i.e. AI positioning information described above) and real positioning information from UE (i.e. tag positioning information described above) of UE or LMF using AI positioning model reasoning, scheme two, PRS measurement data from UE (i.e. AI positioning model reasoning) and real PRS measurement data from UE, scheme three, PRS measurement data from UE (i.e. AI positioning model reasoning) and real PRS measurement data from UE. Subsequent operations of performance monitoring may include at least one of notifying the model provider device to retrain or reselect the AI location model, notifying the UE to fall back (i.e., disabling AI location based on the model, and changing to conventional location methods).

In one scenario provided by embodiments of the present application, a positioning model provider (e.g., OTT server) or a positioning model reasoner (e.g., UE) triggers an AI positioning capability detection request to a target network side device (e.g., LMF), indicating a UE ID list (or AOI, area of interest (area of interested), which may be network converted to a UE list). The UE ID may be an intra-UE network identifier, such as an international mobile subscriber identity (International Mobile Subscriber Identity, IMSI), subscription permanent identifier (Subscription PERMANENT IDENTIFIER, SUPI), or an external UE network identifier, such as a general public Subscription identifier (Generic Public Subscription Identifier, GPSI), or other forms, but is not limited thereto. The AOI may be an intra-network area identifier, such as a cell identifier (cell ID), a tracking area identifier (TRACKING AREA IDENTITY, TAI), etc., or an off-network area identifier, such as a geographic area identifier, an administrative area identifier, etc.

In this scenario, the LMF may request that the relevant UE report two downlink positioning results, one AI positioning result and one real (ground truth) positioning result (alternatively referred to as a tag positioning result). Based on the reported data, the LMF calculates model performance. Fig. 6 shows a schematic flow chart of a method for obtaining positioning performance in this scenario, as shown in fig. 6, the method mainly includes the following steps:

Step 601a. A positioning model provider (e.g., OTT server or CN model training network element, etc.) performs a model training process to obtain an AI positioning model for AI positioning.

The AI positioning model is subsequently used by the UE in the AI reasoning process to obtain positioning results

Step 601b, the positioning model provider delivers the AI positioning model to at least one UE. The embodiment of the application is not limited to a model transfer method.

The LMF then triggers the positioning model performance monitoring.

The triggering manner may include at least one of the following steps 602 a-602 c:

Step 602a, a location model provider (e.g., OTT server) triggers a location performance monitoring request message to the LMF.

Wherein, the positioning performance monitoring request message comprises at least one of the following information:

(1) model ID identification information of the positioning model

(2) Functionality ID/feature group ID, function description information/feature group information corresponding to the positioning model;

(3) Positioning performance metric) a class used to indicate the positioning performance to be monitored, such as correctness, precision, recall, MSE, MAE, RMSE, etc.;

(4) A first performance Threshold (Threshold) is a Threshold value for indicating a performance value. When the threshold value is higher or lower, corresponding operations need to be executed, such as notifying OTT server, UE and the like.

(5) The UE ID(s) is the using object of the AI positioning model or the related UE (which UE possibly uses the positioning model), and can be UE network external identifiers such as GPSI, UE public network IP, user name in APP and the like, or network UE identifiers such as IMSI, SUPI, UE private network IP and the like.

(6) Regional information, namely the application range of the positioning model. The network can be an area in the network such as a cell ID, a TA and the like, or an area outside the network such as a geographic area, an administrative area and the like.

(7) And the time period is the time period for monitoring the positioning performance.

It should be noted that this message may occur after the OTT server positioning model training is completed, or at any time after the OTT server delivers the model to the UE.

It should be noted that the message may be sent from the model provider (OTT server) to the LMF through the transit or conversion of the intermediate network element. For example, the intermediate network element may be a mobile location center (Gateway Mobile Location Centre, GMLC), an AMF, etc., such as a NEF, gateway.

Step 602b, the UE (positioning model reasoning network element) triggers a positioning performance monitoring request message to the LMF.

The positioning performance monitoring request message is identical to the positioning performance monitoring request message in step 602a, see in particular the relevant description above.

It should be noted that this message may occur at any time after the UE acquires the positioning model, such as before or after the UE performs positioning reasoning based on the model.

It should be noted that the message may be sent from the UE to the LMF through the intermediary network element. The intermediate network elements include RAN, AMF, etc.

Step 602c, LMF triggers performance monitoring of the positioning model based on its decision. If the LMF decides to trigger performance monitoring of the positioning model for all AI positioning procedures, or for AI positioning procedures corresponding to a particular model ID.

Step 603, LMF obtains a Mobile Originating (MO) or Mobile terminating (Mobile Terminated, MT) location procedure for at least one UE location request. The location request is from a Mobile Originating (MO) or Mobile terminating (Mobile Terminated, MT) location request triggered by an LCS client (client) or a location user (client).

This step is optional.

Specifically, LCS CLIENT or location consumer may trigger a location request to the LMF through the GMLC and AMF. For positioning technology details, reference may be made to the 3GPP positioning related protocol.

In one implementation, the location request is based on an LTE location protocol (LTE Positioning Protocol, LPP) protocol stack that is forwarded by the AMF using a downlink NAS message.

Step 604, the LMF initiates a downlink positioning message to the UE, for triggering the UE to execute a downlink positioning procedure.

Optionally, the downlink positioning message includes at least one of the following information:

(1) The AI positioning information request indication is used for indicating the UE to report positioning information based on AI positioning model reasoning;

(2) The legacy positioning information request indicates that the UE is instructed to report legacy (i.e., non-AI-method) positioning information (i.e., tag positioning information as described above), which is subsequently used as a true value (ground truth) of the positioning information, or referred to as a positioning baseline value, to be compared with the AI positioning information for the LMF to calculate positioning performance.

(3) The positioning deviation request indicates that the deviation value used for indicating the UE to report the AI positioning information and the traditional positioning information can be absolute deviation value or relative deviation value (such as normalized deviation value), and is not limited.

(4) And the positioning performance monitoring indication is used for indicating to the UE that the LMF is to be monitored to acquire AI positioning performance information. The indication information is used for the UE to decide what positioning information to report by itself, so that the subsequent LMF can acquire the AI positioning performance based on the information reported by the UE.

The positioning information includes a calculated value of a positioning result (positioning) by the UE or a calculated value of a positioning intermediate measurement (e.g., PRS measurement). Therefore, the downlink positioning message may further indicate specific type information (location or specific measurement) of positioning information reported by the UE.

According to the downlink positioning message in step 604, the UE synchronously performs step 605 and step 606 to obtain AI-positioning information and legacy-positioning information.

Step 605, the UE performs downlink positioning measurement, and performs reasoning based on the AI positioning model to obtain AI positioning information.

Specifically, the UE performs measurement on the positioning signal according to the type of input parameter required by the AI positioning model, and obtains the required measurement quantity. The acquired positioning signal measurement is input into an AI positioning model and an inference process is performed to obtain an output result, which is the AI positioning information.

Step 606, the UE performs downlink positioning measurement to obtain tag positioning information.

Wherein, for comparison, the two positioning information in step 605 and step 606 may be the positioning information of the UE at the same time point (or within a time difference range).

Step 607, the UE sends an uplink positioning message to the LMF, where the uplink positioning message includes at least one of the following information:

(1) The first positioning information is AI positioning information;

(2) The AI indication is used for indicating that the first positioning information is positioning information based on AI model reasoning;

(3) And the second positioning information corresponds to the traditional positioning information.

(4) The tag data (Labeled data)/ground truth value indication (ground truth indication) is used to indicate that the second positioning information is legacy positioning information or that the second positioning information is to be treated as a positioning tag value/truth value/baseline value.

(5) Difference of positioning information AI positioning information and conventional positioning information.

(6)Model ID;

(7)Functionality ID/feature group ID;

(8) UE ID/LCS related (correlation) ID corresponding to a single UE;

(9) And the time information is a time stamp or a time period which is acquired by the first positioning information, the second positioning information and the positioning information difference value.

And 608, the LMF calculates the performance of the positioning model based on the positioning information reported by the UE.

Based on step 607, for the same model ID/Functionality ID/feature group ID, the LMF obtains data (including the first and second positioning information, the difference, etc. described in step 607) reported by at least one UE, and forms these data into associated sample data. For example, the LMF forms a sample data set from AI location information and legacy location information acquired by the same UE at the same time stamp, or forms a sample data from a difference in location information reported by the same UE at the same time stamp as the LMF.

Based on the plurality of sample data (sets), the LMF calculates the performance of the positioning model. Specifically, the following implementations may be included, but are not limited to:

Mode 1 when the sample data set contains AI positioning measurement values (e.g., PRS measurement) and conventional positioning measurement values, the LMF directly compares the two measurement values and obtains a calculation result (e.g., variance, MSE, MAE, etc.) based on a specific built-in algorithm. The LMF takes the calculation result as a positioning performance value.

Mode 2 when the sample data set contains an AI positioning result (such as a positioning result) and a traditional positioning result, the LMF directly compares the two positioning results and obtains a calculation result (such as variance, MSE, MAE, etc.) based on a specific built-in algorithm. The LMF takes the calculation result as a positioning performance value.

Mode 3 when the sample data set contains a deviation value of AI positioning information (positioning result or PRS measurement) and conventional positioning information, the LMF obtains a calculation result (such as MAE, correctness) based on the deviation value and a specific built-in algorithm.

Step 609a, LMF sends a location capability response message to OTT server.

Step 603 b, lmf sends a location capability response message to UEOTT sever

Wherein, the positioning performance response message in step 609a and step 609b may include at least one of the following:

-model ID

-Functionality ID/feature group ID

Positioning performance metric

-Positioning performance value (acquired in step 608)

Regional information: the range of use of the positioning model. The network can be an area in the network such as a cell ID, a TA and the like, or an area outside the network such as a geographic area, an administrative area and the like.

Time period: time period required for positioning performance monitoring.

-Positioning performance degradation or insufficient threshold indication information.

Back-off (Fallback) recommendation indicates that OTT server or UE is recommended to stop using AI positioning methods.

Alternatively, the LMF may perform step 609a or step 609b after step 608 is completed;

alternatively, step 609a or step 609b may be sent after the LMF determines that the positioning performance satisfies the condition. The condition is that the positioning performance value is below a certain performance threshold or above a certain performance threshold, in particular determined in relation to the magnitude of the positioning performance value and the positioning performance.

It should be noted that in steps 602a, 602b, 603, 604, 607, 609a and 609b, the AMF may need to convert the UE ID(s) into LCS correlation ID.

It should be noted that after the LMF is triggered to perform performance monitoring, in one implementation, the LMF may actively trigger UE positioning to collect performance data, or in another implementation, the LMF may also perform necessary performance data collection in a UE positioning procedure triggered by other subscribers (such as LCS CLIENT, NWDAF, or the UE itself), in other words, the LMF does not actively trigger the UE positioning procedure due to performance monitoring.

In the above scenario, unlike fig. 6, the LMF here requests the UE to report the downlink location (AI location information), while the LMF requests the RAN to perform the uplink location (as ground truth). The LMF correlates the uplink and downlink positioning results corresponding to the same UE (LCS correlation ID by the same allocation) and the same timestamp to form statistical sample data. Based on the sample data, the LMF calculates model performance. Fig. 7 shows a schematic flow chart of a method for obtaining positioning performance in this scenario, as shown in fig. 7, the method mainly includes the following steps:

steps 701-703, steps 601a-603.

In step 704, the lmf decides to perform the downlink positioning of step 705 and the uplink positioning of step 706 simultaneously for the UE in the UE list or LCS related ID list.

In step 705, the lmf initiates a downlink positioning procedure for the UE.

In step 705, the LMF may initiate a downlink positioning message to the UE, the UE performs reasoning based on the AI positioning Model to obtain AI positioning information, and then the UE sends a location report message to the LMF, where the location report message may include information such as AI positioning information, AI indication, model ID/Functionality ID/feature group ID, UE ID/LCS related (correlation) ID, timestamp, and the like.

In step 706, the lmf initiates an uplink positioning procedure to the RAN.

In step 706, the LMF may send a request message to the RAN, which, after receiving the request message, sends a response message to the LMF, which may include tag location information of the UE.

In step 707, the lmf associates the same LCS related ID, AI location information corresponding to the same timestamp, and tag location information to form sample data.

In step 708, the LMF calculates the positioning performance (e.g., accuracy) of the AI positioning Model based on the sample data for the same Model ID/Functionality ID/feature group ID.

Step 709, steps 609a and 609b are synchronized.

It should be noted that in steps 702a, 702b, 703, 704, 707, 709a and 709b, the AMF may need to convert the UE ID(s) into LCS correlation ID.

After the LMF is triggered to monitor performance, the LMF may actively trigger UE positioning for collecting performance data, or the LMF may perform necessary performance data collection in the UE positioning procedure.

In the scenario described above, in step 602a, the OTT server may not provide the UE ID, only the model ID of the AI location model, i.e. the LMF does not know which UEs need to be monitored for location accuracy. For this case, fig. 8 shows a further flowchart of a method of acquiring positioning performance, as shown in fig. 8, which mainly includes the steps of:

Step 801a. A positioning model provider (e.g., OTT server or CN model training network element, etc.) performs a model training process to obtain an AI positioning model for AI positioning.

Step 801b, the positioning model provider communicates the AI positioning model to at least one UE. The embodiment of the application is not limited to a model transfer method.

At step 802, a location model provider (e.g., OTT server) triggers a location performance monitoring request message to the LMF.

The positioning performance monitoring request message comprises the following information:

(1)model ID functionality ID/feature group ID;

(2) The time period is the time period for monitoring the positioning performance;

(3)AF ID。

In step 803, the lmf obtains a positioning procedure for at least one UE positioning request. The location request is from a Mobile Originating (MO) or Mobile terminating (Mobile Terminated, MT) location request triggered by an LCS client (client) or a location user (client).

In step 804, the ue reports the location to the LMF, which may include AI location information, AI indication, model ID functionality ID/feature group ID.

Step 805, the lmf determines that the AI-positioning model used by the UE is an AI-positioning model to be monitored, and executes step 806;

steps 806-811, steps 604-609a/609b.

In this embodiment, the LMF can only passively wait for the UE to report the AI-based positioning result once (indicating the corresponding model ID), and then acquire positioning performance by using the method described in fig. 6 or fig. 7.

After the LMF is triggered to monitor performance, the LMF waits for the UE to report a positioning result (a model ID appears), and performs necessary performance data acquisition in a subsequent UE positioning procedure.

In addition, in step 806 or 809, the LMF may request the UE to directly report the AI location result and the deviation value of the conventional location result.

By the method provided by the embodiment of the application, the AI positioning model used by the UE can be subjected to positioning model performance monitoring, and corresponding operation can be executed according to the monitoring result so as to improve the positioning accuracy.

According to the method for acquiring the positioning performance, provided by the embodiment of the application, the execution main body can be the device for acquiring the positioning performance. In the embodiment of the present application, an example of a method for acquiring positioning performance by using a positioning performance acquiring device is described.

Fig. 9 shows a schematic structural diagram of a positioning performance acquisition device according to an embodiment of the present application, and as shown in fig. 9, the device 900 includes a first acquisition module 901 and a second acquisition module 902.

In the embodiment of the application, a first obtaining module 901 is configured to obtain target positioning data corresponding to at least one first terminal, where the target positioning data includes at least one of AI positioning information and tag positioning information, where the AI positioning information is positioning information generated by reasoning based on an AI positioning model, positioning information deviation is configured to indicate deviation between the AI positioning information and the tag positioning information, and a second obtaining module 902 is configured to obtain AI positioning performance information based on the target positioning data.

In one implementation, the tag location information includes location information of the at least one first terminal acquired through a non-AI location method.

In one implementation manner, the terminal device further comprises a transmission module, wherein the transmission module is used for transmitting the AI positioning performance information to a target device, and the target device comprises at least one of a provider device, a first terminal, a second terminal and a positioning control function network element of the AI positioning model.

In one implementation manner, the transmission module is further configured to receive a request message that the target device requests or subscribes to the AI-positioning performance information, where the request message includes at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

A first performance threshold;

locating the performance metric type;

In one implementation, the AI-positioning performance information includes at least one of:

locating the performance metric type;

And locating the performance value.

In one implementation manner, the system further comprises a transmission module, wherein the transmission module is used for transmitting first information to target equipment when the positioning performance value indicated by the AI positioning performance information is lower than a second performance threshold value, the first information is used for indicating that the performance of the positioning method based on AI is reduced or insufficient, and the target equipment comprises at least one of provider equipment, a first terminal, a second terminal and a positioning control function network element of the AI positioning model.

In one implementation, AI location performance information is obtained based on the target location data, including one of:

When the target positioning data comprise the AI positioning information and the tag positioning information, the target network side equipment takes the AI positioning information and the tag positioning information corresponding to each first terminal in association time as input information, and calculates to obtain the AI positioning performance information;

and under the condition that the target positioning data comprises positioning information deviation values, taking the positioning information deviation values corresponding to the first terminals as input information, and calculating to obtain the AI positioning performance information.

In one implementation, obtaining the target positioning data corresponding to the at least one first terminal includes one of:

Acquiring the AI positioning information and the tag positioning information from the at least one first terminal, wherein the AI positioning information is positioning information generated by the at least one first terminal based on the AI positioning model in an inference manner, and the tag positioning information is positioning information acquired by the at least one first terminal based on a non-AI positioning method;

The AI positioning information is acquired from the at least one first terminal, the tag positioning information is acquired from access network equipment, wherein the AI positioning information is positioning information generated by reasoning the at least one first terminal based on the AI positioning model, and the tag positioning information is positioning information for the at least one first terminal, which is acquired by the access network equipment based on a non-AI positioning method.

The AI positioning information is obtained from the at least one first terminal, the tag positioning information is obtained from the local, wherein the AI positioning information is generated by the at least one first terminal based on the AI positioning model in an inference mode, and the tag positioning information is obtained by the target network side equipment based on a non-AI positioning method and is aimed at the at least one first terminal.

Acquiring the positioning information deviation from the at least one first terminal;

And generating the positioning information deviation based on the acquired AI positioning information and the tag positioning information.

In one implementation, obtaining target positioning data corresponding to at least one first terminal includes:

the method comprises the steps of sending a first request message to at least one first terminal, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated based on reasoning of an AI positioning model, tag positioning information request indication used for indicating to report tag positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication used for indicating to-be-monitored AI positioning performance information of target network side equipment, type information of positioning information used for indicating that the AI positioning information or the specific type of the tag positioning information required to be reported is positioning result data or positioning intermediate data;

Obtaining target positioning data corresponding to at least one first terminal, including:

A first response message is received from the at least one first terminal, the first response message including at least one of the AI location information, the tag location information, a location information offset between the AI location information and the tag location information.

In one implementation, the first response message further includes at least one of the following information:

the first indication information is used for indicating that the AI positioning information is positioning information obtained based on the AI positioning model reasoning;

the second indication information is used for indicating that the tag positioning information is real positioning information or positioning information acquired based on a non-AI positioning method;

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

identification information of the first terminal;

A location service (LCS) associated identifier corresponding to the first terminal;

and the time information is used for indicating the acquisition time corresponding to the AI positioning information or the tag positioning information.

In one implementation, obtaining the target positioning data corresponding to the at least one first terminal further includes:

Sending a second request message to the access network equipment, wherein the second request message comprises identification information or LCS (liquid crystal control) associated identification information of the at least one first terminal;

A second response message is received from the access network device, the second response message comprising tag location information of the at least one first terminal.

In one implementation, the AI location information and the type information of the tag location information includes at least one of location result data or location intermediate data.

In one implementation, the positioning intermediate data includes at least one of PRS measurement data and SRS measurement data.

The device for acquiring positioning performance provided by the embodiment of the present application can implement each process implemented by the method embodiment of fig. 3, and achieve the same technical effects, and in order to avoid repetition, a detailed description is omitted here.

Fig. 10 shows a schematic structural diagram of a reporting device for positioning data according to an embodiment of the present application, and as shown in fig. 10, the device 1000 includes a first receiving module 1001 and a sending module 1002.

In the embodiment of the present application, a first receiving module 1001 is configured to receive a first request message sent by a target network side device, where the first request message includes at least one information including an AI location information request indication for indicating to report location information generated by reasoning based on an AI location model, a tag location information request indication for indicating to report tag location information, a location deviation request indication for indicating to report location deviation between the AI location information and the tag location information, a location performance monitoring indication for indicating to the target network side device that AI location performance information is to be monitored, location information type information for indicating that the AI location information or a specific type of the tag location information to be reported is location result data or location intermediate data, and a sending module 1002 for sending a first response message to the target network side device, where the first response message includes at least one information including AI location information, tag location information, location information located between the AI location information and the tag location information, where the AI location information is the AI location information generated by reasoning based on the AI location model by the first terminal.

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

identification information of the first terminal;

In one implementation, the tag location information includes location information of the first terminal acquired through a non-AI location method.

In one implementation, the sending the first response message to the target network side device includes one of the following:

determining to report the AI positioning information and the tag positioning information under the condition that the first request message comprises the positioning performance monitoring indication, and sending the first response message carrying the AI positioning information and the tag positioning information to the target network side equipment;

Determining to report the positioning information deviation between the AI positioning information and the tag positioning information under the condition that the first request message comprises the positioning performance monitoring indication, and sending the first response message carrying the positioning information deviation to the target network side equipment;

and under the condition that the first request message comprises the positioning performance monitoring indication, determining to report the AI positioning information, and sending the first response message carrying the AI positioning information to the target network side equipment.

In one implementation, the sending module 1002 is further configured to at least one of:

performing reasoning based on the AI positioning model to acquire the AI positioning information;

Acquiring the tag positioning information based on a non-AI positioning method;

And acquiring the deviation of the positioning information according to the AI positioning information and the tag positioning information.

In one implementation, performing reasoning based on the AI-positioning model to obtain the AI-positioning information includes one of:

under the condition that the first request message comprises the AI positioning information request indication, performing reasoning based on the AI positioning model to acquire the AI positioning information;

under the condition that the first request message comprises the positioning deviation request indication, performing reasoning based on the AI positioning model to acquire the AI positioning information;

and under the condition that the first request message comprises the positioning performance monitoring indication, performing reasoning based on the AI positioning model to acquire the AI positioning information.

In one implementation, the first receiving module 1001 is further configured to obtain the AI positioning model.

In one implementation, the AI location information and the type information of the tag location information includes at least one of location result data, location intermediate data.

The reporting device for positioning data provided by the embodiment of the present application can implement each process implemented by the method embodiment of fig. 4, and achieve the same technical effects, so that repetition is avoided, and no further description is provided herein.

Fig. 11 shows a schematic structural diagram of a positioning performance obtaining apparatus according to an embodiment of the present application, and as shown in fig. 11, the apparatus 1100 includes a second receiving module 1101 and a third obtaining module 1102.

In the embodiment of the present application, the second receiving module 1101 is configured to receive AI-positioning performance information or first information corresponding to an AI-positioning model sent by a target network side device, where the first information is used to indicate that the AI-based positioning method has reduced or insufficient performance, and the third obtaining module 1102 is configured to obtain the AI-positioning performance information or the first information.

The apparatus may be applied to the target device described above.

In one implementation manner, the system further comprises a sending module, a receiving module and a sending module, wherein the sending module is used for sending a request message for requesting or subscribing the AI positioning performance information to the target network side equipment, and the request message comprises at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

A first performance threshold;

locating the performance metric type;

In one implementation manner, acquiring AI positioning performance information or first information corresponding to an AI positioning model sent by a target network side device includes:

And receiving a response message sent by the target network side equipment, wherein the response message carries the AI positioning performance information or the first information.

In one implementation, the response message further carries at least one of the following:

model identification information of the AI positioning model;

and function identification information corresponding to the AI positioning model.

locating the performance metric type;

And locating the performance value.

In one implementation, the target device comprises at least one of a provider device of the AI positioning model, a first terminal, a second terminal and a positioning control function network element, wherein the first terminal is a terminal for performing positioning reasoning by using the AI positioning model, and the second terminal is a terminal for requesting or subscribing the AI positioning performance information or the first information from the target device.

In one implementation, in the case that the target device includes the first terminal, the second terminal or the positioning control function network element, the method further includes an execution module, configured to execute a first target operation according to the AI-positioning performance information or the first information, where the first target operation includes one of:

switching from an AI-positioning method to a non-AI-positioning method;

switching from the non-AI-location method to the AI-location method.

In one implementation, performing a first target operation according to the AI-positioning performance information, or the first information, includes:

Executing the first target operation in a case that a target condition is determined to be met, wherein the target condition comprises at least one of the following:

and receiving the first information sent by the target network side equipment.

In one implementation, the system further comprises an execution module for executing a second target operation according to the AI positioning performance information or the first information, where the target device includes the AI positioning model provider device, the second target operation including at least one of:

retraining the AI-positioning model;

the new AI-positioning model is reselected.

The device for acquiring positioning performance provided by the embodiment of the present application can implement each process implemented by the method embodiment of fig. 5, and achieve the same technical effects, and in order to avoid repetition, a detailed description is omitted here.

As shown in fig. 12, the embodiment of the present application further provides a communication device 1200, including a processor 1201 and a memory 1202, where the memory 1202 stores a program or an instruction that can be executed on the processor 1201, for example, when the communication device 1200 is a terminal, the program or the instruction is executed by the processor 1201 to implement each step of the embodiment of the method 300 for reporting positioning data, or implement each step of the embodiment of the method 500 for obtaining positioning performance, and achieve the same technical effects. When the communication device 1200 is a network side device, the program or the instruction, when executed by the processor 1201, implements the steps of the embodiment of the method 300 or 500 for obtaining positioning performance, and can achieve the same technical effects, so that repetition is avoided, and no further description is given here.

The embodiment of the application also provides a terminal, which comprises a processor and a communication interface, wherein the communication interface is coupled with the processor, and the processor is used for running programs or instructions to realize the steps in the embodiment of the method shown in fig. 4. The terminal embodiment corresponds to the terminal-side method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the terminal embodiment, and the same technical effects can be achieved. Specifically, fig. 13 is a schematic diagram of a hardware structure of a terminal for implementing an embodiment of the present application.

The terminal 1300 includes, but is not limited to, at least some of the components of a radio frequency unit 1301, a network module 1302, an audio output unit 1303, an input unit 1304, a sensor 1305, a display unit 1306, a user input unit 1307, an interface unit 1308, a memory 1309, and a processor 1310.

Those skilled in the art will appreciate that the terminal 1300 may further include a power source (e.g., a battery) for supplying power to the various components, and the power source may be logically connected to the processor 13 through a power management system, so as to perform functions of managing charging, discharging, power consumption management, etc. through the power management system. The terminal structure shown in fig. 13 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.

It should be appreciated that in embodiments of the present application, the input unit 1304 may include a graphics processing unit (Graphics Processing Unit, GPU) 13041 and a microphone 13042, where the graphics processing unit 13041 processes image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 1306 may include a display panel 13061, and the display panel 13061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 1307 includes at least one of a touch panel 13071 and other input devices 13072. Touch panel 13071, also referred to as a touch screen. The touch panel 13071 may include two parts, a touch detection device and a touch controller. Other input devices 13072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.

In the embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 1301 may transmit the downlink data to the processor 1310 for processing, and in addition, the radio frequency unit 1301 may send uplink data to the network side device. Typically, the radio unit 1301 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

Memory 1309 may be used to store software programs or instructions and various data. The memory 1309 may mainly include a first memory area storing programs or instructions and a second memory area storing data, wherein the first memory area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 1309 may include volatile memory or nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static random access memory (STATIC RAM, SRAM), dynamic random access memory (DYNAMIC RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate Synchronous dynamic random access memory (Double DATA RATE SDRAM, DDRSDRAM), enhanced Synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCH LINK DRAM, SLDRAM), and Direct random access memory (DRRAM). Memory 1309 in embodiments of the application include, but are not limited to, these and any other suitable types of memory.

Processor 1310 may include one or more processing units, and optionally, processor 1310 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, etc., and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 1310.

Wherein, radio frequency unit 1301 is configured to:

The method comprises the steps of receiving a first request message sent by target network side equipment, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated by reasoning based on an AI positioning model, tag positioning information request indication used for indicating to report tag positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication used for indicating to-be-monitored AI positioning performance information of the target network side equipment, and type information of positioning information used for indicating whether the AI positioning information or the specific type of the tag positioning information to be reported is positioning result data or positioning intermediate data;

And sending a first response message to the target network side equipment, wherein the first response message comprises at least one of AI positioning information, tag positioning information and positioning information deviation between the AI positioning information and the tag positioning information, and the AI positioning information is positioning information generated by the first terminal based on an AI positioning model in an inference mode.

Or a radio frequency unit 1301, configured to obtain AI positioning performance information or first information corresponding to an AI positioning model sent by a target network side device, where the first information is used to indicate that performance of an AI-based positioning method is reduced or insufficient

It can be appreciated that the implementation procedure of each implementation manner mentioned in this embodiment may refer to the related description of the method embodiment 400, and achieve the same or corresponding technical effects, and are not described herein again for avoiding repetition.

The embodiment of the application also provides network side equipment, which comprises a processor and a communication interface, wherein the communication interface is coupled with the processor, and the processor is used for running programs or instructions to realize the steps of the method embodiment shown in fig. 3 or 5. The network side device embodiment corresponds to the network side device method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the network side device embodiment, and the same technical effects can be achieved.

Specifically, the embodiment of the application also provides network side equipment. As shown in fig. 14, the network-side apparatus 1400 includes an antenna 1401, a radio frequency device 1402, a baseband device 1403, a processor 1404, and a memory 1405. An antenna 1401 is coupled to a radio 1402. In the uplink direction, the radio frequency device 1402 receives information via the antenna 1401 and transmits the received information to the baseband device 1403 for processing. In the downlink direction, the baseband device 1403 processes information to be transmitted, and transmits the processed information to the radio frequency device 1402, and the radio frequency device 1402 processes the received information and transmits the processed information through the antenna 1401.

The method performed by the network side device in the above embodiment may be implemented in the baseband apparatus 1403, and the baseband apparatus 1403 includes a baseband processor.

The baseband apparatus 1403 may, for example, include at least one baseband board, where a plurality of chips are disposed, as shown in fig. 14, where one chip, for example, a baseband processor, is connected to the memory 1405 through a bus interface, so as to invoke a program in the memory 1405 to perform the network device operation shown in the above method embodiment.

The network-side device may also include a network interface 1406, such as a common public radio interface (Common Public Radio Interface, CPRI).

Specifically, the network side device 1400 according to the embodiment of the present application further includes instructions or programs stored in the memory 1405 and capable of running on the processor 1404, and the processor 1404 calls the instructions or programs in the memory 1405 to execute the methods executed by the modules shown in fig. 9 or 11, so as to achieve the same technical effects, and thus, for avoiding repetition, the description is omitted herein.

Specifically, the embodiment of the application also provides network side equipment. As shown in fig. 15, the network-side device 1500 includes a processor 1501, a network interface 1502, and a memory 1503. The network interface 1502 is, for example, a common public radio interface (common public radio interface, CPRI).

Specifically, the network side device 1500 of the embodiment of the present application further includes instructions or programs stored in the memory 1503 and capable of running on the processor 1501, and the processor 1501 calls the instructions or programs in the memory 1503 to execute the methods executed by the modules shown in fig. 9 or 11, and achieves the same technical effects, so that repetition is avoided and detailed description thereof is omitted.

The embodiment of the application also provides a readable storage medium, on which a program or an instruction is stored, which when executed by a processor, implements each process of each method embodiment described above, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here.

Wherein the processor is a processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc. In some examples, the readable storage medium may be a non-transitory readable storage medium.

The embodiment of the application further provides a chip, which comprises a processor and a communication interface, wherein the communication interface is coupled with the processor, and the processor is used for running programs or instructions to realize the processes of the above method embodiments, and can achieve the same technical effects, so that repetition is avoided, and the description is omitted here.

It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.

The embodiments of the present application further provide a computer program/program product stored in a storage medium, where the computer program/program product is executed by at least one processor to implement the processes of the above method embodiments, and achieve the same technical effects, and are not repeated herein.

The embodiment of the application also provides a communication system, which comprises a terminal, network side equipment and target equipment, wherein the terminal can be used for executing the steps of the embodiment of the method 400, the network side equipment can be used for executing the steps of the embodiment of the method 300, and the target equipment can be used for executing the steps of the embodiment of the method 500.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

From the description of the embodiments above, it will be apparent to those skilled in the art that the above-described example methods may be implemented by means of a computer software product plus a necessary general purpose hardware platform, but may also be implemented by hardware. The computer software product is stored on a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes instructions for causing a terminal or network side device to perform the methods according to the embodiments of the present application.

The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms of embodiments may be made by those of ordinary skill in the art without departing from the spirit of the application and the scope of the claims, which fall within the protection of the present application.

Claims

1. The method for acquiring the positioning performance is characterized by comprising the following steps:

the target network side equipment acquires target positioning data corresponding to at least one first terminal, wherein the target positioning data comprises at least one of artificial intelligent AI positioning information and tag positioning information, wherein the AI positioning information is positioning information generated by reasoning based on an AI positioning model;

And the target network side equipment acquires AI positioning performance information based on the target positioning data.

2. The method of claim 1, wherein the tag location information comprises location information of the at least one first terminal obtained by a non-AI location method.

3. The method according to claim 1 or 2, characterized in that the method further comprises:

the target network side equipment sends the AI positioning performance information to target equipment, wherein the target equipment comprises at least one of provider equipment, a first terminal, a second terminal and a positioning control function network element of the AI positioning model.

4. A method according to claim 3, characterized in that the method further comprises:

the target network side equipment receives a request message for requesting or subscribing the AI positioning performance information by the target equipment, wherein the request message comprises at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

A first performance threshold;

locating the performance metric type;

5. The method of any of claims 1 to 4, wherein the AI-positioning performance information comprises at least one of:

locating the performance metric type;

And locating the performance value.

6. The method according to any one of claims 1 to 5, wherein after the target network side device acquires AI-positioning performance information based on the target positioning data, the method further comprises:

And under the condition that the positioning performance value indicated by the AI positioning performance information is lower than a second performance threshold value, the target network side equipment sends first information to target equipment, wherein the first information is used for indicating that the performance of the positioning method based on the AI is reduced or insufficient, and the target equipment comprises at least one of provider equipment, a first terminal, a second terminal and a positioning control function network element of the AI positioning model.

7. The method according to any one of claims 1 to 6, wherein the target network side device obtains AI-location performance information based on the target location data, including one of:

And under the condition that the target positioning data comprises positioning information deviation values, the target network side equipment takes the positioning information deviation values corresponding to the first terminals as input information, and calculates to obtain the AI positioning performance information.

8. The method according to any one of claims 1 to 7, wherein, in the case where the target positioning data includes the AI positioning information and the tag positioning information, the target network side device obtaining target positioning data corresponding to at least one first terminal includes one of:

The target network side equipment acquires the AI positioning information and the tag positioning information from the at least one first terminal, wherein the AI positioning information is positioning information generated by the at least one first terminal based on the AI positioning model in an inference mode, and the tag positioning information is positioning information acquired by the at least one first terminal based on a non-AI positioning method;

The target network side equipment acquires the AI positioning information from the at least one first terminal, and acquires the tag positioning information from an access network equipment, wherein the AI positioning information is positioning information generated by reasoning the at least one first terminal based on the AI positioning model, and the tag positioning information is positioning information for the at least one first terminal, which is acquired by the access network equipment based on a non-AI positioning method;

The target network side equipment acquires the AI positioning information from the at least one first terminal, and the target network side equipment acquires the tag positioning information from the local, wherein the AI positioning information is positioning information generated by reasoning the at least one first terminal based on the AI positioning model, and the tag positioning information is positioning information for the at least one first terminal, which is acquired by the target network side equipment based on a non-AI positioning method.

9. The method according to any one of claims 1 to 8, wherein, in the case where the target positioning data includes the positioning information deviation, the target network side device obtaining target positioning data corresponding to at least one first terminal includes one of:

The target network side equipment acquires the positioning information deviation from the at least one first terminal;

And the target network side equipment generates the positioning information deviation based on the acquired AI positioning information and the tag positioning information.

10. The method according to claim 8 or 9, wherein the target network side device obtains target positioning data corresponding to at least one first terminal, including:

The target network side equipment sends a first request message to the at least one first terminal, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated by reasoning based on the AI positioning model, tag positioning information request indication used for indicating to report tag positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication used for indicating to the target network side equipment to monitor AI positioning performance information, and type information of positioning information used for indicating that the AI positioning information or the specific type of the tag positioning information required to be reported is positioning result data or positioning intermediate data;

the target network side equipment acquires target positioning data corresponding to at least one first terminal, and the target positioning data comprises:

The target network side equipment receives a first response message from the at least one first terminal, wherein the first response message comprises at least one of the AI positioning information, the tag positioning information and a positioning information deviation between the AI positioning information and the tag positioning information.

11. The method of claim 10, wherein the first response message further comprises at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

identification information of the first terminal;

12. The method according to any one of claims 8 to 11, wherein the target network side device obtaining target positioning data corresponding to at least one first terminal further includes:

The target network side equipment sends a second request message to the access network equipment, wherein the second request message comprises identification information of at least one first terminal or LCS (liquid crystal control) associated identification information;

The target network side equipment receives a second response message from the access network equipment, wherein the second response message comprises the label positioning information of the at least one first terminal.

13. The method of any one of claims 1 to 12, wherein the AI-location information and the type information of the tag-location information includes at least one of location result data or location intermediate data.

14. The method of claim 13, wherein the positioning intermediate data comprises at least one of positioning reference signal, PRS, measurement data and positioning sounding reference signal, SRS, measurement data.

15. The method for reporting the positioning data is characterized by comprising the following steps:

The first terminal receives a first request message sent by target network side equipment, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated based on an AI positioning model in an inference mode, tag positioning information request indication used for indicating to report tag positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication used for indicating to the target network side equipment AI positioning performance information to be monitored, type information of positioning information used for indicating whether the AI positioning information or the specific type of the tag positioning information to be reported is positioning result data or positioning intermediate data;

The first terminal sends a first response message to the target network side equipment, wherein the first response message comprises at least one of AI positioning information, tag positioning information and positioning information deviation between the AI positioning information and the tag positioning information, and the AI positioning information is positioning information generated by the first terminal based on an AI positioning model in an inference mode.

16. The method of claim 15, wherein the first response message further comprises at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

identification information of the first terminal;

17. The method according to claim 15 or 16, wherein the tag positioning information comprises positioning information of the first terminal obtained by a non-AI positioning method.

18. The method according to any one of claims 15 to 17, wherein the first terminal sends a first response message to the target network side device, including one of:

The first terminal determines to report the AI positioning information and the tag positioning information under the condition that the first request message comprises the positioning performance monitoring indication, and the first terminal sends a first response message carrying the AI positioning information and the tag positioning information to the target network side equipment;

The first terminal determines to report the positioning information deviation between the AI positioning information and the tag positioning information under the condition that the first request message comprises the positioning performance monitoring indication, and the first terminal sends the first response message carrying the positioning information deviation to the target network side equipment;

And under the condition that the first request message comprises the positioning performance monitoring indication, the first terminal determines to report the AI positioning information, and the first terminal sends the first response message carrying the AI positioning information to the target network side equipment.

19. The method according to any of the claims 15 to 18, characterized in that before the first terminal sends a first response message to the target network side device, the method further comprises at least one of:

the first terminal performs reasoning based on the AI positioning model to acquire the AI positioning information;

The first terminal obtains the tag positioning information based on a non-AI positioning method;

And the first terminal acquires the deviation of the positioning information according to the AI positioning information and the tag positioning information.

20. The method of claim 19, wherein the first terminal performing reasoning to obtain the AI-positioning information based on the AI-positioning model comprises one of:

21. The method according to claim 19 or 20, wherein before the first terminal performs reasoning to obtain the AI-positioning information based on the AI-positioning model, the method further comprises:

and the first terminal acquires the AI positioning model.

22. The method of any one of claims 15 to 21, wherein the AI-location information and the type information of the tag-location information includes at least one of location result data, location intermediate data.

23. The method of claim 22, wherein the positioning intermediate data comprises at least one of positioning reference signal, PRS, measurement data and positioning sounding reference signal, SRS, measurement data.

24. The method for acquiring the positioning performance is characterized by comprising the following steps:

The method comprises the steps that target equipment acquires AI positioning performance information or first information corresponding to an AI positioning model sent by target network side equipment, wherein the first information is used for indicating that the performance of a positioning method based on AI is reduced or insufficient.

25. The method of claim 24, wherein before the target device obtains AI-positioning performance information or first information corresponding to an AI-positioning model sent by a target network-side device, the method further comprises:

The target device sends a request message for requesting or subscribing the AI positioning performance information to the target network side device, wherein the request message comprises at least one of the following information:

model identification information of the AI positioning model;

Function identification information corresponding to the AI positioning model;

A first performance threshold;

locating the performance metric type;

26. The method of claim 25, wherein the target device obtains AI-positioning performance information or first information corresponding to an AI-positioning model sent by a target network side device, and the method comprises:

27. The method of claim 26, wherein the response message further carries at least one of:

model identification information of the AI positioning model;

28. The method of any of claims 24 to 27, wherein the AI-positioning performance information comprises at least one of:

locating the performance metric type;

And locating the performance value.

29. The method of any one of claims 24 to 28, wherein the target device comprises at least one of a provider device of the AI-positioning model, a first terminal that is a terminal that uses the AI-positioning model for positioning reasoning, a second terminal that is a terminal that requests or subscribes to the AI-positioning performance information, or the first information, and a positioning control function network element.

30. The method of claim 29, wherein in the case where the target device comprises the first terminal, the second terminal, or the positioning control function network element, the method further comprises:

switching from an AI-positioning method to a non-AI-positioning method;

switching from the non-AI-location method to the AI-location method.

31. The method of claim 30, wherein the target device performing a first target operation based on the AI-positioning performance information or the first information comprises:

and receiving the first information sent by the target network side equipment.

32. The method of claim 29, wherein, where the target device comprises the AI positioning model provider device, the method further comprises:

retraining the AI-positioning model;

the new AI-positioning model is reselected.

33. A positioning performance acquisition apparatus, comprising:

The first acquisition module is used for acquiring target positioning data corresponding to at least one first terminal, wherein the target positioning data comprises at least one of artificial intelligent AI positioning information and tag positioning information, wherein the AI positioning information is positioning information generated by reasoning based on an AI positioning model;

And the second acquisition module is used for acquiring the AI positioning performance information based on the target positioning data.

34. The utility model provides a report device of positioning data which characterized in that includes:

The first receiving module is used for receiving a first request message sent by target network side equipment, wherein the first request message comprises at least one of AI positioning information request indication used for indicating to report positioning information generated by reasoning based on an AI positioning model, tag positioning information request indication used for indicating to report tag positioning information, positioning deviation request indication used for indicating to report positioning deviation between the AI positioning information and the tag positioning information, positioning performance monitoring indication used for indicating to-be-monitored AI positioning performance information of the target network side equipment, and type information of positioning information used for indicating whether the AI positioning information or the specific type of the tag positioning information to be reported is positioning result data or positioning intermediate data;

The sending module is used for sending a first response message to the target network side equipment, wherein the first response message comprises at least one of AI positioning information, tag positioning information and positioning information deviation between the AI positioning information and the tag positioning information, and the AI positioning information is positioning information generated by the first terminal based on an AI positioning model in an inference mode.

35. A positioning performance acquisition apparatus, comprising:

The second receiving module is used for receiving the AI positioning performance information or the first information corresponding to the AI positioning model sent by the target network side equipment, wherein the first information is used for indicating the performance reduction or insufficiency of the positioning method based on the AI;

And the third acquisition module is used for acquiring the AI positioning performance information or the first information.

36. A terminal comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, performs the steps of the method of reporting positioning data as claimed in any one of claims 15 to 23, or the steps of the method of acquiring positioning performance as claimed in any one of claims 24 to 32.

37. A network side device comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the method of obtaining positioning performance according to any one of claims 1 to 14, or the steps of the method of obtaining positioning performance according to any one of claims 24 to 32.

38. A readable storage medium, wherein a program or an instruction is stored on the readable storage medium, the program or the instruction realizing the steps of the positioning performance acquisition method according to any one of claims 1 to 14, the steps of the positioning data reporting method according to any one of claims 15 to 23, or the steps of the positioning performance acquisition method according to any one of claims 24 to 32 when executed by a processor.