Positioning performance acquisition method, terminal and network side equipment

Abstract

The invention discloses a positioning performance obtaining method, a terminal and network side equipment, and belongs to the technical field of wireless communication, and the positioning performance obtaining method comprises the steps that target network side equipment obtains target positioning data corresponding to at least one first terminal, the target positioning data comprises at least one of AI positioning information and label positioning information, and the AI positioning information is positioning information generated by reasoning based on an AI positioning model; the positioning information deviation is used for indicating the deviation between the AI positioning information and the label positioning information; and the target network side equipment acquires AI positioning performance information based on the target positioning data.

Description

Technical Field

[0001] The present application belongs to the field of wireless communication technology, and specifically relates to a method for acquiring positioning performance, a terminal, and a network-side device. Background Art

[0002] Artificial Intelligence (AI) positioning models (or also called Machine Learning (ML) positioning models) can directly or assistedly perform positioning through reasoning, thereby saving a lot of measurement resources and reducing computing power consumption.

[0003] In the related art, the AI ​​positioning model can be set in the terminal for model reasoning to obtain AI positioning information, which can be positioning result data, or the AI ​​positioning information can be positioning intermediate data, and the positioning intermediate data is reported to the positioning control function network element, for example, the location management function (Location Management Function, LMF), and the positioning control function network element can perform position estimation based on the received positioning intermediate data to obtain the terminal's location information. In addition, the AI ​​positioning model can also be set in the LMF for model reasoning to obtain AI positioning information.

[0004] However, in actual applications, the performance of the AI ​​positioning model is unknown, which makes it impossible to determine the accuracy and precision of the AI ​​positioning information inferred by the AI ​​positioning model, and thus it is impossible to know whether the acquired location information is accurate. Summary of the invention

[0005] The embodiments of the present application provide a method for obtaining positioning performance, a terminal, and a network-side device, which can solve the problem that the accuracy and precision of AI positioning information obtained by using the AI ​​positioning model inference cannot be determined due to the unknown performance of the AI ​​positioning model.

[0006] In a first aspect, a method for acquiring positioning performance is provided, including: a target network side device acquires 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 is used to indicate the deviation between the AI ​​positioning information and the tag positioning information; the target network side device acquires AI positioning performance information based on the target positioning data.

[0007] In a second aspect, a method for reporting positioning data is provided, including: a first terminal receives a first request message sent by a target network side device, the first request message including at least one of the following information: an AI positioning information request indication, used to indicate reporting positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate reporting tag positioning information; a positioning deviation request indication, used to indicate reporting a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; positioning information type information, used to indicate 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; the first terminal sends a first response message to the target network side device, the first response message including at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is positioning information generated by the first terminal through reasoning based on the AI ​​positioning model.

[0008] According to a third aspect, a method for acquiring positioning performance is provided, including: a target device acquires AI positioning performance information or first information corresponding to an AI positioning model sent by a target network side device, wherein the first information is used to indicate that the performance of an AI-based positioning method has degraded or is insufficient.

[0009] In a fourth aspect, a device for acquiring positioning performance is provided, comprising: a first acquisition module, used to acquire target positioning data corresponding to at least one first terminal, wherein the target positioning data comprises at least one of the following: artificial intelligence (AI) positioning information and label 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 label positioning information; a second acquisition module, used to acquire AI positioning performance information based on the target positioning data.

[0010] In a fifth aspect, a positioning data reporting device is provided, comprising: a first receiving module, used to receive a first request message sent by a target network side device, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate the reporting of positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate the reporting of tag positioning information; a positioning deviation request indication, used to indicate the reporting of the positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; positioning information type information, used to indicate 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; a sending module, used to send a first response message to the target network side device, wherein the first response message includes at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is the positioning information generated by the first terminal through reasoning based on the AI ​​positioning model.

[0011] In a sixth aspect, a device for acquiring positioning performance is provided, including: a second receiving module, used to receive AI positioning performance information or first information corresponding to the AI ​​positioning model sent by a target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient; a third acquisition module, used to acquire the AI ​​positioning performance information or the first information.

[0012] In the seventh aspect, a terminal is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented, or the steps of the method described in the third aspect are implemented.

[0013] In an eighth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the processor is used to implement the steps of the method described in the second aspect, or to implement the steps of the method described in the third aspect, and the communication interface is used to couple with the processor.

[0014] In the ninth aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the third aspect are implemented.

[0015] In the tenth aspect, a network side device is provided, comprising a processor and a communication interface, wherein the processor is used to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the third aspect, and the communication interface is used to couple with the processor.

[0016] In the eleventh aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented, or the steps of the method described in the third aspect are implemented.

[0017] In the twelfth aspect, a communication system is provided, including: a terminal, a network side device and a target device, wherein the terminal can be used to execute the steps of the method described in the second aspect, the network side device can be used to execute the steps of the method described in the first aspect, and the target device can be used to execute the steps of the method described in the third aspect.

[0018] In the thirteenth aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instructions to implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect, or the steps of the method described in the third aspect.

[0019] In the fourteenth aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a storage medium, and the program / program product is executed by at least one processor to implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect, or the steps of the method described in the third aspect.

[0020] In an embodiment of the present application, the 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. Thus, the target network side device can obtain AI positioning performance information based on the target positioning data reported by at least one first terminal, solving the problem that the accuracy and precision of the AI ​​positioning information obtained by reasoning using the AI ​​positioning model cannot be determined due to the unknown performance of the AI ​​positioning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A block diagram showing a wireless communication system to which the embodiments of the present application can be applied;

[0022] Figure 2a A schematic diagram of a layout scenario of an AI positioning model in an embodiment of the present application is shown;

[0023] Figure 2b Another schematic diagram of the layout scenario of the AI ​​positioning model in the embodiment of the present application is shown;

[0024] Figure 2c A schematic diagram showing another layout scenario of the AI ​​positioning model in an embodiment of the present application is shown;

[0025] Figure 2d A schematic diagram showing another layout scenario of the AI ​​positioning model in an embodiment of the present application is shown;

[0026] Figure 3 A schematic diagram showing a flow chart of a method for acquiring positioning performance provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram showing a flow chart of a method for reporting positioning data provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram showing a flow chart of a method for acquiring positioning performance provided in an embodiment of the present application;

[0029] Figure 6 Another schematic diagram showing a flow chart of a method for acquiring positioning performance provided in an embodiment of the present application;

[0030] Figure 7 A schematic diagram showing another flow chart of a method for acquiring positioning performance provided in an embodiment of the present application;

[0031] Figure 8 A schematic diagram showing another flow chart of a method for acquiring positioning performance provided in an embodiment of the present application;

[0032] Fig. 9 A schematic diagram showing a structure of a device for acquiring positioning performance provided in an embodiment of the present application;

[0033] Fig.10 A schematic diagram showing a structure of a device for reporting positioning data provided in an embodiment of the present application;

[0034] Fig.11 A schematic diagram showing a structure of a device for acquiring positioning performance provided in an embodiment of the present application;

[0035] Fig.12 A schematic diagram showing the structure of a communication device provided in an embodiment of the present application is shown;

[0036] Fig.13A schematic diagram showing the hardware structure of a terminal provided in an embodiment of the present application is shown;

[0037] Fig.14 A schematic diagram of the hardware structure of a network side device provided in an embodiment of the present application is shown;

[0038] Fig.15 A schematic diagram of the hardware structure of a network side device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of this application.

[0040] The terms "first", "second", etc. of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of one type, and the number of objects is not limited, for example, the first object can be one or more. In addition, "or" in the present application represents at least one of the connected objects. For example, "A or B" covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B. The character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0041] The term "indication" in this application can be a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, operations to be performed, or request results in the sent indication; an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.

[0042] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the described technology can be used for the systems and radio technologies mentioned above as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following descriptions, but these technologies can also be applied to systems other than NR systems, such as the 6th generation (6 th Generation, 6G) communication system.

[0043] Figure 1A block diagram of a wireless communication system applicable to an embodiment of the present application is shown. The wireless communication system includes a terminal 11 and a network side device 12. Among them, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (Augmented Reality, AR), a virtual reality (Virtual Reality, VR) device, a robot, a wearable device (Wearable Device), an aircraft (flight vehicle), a vehicle-mounted device (Vehicle User Equipment, VUE), a ship-mounted device, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (Personal Computer, PC), a teller machine or a self-service machine and other terminal side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be referred to as a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AS) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.Among them, the base station may be referred to as a Node B (NB), an evolved Node B (eNB), a next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a radio base station, a radio transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a Home Node B (HNB), a Home Evolved Node B, a Transmission Reception Point (TRP) or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0044] The core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home user server (Home Subscriber Server, HSS), centralized network configuration (CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), binding support function (Binding Support Function, BSF), application function (Application Function, AF), Location Management Function (LMF), Gateway Mobile Location Center (GMLC), Network Data Analytics Function (NWDAF), etc. It should be noted that in the embodiments of the present application, only the core network equipment in the NR system is introduced as an example, and the specific type of the core network equipment is not limited.

[0045] In related technologies, such as Figure 2a and Figure 2bAs shown, the AI ​​positioning model is set in the terminal, and the UE can measure the positioning reference signal (PRS) sent by the base station, and then obtain the AI ​​positioning information through inference through the AI ​​positioning model based on the measurement information, where Figure 2a The AI ​​positioning information shown can be positioning result data, or, as Figure 2b As shown, the AI ​​positioning information may be intermediate positioning data (for example, measurement information based on PRS), and the terminal feeds back the inferred AI positioning information to the LMF. When the AI ​​positioning information is intermediate positioning data, the LMF may perform position calculation based on the intermediate positioning data. Figure 2c and Figure 2d As shown, the AI ​​positioning model is set in LMF, such as Figure 2c As shown, the UE reports the measurement information based on PRS to the LMF, and the LMF inputs the measurement information reported by the UE into the AI ​​positioning model to obtain the AI ​​positioning result, or, as shown Figure 2d As shown, the base station reports the SRS-based measurement information to the LMF, and the LMF inputs the measurement information reported by the base station into the AI ​​positioning model to obtain the AI ​​positioning result.

[0046] However, the model accuracy of the AI ​​positioning model in actual use may not be high, and the positioning accuracy may also be poor, and the performance of the AI ​​positioning model is unknown, which makes it impossible to determine the accuracy and precision of the AI ​​positioning information obtained by using the AI ​​positioning model to infer, and thus it is impossible to know whether the obtained positioning results are accurate.

[0047] To address this problem, the embodiments of the present application provide a method for obtaining positioning performance, a terminal, and a network-side device, which can monitor information such as positioning accuracy and model accuracy of the AI ​​positioning model, and obtain AI positioning performance information of the AI ​​positioning model.

[0048] The technical solution provided by the embodiments of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

[0049] Figure 3 A flow chart of the transmission method in the embodiment of the present application is shown, and the method 30 can be executed by the target network side device. In other words, the method can be executed by software or hardware installed on the target network side device. In the embodiment of the present application, the target network side device may include but is not limited to a positioning control function network element, such as a location management function (LMF), AMF, GMLC, NWDAF, etc.

[0050] like Figure 3 As shown, the method may include the following steps.

[0051] S310: The target network side device obtains target positioning data corresponding to at least one first terminal.

[0052] In an embodiment of the present application, the target positioning data includes at least one of the following: (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; (2) positioning information deviation, which is used to indicate the deviation between the AI ​​positioning information and the tag positioning information.

[0053] In an embodiment of the present application, optionally, the tag positioning information may include positioning information of the at least one first terminal obtained by a non-AI positioning method. For example, the tag positioning information may be the real positioning information of the first terminal. Among them, the non-AI positioning method includes but is not limited to the existing traditional positioning method, such as the positioning method described in 3GPP protocol 23.273.

[0054] In the first implementation manner, when the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device can obtain the AI ​​positioning information and the tag positioning information from the at least one first terminal, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information obtained by the at least one first terminal based on a non-AI positioning method.

[0055] For example, the target network side device can send a first request message to the at least one first terminal, and the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate the reporting of positioning information generated by reasoning based on the AI ​​positioning model; a tag positioning information request indication, used to indicate the reporting of tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; positioning information type information, used to indicate 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; after receiving the above-mentioned first request message, at least one first terminal can send a first response message to the target network side device, and the target network side device receives a first response message from the at least one first terminal, and the first response message includes the AI ​​positioning information and the tag positioning information.

[0056] Among them, in the case where 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, which are positioning information of the type corresponding to the type information of the positioning information. For example, in the case where the type information of the positioning information indicates that the type is positioning result data, the AI ​​positioning information carried in the first response message is AI positioning result data (for example, the position data of the first terminal inferred by the AI ​​positioning model), and the tag positioning information is real positioning result data (for example, the real position data of the first terminal). In the case where the type information of the positioning information indicates that the type is positioning intermediate data, the AI ​​positioning information carried in the first response message is AI positioning intermediate data (for example, PRS measurement data of the first terminal inferred by the AI ​​positioning model), and the tag positioning information is real positioning intermediate data (for example, PRS measurement data measured by the first terminal).

[0057] Optionally, the first response message may further include at least one of the following information:

[0058] (1) First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model; through the first indication information, the target network side device can obtain the AI ​​positioning information included in the first response message.

[0059] (2) second indication information, used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method; through the second indication information, the target network side device can obtain the tag positioning information included in the first response.

[0060] (3) Model identification information of the AI ​​positioning model;

[0061] (4) Function identification information corresponding to the AI ​​positioning model;

[0062] (5) feature group identification information corresponding to the AI ​​positioning model;

[0063] Through any one of the above (3)-(5), the target network side device can obtain the AI ​​positioning model corresponding to the AI ​​positioning information carried in the first response message.

[0064] (6) identification information of the first terminal;

[0065] (7) a location service (LCS) association identifier corresponding to the first terminal;

[0066] Through any one of the above (6) and (7), the target network side device can obtain the terminal corresponding to the AI ​​positioning information and the identification positioning information.

[0067] (8) Time information, used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

[0068] Through the time information, the target network side device can obtain the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

[0069] In a second implementation, when the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device can obtain the AI ​​positioning information from the at least one first terminal, and the target network side device obtains the tag positioning information from the access network device, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information for the at least one first terminal obtained by the access network device based on a non-AI positioning method.

[0070] For example, the target network side device may send a first request message to at least one first terminal. Different from the first implementation method mentioned above, the first request message may not carry the above-mentioned tag location information request indication, and the target network side device receives a first response message sent by at least one first terminal. Different from the first implementation method mentioned above, the first response message may not carry the above-mentioned tag location information and the second indication information. In addition, the target network side device may send a second request message to the access network device, and the second request message includes: identification information or LCS associated identification information of the at least one first terminal; after receiving the second request message, the access network device may send a second response message to the target network side device, and the target network side device receives a second response message from the access network device, and the second response message includes: tag location information of the at least one first terminal. The target network side device generates the positioning information deviation based on the acquired AI positioning information and the tag positioning information.

[0071] Optionally, the second response message may also carry time information to indicate the acquisition time corresponding to the tag location information. The target network side device may associate the AI ​​location information and the 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.

[0072] In the above implementation, optionally, the tag positioning information may include at least one of the following: positioning result data or positioning intermediate data.

[0073] In the above implementation, optionally, the intermediate positioning data includes at least one of the following: PRS measurement data and positioning sounding reference signal (Sounding Reference Signal, SRS) measurement data.

[0074] In the above implementation, when the tag positioning information includes the positioning intermediate data, and the AI ​​positioning information obtained by the target network side device from the first terminal is AI positioning result data, the target network side device can perform position estimation based on the positioning intermediate data to obtain the true location information of the at least one terminal, thereby generating the positioning information deviation.

[0075] In a third implementation manner, when the target positioning data includes the positioning information deviation, the target network side device may obtain the positioning information deviation from the at least one first terminal.

[0076] For example, the target network side device can send a first request message to at least one first terminal. Different from the first implementation method, the first request message also carries a positioning deviation request indication, which is used to indicate the reporting of the 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 the type information of the positioning information; the first response message received from the first terminal carries the 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.

[0077] S312: The target network side device obtains AI positioning performance information based on the target positioning data.

[0078] In one implementation, when the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device can use the AI ​​positioning information and the tag positioning information corresponding to each of the first terminals at the associated time as input information to calculate the AI ​​positioning performance information; for example, the target network side device can compare the AI ​​positioning information and the tag positioning information of the same first terminal, and 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, etc.), and obtain the AI ​​positioning performance information according to the calculation results.

[0079] In one implementation, when the target positioning data includes a positioning information deviation value, the target network side device uses the positioning information deviation value corresponding to each of the first terminals as input information to calculate the AI ​​positioning performance information. For example, the target network side device can calculate the positioning information deviation value corresponding to each first terminal based on a preset built-in algorithm (e.g., mean square error (MSE) algorithm, mean absolute error (MAE), accuracy (correctness), precision (precision), recall (Recall)), etc., and obtain the AI ​​positioning performance based on the calculation result.

[0080] In one implementation, the method may further include: the target network side device sends the AI ​​positioning performance information to the target device, wherein the target device includes at least one of the following: the provider device of the AI ​​positioning model, the first terminal, the second terminal, and the positioning control function network element. Through this implementation, the target network side device can send the AI ​​positioning performance to at least one of the provider device of the AI ​​positioning model, the first terminal, the second terminal, and the positioning control function network element, so that at least one of the provider device of the AI ​​positioning model, the first terminal, the second terminal, and the positioning control function network element can obtain the AI ​​positioning performance information of the AI ​​positioning model.

[0081] In the above implementation, optionally, the method may further include: the target network side device receives a request message from the target device requesting or subscribing to the AI ​​positioning performance information, wherein the request message includes at least one of the following information:

[0082] Model identification information of the AI ​​positioning model;

[0083] Function identification information corresponding to the AI ​​positioning model;

[0084] Feature group identification information corresponding to the AI ​​positioning model;

[0085] The first performance threshold value; the first performance threshold value can be used to instruct the target network side device to report the AI ​​positioning performance information to the target device when the AI ​​positioning performance information reaches the first performance threshold value.

[0086] Positioning performance metric type (metric); used to indicate the category of positioning performance that needs to be monitored, such as correctness, precision, recall, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), etc.

[0087] Terminal identification information, used to indicate a first terminal using the AI ​​positioning model;

[0088] The area information is used to indicate the use scope of the AI ​​positioning model; in S310, the target network side device can obtain the target positioning data of at least one first terminal corresponding to the area information.

[0089] The time period information is used to indicate the time period for positioning performance monitoring.

[0090] In the above implementation, when the target network side device receives a request message from the target device requesting 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 a first performance threshold, when the positioning performance value indicated by the acquired AI positioning performance information reaches the first performance threshold, the AI ​​positioning performance information is sent to the target device, and thereafter, the AI ​​positioning performance information may no longer be sent to the target device. Alternatively, the target network side device may also send AI positioning performance information to the target device multiple times. For example, when the request message includes a first performance threshold and time period information, the target network side device may continuously monitor the AI ​​positioning performance information of the AI ​​positioning module within the time period indicated by the time period information, and send 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.

[0091] In the above implementation, when the target network side device receives the request message for the target device to subscribe to the AI ​​positioning performance information, the target network side device can send the AI ​​positioning performance information to the target device multiple times. For example, the target network side device can periodically obtain the AI ​​positioning performance information. When the obtained AI positioning performance information changes relative to the last obtained AI positioning performance information, the target network side device sends the current AI positioning performance information to the target device. Alternatively, the target network side device can continuously monitor the AI ​​positioning performance information of the AI ​​positioning module within 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.

[0092] In one or more of the above implementations, the AI ​​positioning performance information acquired by the target network side device may include at least one of the following:

[0093] Positioning performance metric type: used to indicate the metric type of the AI ​​positioning performance information obtained, such as correctness, precision, recall, mean square error (MSE), mean absolute error (MAE), or root mean square error (RMSE);

[0094] Positioning performance value; used to indicate the specific value of the obtained AI positioning performance information.

[0095] In one implementation, after S212, the method may further include: when the positioning performance value indicated by the AI ​​positioning performance information is lower than the second performance threshold, the target network side device sends a first message to the target device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient, and the target device includes at least one of the following: a provider device of the AI ​​positioning model, a first terminal, a second terminal, and a positioning control function network element. Optionally, the second performance threshold may be the same as or different from the above-mentioned first performance threshold, for example, the second performance threshold may be greater than the first performance threshold. In this implementation, 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 can be informed that the performance of the AI-based positioning method is degraded or insufficient, and then the AI ​​positioning model can be adjusted or the positioning method can be switched to improve the accuracy of positioning.

[0096] Through the above technical solution provided by the embodiment of the present application, the target network side device obtains the 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 the positioning information generated by reasoning based on the 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. Thus, the target network side device can obtain AI positioning performance information based on the target positioning data reported by at least one first terminal, solving the problem that the accuracy and precision of the AI ​​positioning information obtained by reasoning using the AI ​​positioning model cannot be determined due to the unknown performance of the AI ​​positioning model.

[0097] Based on the same technical concept, an embodiment of the present application also provides a method for reporting positioning data.

[0098] Figure 4 A flow chart of a method for reporting positioning data provided in an embodiment of the present application is shown. The method 400 can be executed by the first terminal. Figure 4As shown, the method mainly includes the following steps.

[0099] S410: A first terminal receives a first request message sent by a target network side device.

[0100] The first request message includes at least one of the following information:

[0101] (1) AI positioning information request indication, used to indicate the reporting of positioning information generated by reasoning based on the AI ​​positioning model;

[0102] (2) a tag location information request indication, used to indicate reporting of tag location information;

[0103] (3) a positioning deviation request indication, used to indicate the reporting of the positioning deviation between the AI ​​positioning information and the tag positioning information;

[0104] (4) Positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; through this indication, the first terminal can learn that the target network side device needs to perform positioning performance monitoring on the AI ​​positioning module, so as to determine at least one of the following: AI positioning information, AI positioning information and tag positioning information, and positioning information deviation between the AI ​​positioning information and the tag positioning information.

[0105] (5) Type information of positioning information, used to indicate 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 this type information, the first terminal can know which type of AI positioning information or tag positioning information needs to be reported.

[0106] The above-mentioned first request message is the same as the first request message in method 300 , and details may refer to the relevant description in method 300 .

[0107] In one implementation, before executing the subsequent S412, the method may further include at least one of the following steps:

[0108] Step 1: The first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0109] Step 2: The first terminal obtains the tag positioning information based on a non-AI positioning method;

[0110] Step 3: The first terminal obtains the positioning information deviation according to the AI ​​positioning information and the tag positioning information.

[0111] In one implementation, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information, including one of the following:

[0112] In a case where the first request message includes the AI ​​positioning information request indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0113] In a case where the first request message includes the positioning deviation request indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0114] When the first request message includes the positioning performance monitoring indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information.

[0115] For example, when the first request message carries an AI positioning information request indication, the first terminal can generate AI positioning information based on the AI ​​positioning model through reasoning. When the first request message carries a tag positioning information request indication, the first terminal can perform positioning measurement to obtain tag positioning information. When the first request message carries a positioning deviation request indication, the first terminal can generate AI positioning information based on the AI ​​positioning model through reasoning, perform positioning measurement, obtain tag positioning information, and then compare the AI ​​positioning information generated by reasoning based on the AI ​​positioning model and the tag positioning information obtained by positioning measurement, and generate positioning deviation information between the AI ​​positioning information and the tag positioning information.

[0116] In one or more of the above implementations, before the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information, the method further includes: the first terminal obtains the AI ​​positioning model. For example, the first terminal can train and obtain the AI ​​positioning model by itself, or the first terminal obtains the AI ​​positioning model from a network data analysis function (Network Data Analytics Function, NWDAF), or the first terminal obtains the AI ​​positioning model from a positioning control function network element training, or the first terminal obtains the AI ​​positioning model from an OTT server.

[0117] S412: The first terminal sends a first response message to the target network side device.

[0118] The first response message includes at least one of the following information:

[0119] (1) AI positioning information: The AI ​​positioning information is positioning information generated by the first terminal based on the AI ​​positioning model.

[0120] (2) Tag positioning information; the tag positioning information includes positioning information of the first terminal obtained by a non-AI positioning method.

[0121] (3) Positioning information deviation between the AI ​​positioning information and the tag positioning information;

[0122] The first response message is the same as the first response message in method 300 , and details may refer to the relevant description in method 300 .

[0123] Optionally, the first response message further includes at least one of the following information:

[0124] (1) First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model; through the first indication information, the target network side device can obtain the AI ​​positioning information included in the first response message.

[0125] (2) second indication information, used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method; through the second indication information, the target network side device can obtain the tag positioning information included in the first response.

[0126] (3) Model identification information of the AI ​​positioning model;

[0127] (4) Function identification information corresponding to the AI ​​positioning model;

[0128] (5) feature group identification information corresponding to the AI ​​positioning model;

[0129] Through any one of the above (3)-(5), the target network side device can obtain the AI ​​positioning model corresponding to the AI ​​positioning information carried in the first response message.

[0130] (6) identification information of the first terminal;

[0131] (7) an LCS association identifier corresponding to the first terminal;

[0132] Through any one of the above (6) and (7), the target network side device can obtain the terminal corresponding to the AI ​​positioning information and the identification positioning information.

[0133] (8) Time information, used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

[0134] In one implementation, S412 may include: when 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. Through this implementation, the first terminal can determine and report the AI ​​positioning information and the tag positioning information based on the positioning performance monitoring indication in the first request message.

[0135] In another implementation, S412 may include: when the first request message includes the positioning performance monitoring indication, the first terminal determines to report the 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. Through this implementation, the first terminal can determine and report the 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.

[0136] In another implementation, S412 may include: when 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. Through this implementation, the first terminal can determine and report the AI ​​positioning information based on the positioning performance monitoring indication in the first request message.

[0137] In one or more of the above implementations, the type information of the AI ​​positioning information and the tag positioning information includes at least one of the following: positioning result data or positioning intermediate data. The positioning intermediate data may include at least one of the following: PRS measurement data and SRS measurement data.

[0138] Based on the same technical concept, an embodiment of the present application also provides a method for acquiring positioning performance.

[0139] Figure 5 A flow chart of a method for acquiring positioning performance provided in an embodiment of the present application is shown. The method 500 can be executed by the above-mentioned target device, such as Figure 5 As shown, the method mainly includes the following steps.

[0140] S510, the target device obtains AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient.

[0141] Optionally, as described in method 300, the first information may be sent by the target network side device to the target device when the positioning performance value indicated by the AI ​​positioning performance information is lower than a second performance threshold.

[0142] In one implementation, before S510, the method may further include: the target device sending a request message to the target network side device to request or subscribe to the AI ​​positioning performance information, wherein the request message includes at least one of the following information:

[0143] Model identification information of the AI ​​positioning model;

[0144] Function identification information corresponding to the AI ​​positioning model;

[0145] Feature group identification information corresponding to the AI ​​positioning model;

[0146] First performance threshold;

[0147] Positioning performance metric type;

[0148] Terminal identification information, used to indicate a first terminal using the AI ​​positioning model;

[0149] Area information, used to indicate the application scope of the AI ​​positioning model;

[0150] The time period information is used to indicate the time period for positioning performance monitoring.

[0151] The request message for requesting or subscribing to the AI ​​positioning performance information is the same as the request message for requesting or subscribing to the AI ​​positioning performance information in method 300. For details, please refer to the relevant description in method 300.

[0152] Based on the above implementation, optionally, the target device obtains AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, including:

[0153] The target device receives a response message sent by the target network side device, wherein the response message carries the AI ​​positioning performance information or the first information.

[0154] Optionally, the response message sent by the target network side device may also carry at least one of the following:

[0155] Model identification information of the AI ​​positioning model, for example, model ID;

[0156] Function identification information corresponding to the AI ​​positioning model (e.g., Functionality ID or feature group ID).

[0157] Through the above-mentioned model identification information or function identification information, the target device can obtain the received AI positioning performance information or the AI ​​positioning model corresponding to the first information.

[0158] Optionally, the AI ​​positioning performance information includes at least one of the following:

[0159] Positioning performance measurement type; used to indicate the measurement type corresponding to the positioning performance value indicated by the AI ​​positioning performance information;

[0160] Positioning performance value: used to indicate the specific value corresponding to the positioning performance indicated by the AI ​​positioning performance information. Optionally, the larger the positioning performance value, the worse the indicated positioning performance.

[0161] In an embodiment of the present application, the above-mentioned target device may include at least one of the following: 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 that uses the AI ​​positioning model for positioning reasoning, and the second terminal is a terminal that requests or subscribes to the AI ​​positioning performance information or the first information from the target device.

[0162] In one implementation, when the target device includes the first terminal, the second terminal, or the positioning control function network element, the method may further include:

[0163] 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 the following:

[0164] Switching from the AI ​​positioning method to the non-AI positioning method; for example, when the AI ​​positioning performance information indicates that the positioning performance is poor or the first information is received, the AI ​​positioning method may be switched to the non-AI positioning method;

[0165] Switch from a non-AI positioning method to an AI positioning method; for example, when the AI ​​positioning performance information indicates that the positioning performance is good, you can switch from the non-AI positioning method to the AI ​​positioning method.

[0166] Optionally, the target device performing a first target operation according to the AI ​​positioning performance information or the first information may include:

[0167] When it is determined that the target condition is met, the target device performs the first target operation, wherein the target condition includes at least one of the following:

[0168] The AI ​​performance value indicated by the AI ​​positioning performance information reaches a second performance threshold;

[0169] The first information sent by the target network side device is received.

[0170] In one implementation, when the target device includes the AI ​​positioning model provider device, the method may further include:

[0171] The target device performs a second target operation according to the AI ​​positioning performance information or the first information, where the second target operation includes at least one of the following:

[0172] Retraining the AI ​​positioning model;

[0173] Reselect a new AI positioning model.

[0174] For example, when the AI ​​positioning performance information indicates that the positioning performance is poor or the first information is received, the AI ​​positioning model provider device can retrain the AI ​​positioning model to improve the positioning performance of the AI ​​positioning model, or the AI ​​positioning model provider device can reselect a new AI positioning model, for example, select an AI positioning model with better positioning performance. Through this implementation, when 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, thereby improving the positioning performance.

[0175] Using the technical solution provided in the embodiment of the present application, Figure 2a In the scenario shown, on the Over The Top (OTT) server or network (NW) (for example, the Core Network (CN) or RAN) side, the UE performs model inference, and the inference result is the target location (i.e., AI positioning result data). The positioning performance monitoring can be LMF or model provider device, and the performance monitoring trigger condition can be one of the following: when a new model (model ID, UE, area of ​​interest (AOI)) is passed to the UE, the model provider device (such as OTT) triggers the LMF, the UE triggers the LMF when the new model is used, and the LMF monitors the new model by default. The data required for performance monitoring may include one of the following: target positioning information based on the AI ​​positioning model from the UE (i.e., the above-mentioned AI positioning information), the real positioning information of the UE from the UE or LMF (i.e., the above-mentioned tag positioning information), the target positioning information and the real positioning information Positioning information deviation (optional), model ID or function ID, UE ID (optional), timestamp (optional). Subsequent operations of performance monitoring may include at least one of the following: notifying the model provider device to retrain or reselect the AI ​​positioning model, notifying the UE to fallback (i.e., deactivate AI positioning based on the model and switch to traditional positioning methods).

[0176] Using the technical solution provided in the embodiment of the present application, Figure 2b In the scenario shown, on the OTT server or network (Network, NW) (e.g., Core Network, CN or RAN) side, the UE performs model inference, and the inference result is positioning measurement information (i.e., positioning intermediate data), and the positioning performance monitoring can be LMF or model provider device, and the performance monitoring trigger condition can be one of the following: when the new model (model ID, UE, AOI) is passed to the UE, the model provider device (e.g., OTT server) triggers LMF, when the new model is used, the UE triggers LMF, and the LMF monitors the new model by default. The data required for performance monitoring may include one of the following: model ID or function ID, UE ID (optional), timestamp (optional), solution one: LMF is based on the PRS measurement data (i.e., the above-mentioned AI positioning information) obtained by inference from the UE using the AI ​​positioning model and the real positioning information of the UE from the UE or LMF (i.e., the above-mentioned tag positioning information), solution two: PRS measurement data obtained by inference from the UE using the AI ​​positioning model and the real PRS measurement data from the UE, solution three: the difference between the PRS measurement data obtained by inference from the UE using the AI ​​positioning model and the real PRS measurement data from the UE. Subsequent operations of performance monitoring may include at least one of the following: notifying the model provider device to retrain or reselect the AI ​​positioning model, notifying the UE to fallback (i.e., disabling AI positioning based on the model and replacing it with a traditional positioning method).

[0177] In a scenario provided in an embodiment of the present application, a positioning model provider (such as an OTT server) or a positioning model reasoner (such as a UE) triggers an AI positioning performance detection request to a target network side device (such as an LMF), which specifies a UE IDlist (or AOI, area of ​​interest, AOI can be converted into a UE list on the network). The UE ID can be an identifier within the UE network, such as an International Mobile Subscriber Identity (IMSI), a Subscription Permanent Identifier (SUPI), etc., or it can be an external identifier of the UE network, such as a Generic Public Subscription Identifier (GPSI), etc. or other forms are not limited. The AOI can be an area identifier within the network, such as a cell ID, a Tracking Area Identity (TAI), etc., or it can be an area identifier outside the network, such as a geographic area identifier, an administrative area identifier, etc.

[0178] In this scenario, LMF can request the relevant UE to report two downlink positioning results, one is the AI ​​positioning result, and the other is the real (ground truth) positioning result (or called tag positioning result). Based on the reported data, LMF calculates the model performance. Figure 6 A flow chart of a method for obtaining positioning performance in this scenario is shown as follows: Figure 6 As shown, the method mainly includes the following steps:

[0179] Step 601a. ​​The positioning model provider (eg, OTT server or model training network element in CN, etc.) performs a model training process to obtain an AI positioning model for AI positioning.

[0180] The AI ​​positioning model is subsequently used by the UE in the AI ​​reasoning process to obtain positioning results.

[0181] Step 601b: The positioning model provider transmits the AI ​​positioning model to at least one UE. The method of model transmission is not limited in the embodiments of the present application.

[0182] Then, LMF triggers the positioning model performance monitoring.

[0183] The triggering method may include at least one of the following steps 602a to 602c:

[0184] Step 602a: The positioning model provider (eg, OTT server) triggers a positioning performance monitoring request message to the LMF.

[0185] The positioning performance monitoring request message includes at least one of the following information:

[0186] (1) Model ID: identification information of the positioning model

[0187] (2) functionality ID / feature group ID: functional description information / feature group information corresponding to the positioning model;

[0188] (3) Positioning performance metric: used to indicate the category of positioning performance to be monitored, such as correctness, precision, recall, MSE, MAE, RMSE, etc.

[0189] (4) First performance threshold (Threshold): a threshold used to indicate a performance value. When the value is higher or lower than the threshold, a corresponding operation needs to be performed, such as notifying the OTT server, UE, etc.

[0190] (5) UE ID(s): The user of the AI ​​positioning model or the UE involved (which UEs may use this positioning model); it can be an external UE network identifier such as GPSI, UE public network IP, user name in the APP, etc., or it can be an internal UE identifier such as IMSI, SUPI, UE private network IP, etc.

[0191] (6) Area information: The scope of application of the positioning model. It can be an area within the network such as cell ID, TA, etc.; it can also be an area outside the network such as a geographical area, administrative area, etc.

[0192] (7) Time period: the time period during which positioning performance monitoring is required.

[0193] It should be noted that this message may occur at any time after the OTT server positioning model training is completed, or after the OTT server transmits the model to the UE.

[0194] It should be noted that the message may be sent from the model provider (OTT server) to the LMF through the transfer or conversion of an intermediate network element, such as NEF, Gateway Mobile Location Centre (GMLC), AMF, etc.

[0195] Step 602b: UE (positioning model inference network element) triggers a positioning performance monitoring request message to LMF.

[0196] The positioning performance monitoring request message is the same as the positioning performance monitoring request message in step 602a, and please refer to the above related description for details.

[0197] It should be noted that this message may occur at any time after the UE obtains the positioning model, such as it may be triggered before or after the UE performs positioning inference based on the model.

[0198] It should be noted that the message may be sent from the UE to the LMF through the transfer or conversion of an intermediate network element, such as the RAN, AMF, etc.

[0199] Step 602c: LMF triggers performance monitoring of the positioning model based on its own decision. For example, LMF decides to trigger performance monitoring of the positioning model for all AI positioning processes or for the AI ​​positioning process corresponding to a specific model ID.

[0200] Step 603: LMF obtains at least one UE location request and executes a Mobile Original (MO) or Mobile Terminated (MT) location process. The location request comes from a Mobile Original (MO) or Mobile Terminated (MT) location requirement triggered by an LCS client or a location consumer.

[0201] This step is optional.

[0202] Specifically, the LCS client or positioning consumer can trigger a positioning request to the LMF through the GMLC and AMF. For positioning technology details, please refer to the 3GPP positioning related protocols.

[0203] In one implementation, the positioning request is based on the LTE Positioning Protocol (LPP) protocol stack, and is forwarded by the AMF using a downlink NAS message.

[0204] Step 604: LMF initiates a downlink positioning message to the UE to trigger the UE to perform a downlink positioning process.

[0205] Optionally, the downlink positioning message includes at least one of the following information:

[0206] (1) AI positioning information request indication: used to instruct the UE to report positioning information based on AI positioning model inference;

[0207] (2) Traditional positioning information request indication: used to instruct the UE to report traditional (i.e. non-AI method) positioning information (i.e. the tag positioning information mentioned above); the traditional positioning information is subsequently used as the true value (ground truth) of the positioning information or the positioning baseline value, and is compared with the AI ​​positioning information so that the LMF can calculate the positioning performance.

[0208] (3) Positioning deviation request indication: used to instruct the UE to report the deviation value between AI positioning information and traditional positioning information; the deviation value can be an absolute deviation value or a relative deviation value (such as a normalized deviation value), without restriction.

[0209] (4) Positioning performance monitoring indication: used to indicate to the UE that the LMF is to monitor and obtain AI positioning performance information. This indication information is used by the UE to decide what kind of positioning information to report, so that the LMF can subsequently obtain AI positioning performance based on the UE reported information.

[0210] The above positioning information includes: the calculated value of the UE for the positioning result (location), or the calculated value of the intermediate positioning measurement (such as PRS measurement). Therefore, the downlink positioning message can also further indicate the specific type of positioning information (location or specific measurement) required to be reported by the UE.

[0211] According to the downlink positioning message in step 604, the UE synchronously executes steps 605 and 606 to obtain AI positioning information and traditional positioning information.

[0212] Step 605: The UE performs downlink positioning measurement and performs reasoning based on the AI ​​positioning model to obtain AI positioning information.

[0213] Specifically, the UE performs measurements on the positioning signal according to the input parameter type required by the AI ​​positioning model to obtain the required measurement quantity. The obtained positioning signal measurement quantity is input into the AI ​​positioning model and performs the inference process to obtain an output result, which is the AI ​​positioning information.

[0214] Step 606: The UE performs downlink positioning measurement to obtain tag positioning information.

[0215] For comparison, the two types of positioning information in step 605 and step 606 may be positioning information of the UE at the same time point (or within a time difference range).

[0216] Step 607: The UE sends an uplink positioning message to the LMF, which includes at least one of the following information:

[0217] (1) First positioning information: corresponds to AI positioning information;

[0218] (2) AI indication: used to indicate that the first positioning information is positioning information based on AI model reasoning;

[0219] (3) Second positioning information: corresponds to traditional positioning information.

[0220] (4) Labeled data / ground truth indication: used to indicate that the second positioning information is traditional positioning information, or to indicate that the second positioning information is used as a positioning label value / true value / baseline value.

[0221] (5) Positioning information difference: the difference between AI positioning information and traditional positioning information.

[0222] (6) Model ID;

[0223] (7)Functionality ID / feature group ID;

[0224] (8)UE ID / LCS correlation ID: corresponds to a single UE;

[0225] (9) Time information: the timestamp or time period corresponding to the acquisition of the first positioning information, the second positioning information, and the positioning information difference.

[0226] Step 608: LMF calculates positioning model performance based on the positioning information reported by the UE.

[0227] Based on step 607, for the same model ID / Functionality ID / feature group ID, LMF obtains data reported by at least one UE (including the first and second positioning information, difference, etc. described in step 607), and LMF forms associated sample data with these data. For example, LMF forms a sample data group with AI positioning information and traditional positioning information obtained by the same UE at the same timestamp, or, for example, LMF forms a sample data with the positioning information difference reported by the same UE at the same timestamp.

[0228] Based on multiple sample data (groups), LMF calculates the performance of the positioning model. Specifically, it can include but is not limited to the following implementations:

[0229] Method 1: When the sample data set contains AI positioning measurement values ​​(such as PRS measurement) and traditional positioning measurement values, LMF directly compares the two measurement values ​​and obtains the calculation results (such as variance, MSE, MAE, etc.) based on a specific built-in algorithm. LMF uses the calculation results as the positioning performance value.

[0230] Method 2: When the sample data group contains AI positioning results (such as location result) and traditional positioning results, 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. LMF uses the calculation result as the positioning performance value.

[0231] Method 3: When the sample data group contains the deviation value between AI positioning information (location result or PRS measurement) and traditional positioning information, LMF uses the deviation value as input and obtains a calculation result (such as MAE, correctness, etc.) based on a specific built-in algorithm.

[0232] Step 609a: LMF sends a positioning performance response message to the OTT server.

[0233] Step 609b, LMF sends a positioning performance response message to the UEOTT server

[0234] The positioning performance response message in step 609a and step 609b may include at least one of the following:

[0235] -model ID

[0236] -Functionality ID / feature group ID

[0237] - Positioning performance metric

[0238] - Positioning performance value (obtained in step 608)

[0239] -Regional information: The scope of application of the positioning model. It can be an area within the network such as cell ID, TA, etc.; it can also be an area outside the network such as a geographical area, administrative area, etc.

[0240] -Time period: the time period during which positioning performance monitoring is required.

[0241] - Positioning performance degradation or below threshold indication information.

[0242] - Fallback recommendation indication: It is recommended that the OTT server or UE stop using the AI ​​positioning method.

[0243] In an optional manner, LMF may execute step 609a or step 609b after completing step 608;

[0244] In another optional manner, step 609a or step 609b is sent after the LMF determines that the positioning performance meets a condition. The condition is that the positioning performance value is lower than a certain performance threshold or higher than a certain performance threshold, which is specifically determined by the relationship between the positioning performance value and the positioning performance.

[0245] It should be noted that in steps 602a, 602b, 603, 604, 607, 609a and 609b, the AMF may need to convert the UEID(s) into an LCS correlation ID.

[0246] It should be noted that after LMF is triggered for performance monitoring, in one implementation, LMF can actively trigger UE positioning to collect performance data; or, in another implementation, LMF can also perform necessary performance data collection in the UE positioning process triggered by other consumers (such as LCS client, NWDAF or UE itself). In other words, LMF here does not actively trigger the UE positioning process due to performance monitoring.

[0247] In the above scenario, different from Figure 6 Here, LMF requests UE to report downlink positioning (AI positioning information), and LMF requests RAN to perform uplink positioning (as ground truth). LMF associates the uplink and downlink positioning results corresponding to the same UE (by assigning the same LCScorrelation ID) and the same timestamp to form statistical sample data. Based on the sample data, LMF calculates the model performance. Figure 7 A flow chart of a method for obtaining positioning performance in this scenario is shown as follows: Figure 7 As shown, the method mainly includes the following steps:

[0248] Steps 701-703 are the same as steps 601a-603.

[0249] In step 704, the LMF decides to simultaneously perform the downlink positioning of step 705 and the uplink positioning of step 706 for the UEs in the UE list or the LCS-related ID list.

[0250] Step 705: LMF initiates a downlink positioning process for the UE.

[0251] In step 705, the LMF may initiate a downlink positioning message to the UE. The UE performs inference based on the AI ​​positioning model to obtain the AI ​​positioning information. Then the UE sends a location reporting message to the LMF. The location reporting message may include AI positioning information, AI indication, Model ID / Functionality ID / feature group ID, UE ID / LCS correlation ID, timestamp and other information.

[0252] Step 706: LMF initiates an uplink positioning process to RAN.

[0253] In step 706, the LMF may send a request message to the RAN. After receiving the request message, the RAN sends a response message to the LMF. The response message may include the tag location information of the UE.

[0254] In step 707, LMF associates the AI ​​positioning information and tag positioning information corresponding to the same LCS related ID and the same timestamp to form sample data.

[0255] Step 708: For the same Model ID / Functionality ID / feature group ID, LMF calculates the positioning performance (e.g., accuracy) of the AI ​​positioning model based on the sample data.

[0256] Step 709 is the same as steps 609a and 609b.

[0257] It should be noted that in steps 702a, 702b, 703, 704, 707, 709a and 709b, the AMF may need to convert the UEID(s) into an LCS correlation ID.

[0258] It should be noted that after LMF is triggered for performance monitoring, LMF can actively trigger UE positioning to collect performance data; alternatively, LMF can also perform necessary performance data collection during the UE positioning process.

[0259] In the above scenario, in step 602a, the OTT server may not provide the UE ID, but only the model ID of the AI ​​positioning model, that is, the LMF does not know which UEs need to monitor their positioning accuracy. Figure 8 Another flow chart of a method for obtaining positioning performance is shown, Figure 8 As shown, the method mainly includes the following steps:

[0260] Step 801a. The positioning model provider (eg, OTT server or model training network element in CN, etc.) performs a model training process to obtain an AI positioning model for AI positioning.

[0261] The AI ​​positioning model is subsequently used by the UE in the AI ​​reasoning process to obtain positioning results.

[0262] Step 801b: The positioning model provider transmits the AI ​​positioning model to at least one UE. The method of model transmission is not limited in the embodiments of the present application.

[0263] Step 802: The positioning model provider (eg, OTT server) triggers a positioning performance monitoring request message to the LMF.

[0264] The positioning performance monitoring request message includes the following information:

[0265] (1)model ID functionality ID / feature group ID;

[0266] (2) Time period: the time period during which positioning performance monitoring is required;

[0267] (3)AF ID.

[0268] Step 803: LMF obtains at least one UE location request and executes the location process. The location request comes from a mobile original (MO) or mobile terminated (MT) location requirement triggered by an LCS client or a location consumer.

[0269] Step 804: UE reports its location to LMF, which may include AI positioning information, AI indication, model ID functionality ID / feature group ID.

[0270] Step 805: The LMF determines that the AI ​​positioning model used by the UE is the AI ​​positioning model to be monitored, and executes step 806;

[0271] Steps 806-811 are the same as steps 604-609a / 609b.

[0272] In this embodiment, LMF can only passively wait for UE to report the AI ​​positioning result once (indicating the corresponding model ID), and then use the above Figure 6 or Figure 7 The method described obtains positioning performance.

[0273] It should be noted that after the LMF is triggered for performance monitoring, it waits for the UE to report the positioning result (model ID appears), and the LMF collects necessary performance data in the subsequent UE positioning process.

[0274] In addition, it should be noted that in step 806 or 809, the LMF can request the UE to directly report the deviation value between the AI ​​positioning result and the traditional positioning result.

[0275] Through the above method provided in the embodiment of the present application, the positioning model performance of the AI ​​positioning model used by the UE can be monitored, and corresponding operations can be performed according to the monitoring results to improve the accuracy of positioning.

[0276] The method for acquiring positioning performance provided in the embodiment of the present application may be executed by a device for acquiring positioning performance. In the embodiment of the present application, the device for acquiring positioning performance performing the method for acquiring positioning performance is taken as an example to illustrate the device for acquiring positioning performance provided in the embodiment of the present application.

[0277] Fig. 9 A schematic diagram showing a structure of a device for acquiring positioning performance provided in an embodiment of the present application is shown as follows: Fig. 9 As shown, the device 900 includes: a first acquisition module 901 and a second acquisition module 902 .

[0278] In an embodiment of the present application, a first acquisition module 901 is used to acquire 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; a second acquisition module 902 is used to acquire AI positioning performance information based on the target positioning data.

[0279] In one implementation, the tag positioning information includes positioning information of the at least one first terminal acquired by a non-AI positioning method.

[0280] In one implementation, it may also include: a transmission module, used to send the AI ​​positioning performance information to a target device, wherein the target device includes at least one of the following: a provider device of the AI ​​positioning model, a first terminal, a second terminal, and a positioning control function network element.

[0281] In one implementation, the transmission module is further configured to receive a request message from the target device requesting or subscribing to the AI ​​positioning performance information, wherein the request message includes at least one of the following information:

[0282] Model identification information of the AI ​​positioning model;

[0283] Function identification information corresponding to the AI ​​positioning model;

[0284] Feature group identification information corresponding to the AI ​​positioning model;

[0285] First performance threshold;

[0286] Positioning performance metric type;

[0287] Terminal identification information, used to indicate a first terminal using the AI ​​positioning model;

[0288] Area information, used to indicate the application scope of the AI ​​positioning model;

[0289] The time period information is used to indicate the time period for positioning performance monitoring.

[0290] In one implementation, the AI ​​positioning performance information includes at least one of the following:

[0291] Positioning performance metric type;

[0292] Positioning performance value.

[0293] In one implementation, it also includes: a transmission module, which is used to send first information to a target device when the positioning performance value indicated by the AI ​​positioning performance information is lower than a second performance threshold, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient, and the target device includes at least one of the following: a provider device of the AI ​​positioning model, a first terminal, a second terminal, and a positioning control function network element.

[0294] In one implementation, obtaining AI positioning performance information based on the target positioning data includes one of the following:

[0295] In a case where the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device uses the AI ​​positioning information and the tag positioning information corresponding to each of the first terminals at the association time as input information to calculate the AI ​​positioning performance information;

[0296] In the case where the target positioning data includes a positioning information deviation value, the positioning information deviation value corresponding to each of the first terminals is used as input information to calculate the AI ​​positioning performance information.

[0297] In one implementation, obtaining the target positioning data corresponding to the at least one first terminal includes one of the following:

[0298] Acquire 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 through 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;

[0299] The AI ​​positioning information is obtained from the at least one first terminal, and the tag positioning information is obtained from the access network device, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information for the at least one first terminal obtained by the access network device based on a non-AI positioning method.

[0300] The AI ​​positioning information is obtained from the at least one first terminal, and the tag positioning information is obtained locally, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information for the at least one first terminal obtained by the target network side device based on a non-AI positioning method.

[0301] In one implementation, obtaining the target positioning data corresponding to the at least one first terminal includes one of the following:

[0302] Acquire the positioning information deviation from the at least one first terminal;

[0303] The positioning information deviation is generated based on the acquired AI positioning information and the tag positioning information.

[0304] In one implementation, obtaining target positioning data corresponding to at least one first terminal includes:

[0305] Sending a first request message to the at least one first terminal, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on the AI ​​positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; and positioning information type information, used to indicate 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;

[0306] Acquiring target positioning data corresponding to at least one first terminal includes:

[0307] A first response message is received from the at least one first terminal, wherein the first response message includes at least one of the following information: the AI ​​positioning information, the tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information.

[0308] In one implementation, the first response message further includes at least one of the following information:

[0309] First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model;

[0310] The second indication information is used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method;

[0311] Model identification information of the AI ​​positioning model;

[0312] Function identification information corresponding to the AI ​​positioning model;

[0313] Feature group identification information corresponding to the AI ​​positioning model;

[0314] identification information of the first terminal;

[0315] A location service LCS association identifier corresponding to the first terminal;

[0316] The time information is used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

[0317] In one implementation, obtaining the target positioning data corresponding to the at least one first terminal further includes:

[0318] Sending 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 association identification information;

[0319] A second response message is received from the access network device, where the second response message includes: label location information of the at least one first terminal.

[0320] In one implementation, the type information of the AI ​​positioning information and the tag positioning information includes at least one of the following: positioning result data or positioning intermediate data.

[0321] In one implementation, the intermediate positioning data includes at least one of the following: PRS measurement data and SRS measurement data.

[0322] The positioning performance acquisition device provided in the embodiment of the present application can achieve Figure 3The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0323] Fig.10 A schematic diagram showing a structure of a device for reporting positioning data provided in an embodiment of the present application is shown as follows: Fig.10 As shown, the device 1000 includes: a first receiving module 1001 and a sending module 1002 .

[0324] In an embodiment of the present application, a first receiving module 1001 is used to receive a first request message sent by a target network side device, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; positioning information type information, used to indicate 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; a sending module 1002 is used to send a first response message to the target network side device, wherein the first response message includes at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is positioning information generated by the first terminal through reasoning based on the AI ​​positioning model.

[0325] In one implementation, the first response message further includes at least one of the following information:

[0326] First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model;

[0327] The second indication information is used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method;

[0328] Model identification information of the AI ​​positioning model;

[0329] Function identification information corresponding to the AI ​​positioning model;

[0330] Feature group identification information corresponding to the AI ​​positioning model;

[0331] identification information of the first terminal;

[0332] A location service LCS association identifier corresponding to the first terminal;

[0333] The time information is used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

[0334] In one implementation, the tag positioning information includes positioning information of the first terminal acquired by a non-AI positioning method.

[0335] In one implementation, sending a first response message to the target network side device includes one of the following:

[0336] When the first request message includes the positioning performance monitoring indication, determining to report the AI ​​positioning information and the tag positioning information, and sending the first response message carrying the AI ​​positioning information and the tag positioning information to the target network side device;

[0337] When the first request message includes the positioning performance monitoring indication, determining to report a positioning information deviation between the AI ​​positioning information and the tag positioning information, and sending the first response message carrying the positioning information deviation to the target network side device;

[0338] When the first request message includes the positioning performance monitoring indication, it is determined to report the AI ​​positioning information, and the first response message carrying the AI ​​positioning information is sent to the target network side device.

[0339] In one implementation, the sending module 1002 is further configured to:

[0340] Performing reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0341] Acquire the tag positioning information based on a non-AI positioning method;

[0342] The positioning information deviation is obtained according to the AI ​​positioning information and the tag positioning information.

[0343] In one implementation, performing reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information includes one of the following:

[0344] When the first request message includes the AI ​​positioning information request indication, performing reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0345] In a case where the first request message includes the positioning deviation request indication, performing reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information;

[0346] In a case where the first request message includes the positioning performance monitoring indication, reasoning is performed based on the AI ​​positioning model to obtain the AI ​​positioning information.

[0347] In one implementation, the first receiving module 1001 is further used to obtain the AI ​​positioning model.

[0348] In one implementation, the type information of the AI ​​positioning information and the tag positioning information includes at least one of the following: positioning result data and positioning intermediate data.

[0349] In one implementation, the intermediate positioning data includes at least one of the following: PRS measurement data and SRS measurement data.

[0350] The positioning data reporting device provided in the embodiment of the present application can achieve Figure 4 The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0351] Fig.11 A schematic diagram showing a structure of a device for acquiring positioning performance provided in an embodiment of the present application is shown as follows: Fig.11 As shown, the device 1100 includes: a second receiving module 1101 and a third obtaining module 1102 .

[0352] In an embodiment of the present application, the second receiving module 1101 is used to receive AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient; the third acquisition module 1102 is used to obtain AI positioning performance information or first information.

[0353] The apparatus can be applied to the above-mentioned target device.

[0354] In one implementation, the method further includes: a sending module configured to send a request message for requesting or subscribing to the AI ​​positioning performance information to the target network side device, wherein the request message includes at least one of the following information:

[0355] Model identification information of the AI ​​positioning model;

[0356] Function identification information corresponding to the AI ​​positioning model;

[0357] Feature group identification information corresponding to the AI ​​positioning model;

[0358] First performance threshold;

[0359] Positioning performance metric type;

[0360] Terminal identification information, used to indicate a first terminal using the AI ​​positioning model;

[0361] Area information, used to indicate the application scope of the AI ​​positioning model;

[0362] The time period information is used to indicate the time period for positioning performance monitoring.

[0363] In one implementation, obtaining AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device includes:

[0364] Receive a response message sent by the target network side device, wherein the response message carries the AI ​​positioning performance information or the first information.

[0365] In one implementation, the response message also carries at least one of the following:

[0366] Model identification information of the AI ​​positioning model;

[0367] Function identification information corresponding to the AI ​​positioning model.

[0368] In one implementation, the AI ​​positioning performance information includes at least one of the following:

[0369] Positioning performance metric type;

[0370] Positioning performance value.

[0371] In one implementation, the target device includes at least one of the following: 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 that uses the AI ​​positioning model for positioning reasoning, and the second terminal is a terminal that requests or subscribes to the AI ​​positioning performance information or the first information from the target device.

[0372] In one implementation, when the target device includes the first terminal, the second terminal, or the positioning control function network element, it further includes: an execution module, configured to perform a first target operation according to the AI ​​positioning performance information or the first information, wherein the first target operation includes one of the following:

[0373] Switch from AI positioning method to non-AI positioning method;

[0374] Switch from non-AI positioning method to AI positioning method.

[0375] In one implementation, performing a first target operation according to the AI ​​positioning performance information or the first information includes:

[0376] If it is determined that the target condition is met, the first target operation is performed, wherein the target condition includes at least one of the following:

[0377] The AI ​​performance value indicated by the AI ​​positioning performance information reaches a second performance threshold;

[0378] The first information sent by the target network side device is received.

[0379] In one implementation, the method further includes: an execution module, configured to, when the target device includes the AI ​​positioning model provider device, perform a second target operation according to the AI ​​positioning performance information or the first information, wherein the second target operation includes at least one of the following:

[0380] Retraining the AI ​​positioning model;

[0381] Reselect a new AI positioning model.

[0382] The positioning performance acquisition device provided in the embodiment of the present application can achieve Figure 5 The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0383] like Fig.12 As shown, the embodiment of the present application also provides a communication device 1200, including a processor 1201 and a memory 1202, and the memory 1202 stores a program or instruction that can be run on the processor 1201. For example, when the communication device 1200 is a terminal, the program or instruction is executed by the processor 1201 to implement the various steps of the embodiment of the above-mentioned method for reporting positioning data 300, or to implement the various steps of the embodiment of the above-mentioned method for obtaining positioning performance 500, and can achieve the same technical effect. When the communication device 1200 is a network side device, the program or instruction is executed by the processor 1201 to implement the various steps of the embodiment of the above-mentioned method for obtaining positioning performance 300 or 500, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0384] The embodiment of the present application also provides a terminal, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the following Figure 4 The steps in the method embodiment shown. This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this terminal embodiment and can achieve the same technical effect. Specifically, Fig.13 A schematic diagram of the hardware structure of a terminal for implementing an embodiment of the present application.

[0385] The terminal 1300 includes but is not limited to: 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 at least some of the components of a processor 1310.

[0386] Those skilled in the art will appreciate that the terminal 1300 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 1310 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Fig.13 The terminal structure shown in the figure does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0387] It should be understood that in the embodiment of the present application, the input unit 1304 may include a graphics processing unit (GPU) 13041 and a microphone 13042, and the graphics processing unit 13041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture 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, etc. The user input unit 1307 includes a touch panel 13071 and at least one of other input devices 13072. The touch panel 13071 is also called 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 (such as a volume control button, a switch button, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0388] In the embodiment of the present application, after receiving downlink data from the network side device, the RF unit 1301 can transmit the data to the processor 1310 for processing; in addition, the RF unit 1301 can send uplink data to the network side device. Generally, the RF unit 1301 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0389] The memory 1309 can be used to store software programs or instructions and various data. The memory 1309 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1309 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1309 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0390] The processor 1310 may include one or more processing units; optionally, the processor 1310 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1310.

[0391] The radio frequency unit 1301 is used for:

[0392] Receive a first request message sent by a target network side device, where the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; and positioning information type information, used to indicate whether the specific type of the AI ​​positioning information or the tag positioning information to be reported is positioning result data or positioning intermediate data;

[0393] A first response message is sent to the target network side device, wherein the first response message includes at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is positioning information generated by the first terminal through inference based on the AI ​​positioning model.

[0394] Alternatively, the radio frequency unit 1301 is used to obtain the AI ​​positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient.

[0395] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of method embodiment 400, and achieve the same or corresponding technical effect. To avoid repetition, it will not be repeated here.

[0396] The embodiment of the present application also provides a network side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the following Figure 3 Or the steps of the method embodiment shown in 5. This network side device embodiment corresponds to the above network side device method embodiment, and each implementation process and implementation method of the above method embodiment can be applied to this network side device embodiment and can achieve the same technical effect.

[0397] Specifically, the embodiment of the present application also provides a network side device. Fig.14As shown, the network side device 1400 includes: an antenna 1401, a radio frequency device 1402, a baseband device 1403, a processor 1404 and a memory 1405. The antenna 1401 is connected to the radio frequency device 1402. In the uplink direction, the radio frequency device 1402 receives information through the antenna 1401 and sends the received information to the baseband device 1403 for processing. In the downlink direction, the baseband device 1403 processes the information to be sent and sends it to the radio frequency device 1402, and the radio frequency device 1402 processes the received information and sends it out through the antenna 1401.

[0398] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 1403, which includes a baseband processor.

[0399] The baseband device 1403 may include, for example, at least one baseband board on which a plurality of chips are arranged. Fig.14 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 1405 through a bus interface to call the program in the memory 1405 to execute the network device operations shown in the above method embodiment.

[0400] The network side device may further include a network interface 1406, which is, for example, a Common Public Radio Interface (CPRI).

[0401] Specifically, the network side device 1400 of the embodiment of the present application further includes: instructions or programs stored in the memory 1405 and executable on the processor 1404, and the processor 1404 calls the instructions or programs in the memory 1405 to execute Fig. 9 Or the method executed by each module shown in 11, and achieves the same technical effect, to avoid repetition, it will not be repeated here.

[0402] Specifically, the embodiment of the present application also provides a network side device. Fig.15 As shown, 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 (CPRI).

[0403] Specifically, the network side device 1500 of the embodiment of the present application further includes: instructions or programs stored in the memory 1503 and executable on the processor 1501, and the processor 1501 calls the instructions or programs in the memory 1503 to execute Fig. 9 Or the method executed by each module shown in 11, and achieves the same technical effect, to avoid repetition, it will not be repeated here.

[0404] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned method embodiments are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0405] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk. In some examples, the readable storage medium may be a non-transient readable storage medium.

[0406] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0407] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0408] The embodiments of the present application further provide a computer program / program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the above-mentioned method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0409] An embodiment of the present application also provides a communication system, including: a terminal, a network side device and a target device, wherein the terminal can be used to execute the steps of the above-mentioned method 400 embodiment, the network side device can be used to execute the steps of the above-mentioned method 300 embodiment, and the target device can be used to execute the steps of the above-mentioned method 500 embodiment.

[0410] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0411] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including several instructions to enable a terminal or a network-side device to execute the methods described in each embodiment of the present application.

[0412] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of the present application and the scope of protection of the claims, and these implementation methods are all within the protection of the present application.

Claims

1. A method for obtaining positioning performance, It is characterized in that include: The 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: artificial intelligence AI positioning information and tag positioning information, wherein the AI ​​positioning information is positioning information generated by reasoning based on the AI ​​positioning model; positioning information deviation, which is 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.

2. The method according to claim 1, It is characterized in that The tag positioning information includes positioning information of the at least one first terminal acquired by a non-AI positioning method.

3. The method according to claim 1 or 2, It is characterized in that The method further comprises: The target network side device sends the AI ​​positioning performance information to the target device, wherein the target device includes at least one of the following: a provider device of the AI ​​positioning model, a first terminal, a second terminal, and a positioning control function network element.

4. The method according to claim 3, It is characterized in that The method further comprises: The target network side device receives a request message from the target device requesting or subscribing 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; Feature group identification information corresponding to the AI ​​positioning model; First performance threshold; Positioning performance metric type; Terminal identification information, used to indicate a first terminal using the AI ​​positioning model; Area information, used to indicate the application scope of the AI ​​positioning model; The time period information is used to indicate the time period for positioning performance monitoring.

5. The method according to any one of claims 1 to 4, It is characterized in that The AI ​​positioning performance information includes at least one of the following: Positioning performance metric type; Positioning performance value.

6. The method according to any one of claims 1 to 5, It is characterized in that After the target network side device acquires AI positioning performance information based on the target positioning data, the method further includes: When the positioning performance value indicated by the AI ​​positioning performance information is lower than the second performance threshold, the target network side device sends first information to the target device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient, and the target device includes at least one of the following: a provider device of the AI ​​positioning model, a first terminal, a second terminal, and a positioning control function network element.

7. The method according to any one of claims 1 to 6, It is characterized in that The target network side device obtains AI positioning performance information based on the target positioning data, including one of the following: In a case where the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device uses the AI ​​positioning information and the tag positioning information corresponding to each of the first terminals at the association time as input information to calculate the AI ​​positioning performance information; In the case where the target positioning data includes a positioning information deviation value, the target network side device uses the positioning information deviation value corresponding to each of the first terminals as input information to calculate the AI ​​positioning performance information.

8. The method according to any one of claims 1 to 7, It is characterized in that In a case where the target positioning data includes the AI ​​positioning information and the tag positioning information, the target network side device acquiring the target positioning data corresponding to at least one first terminal includes one of the following: The target network side device obtains 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 through reasoning, and the tag positioning information is positioning information obtained by the at least one first terminal based on a non-AI positioning method; The target network side device obtains the AI ​​positioning information from the at least one first terminal, and the target network side device obtains the tag positioning information from the access network device, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information for the at least one first terminal obtained by the access network device based on a non-AI positioning method; The target network side device obtains the AI ​​positioning information from the at least one first terminal, and the target network side device obtains the tag positioning information locally, wherein the AI ​​positioning information is the positioning information generated by the at least one first terminal based on the AI ​​positioning model through reasoning, and the tag positioning information is the positioning information for the at least one first terminal obtained by the target network side device based on a non-AI positioning method.

9. The method according to any one of claims 1 to 8, It is characterized in that In a case where the target positioning data includes the positioning information deviation, the target network side device acquiring the target positioning data corresponding to at least one first terminal includes one of the following: The target network side device obtains the positioning information deviation from the at least one first terminal; The target network side device 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, It is characterized in that The target network side device obtains target positioning data corresponding to at least one first terminal, including: The target network side device sends a first request message to the at least one first terminal, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on the AI ​​positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; and positioning information type information, used to indicate 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; The target network side device obtains target positioning data corresponding to at least one first terminal, including: The target network side device receives a first response message from the at least one first terminal, and the first response message includes at least one of the following information: 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 according to claim 10, It is characterized in that The first response message also includes at least one of the following information: First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model; The second indication information is used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method; Model identification information of the AI ​​positioning model; Function identification information corresponding to the AI ​​positioning model; Feature group identification information corresponding to the AI ​​positioning model; identification information of the first terminal; A location service LCS association identifier corresponding to the first terminal; The time information is used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

12. The method according to any one of claims 8 to 11, It is characterized in that The target network side device acquiring the target positioning data corresponding to at least one first terminal further includes: The target network side device sends 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 association identification information; The target network side device receives a second response message from the access network device, where the second response message includes: label location information of the at least one first terminal.

13. The method according to any one of claims 1 to 12, It is characterized in that The type information of the AI ​​positioning information and the tag positioning information includes at least one of the following: positioning result data or positioning intermediate data.

14. The method according to claim 13, It is characterized in that The intermediate positioning data includes at least one of the following: positioning reference signal PRS measurement data and positioning sounding reference signal SRS measurement data.

15. A method for reporting positioning data, It is characterized in that include: The first terminal receives a first request message sent by a target network side device, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; and positioning information type information, used to indicate 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. The first terminal sends a first response message to the target network side device, and the first response message includes at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is positioning information generated by the first terminal through reasoning based on the AI ​​positioning model.

16. The method according to claim 15, It is characterized in that The first response message also includes at least one of the following information: First indication information, used to indicate that the AI ​​positioning information is positioning information inferred based on the AI ​​positioning model; The second indication information is used to indicate that the tag positioning information is real positioning information or positioning information obtained based on a non-AI positioning method; Model identification information of the AI ​​positioning model; Function identification information corresponding to the AI ​​positioning model; Feature group identification information corresponding to the AI ​​positioning model; identification information of the first terminal; A location service LCS association identifier corresponding to the first terminal; The time information is used to indicate the acquisition time corresponding to the AI ​​positioning information or the tag positioning information.

17. The method according to claim 15 or 16, It is characterized in that The tag positioning information includes positioning information of the first terminal obtained by a non-AI positioning method.

18. A method according to any one of claims 15 to 17, It is characterized in that The first terminal sends a first response message to the target network side device, including one of the following: When 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; When the first request message includes the positioning performance monitoring indication, the first terminal determines to report the 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; When 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.

19. The method according to any one of claims 15 to 18, It is characterized in that Before the first terminal sends a first response message to the target network side device, the method further includes at least one of the following: The first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information; The first terminal obtains the tag positioning information based on a non-AI positioning method; The first terminal obtains the positioning information deviation according to the AI ​​positioning information and the tag positioning information.

20. The method according to claim 19, It is characterized in that The first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information, including one of the following: In a case where the first request message includes the AI ​​positioning information request indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information; In a case where the first request message includes the positioning deviation request indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information; When the first request message includes the positioning performance monitoring indication, the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information.

21. The method according to claim 19 or 20, It is characterized in that Before the first terminal performs reasoning based on the AI ​​positioning model to obtain the AI ​​positioning information, the method further includes: The first terminal obtains the AI ​​positioning model.

22. A method according to any one of claims 15 to 21, It is characterized in that The type information of the AI ​​positioning information and the tag positioning information includes at least one of the following: positioning result data and positioning intermediate data.

23. The method according to claim 22, It is characterized in that The intermediate positioning data includes at least one of the following: positioning reference signal PRS measurement data and positioning sounding reference signal SRS measurement data.

24. A method for obtaining positioning performance, It is characterized in that include: The target device obtains AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient.

25. The method according to claim 24, It is characterized in that Before the target device obtains the AI ​​positioning performance information or the first information corresponding to the AI ​​positioning model sent by the target network side device, the method further includes: The target device sends a request message to the target network side device to request or subscribe 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; Feature group identification information corresponding to the AI ​​positioning model; First performance threshold; Positioning performance metric type; Terminal identification information, used to indicate a first terminal using the AI ​​positioning model; Area information, used to indicate the application scope of the AI ​​positioning model; The time period information is used to indicate the time period for positioning performance monitoring.

26. The method according to claim 25, It is characterized in that The target device obtains AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, including: The target device receives a response message sent by the target network side device, wherein the response message carries the AI ​​positioning performance information or the first information.

27. The method according to claim 26, It is characterized in that The response message also carries at least one of the following: Model identification information of the AI ​​positioning model; Function identification information corresponding to the AI ​​positioning model.

28. A method according to any one of claims 24 to 27, It is characterized in that The AI ​​positioning performance information includes at least one of the following: Positioning performance metric type; Positioning performance value.

29. The method according to any one of claims 24 to 28, It is characterized in that The target device includes at least one of the following: 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 that uses the AI ​​positioning model for positioning reasoning, and the second terminal is a terminal that requests or subscribes to the AI ​​positioning performance information or the first information from the target device.

30. The method according to claim 29, It is characterized in that In a case where the target device includes the first terminal, the second terminal, or the positioning control function network element, the method further includes: 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 the following: Switch from AI positioning method to non-AI positioning method; Switch from non-AI positioning method to AI positioning method.

31. The method according to claim 30, It is characterized in that The target device performs a first target operation according to the AI ​​positioning performance information or the first information, including: When it is determined that the target condition is met, the target device performs the first target operation, wherein the target condition includes at least one of the following: The AI ​​performance value indicated by the AI ​​positioning performance information reaches a second performance threshold; The first information sent by the target network side device is received.

32. The method according to claim 29, It is characterized in that In the case where the target device includes the AI ​​positioning model provider device, the method further includes: The target device performs a second target operation according to the AI ​​positioning performance information or the first information, where the second target operation includes at least one of the following: Retraining the AI ​​positioning model; Reselect a new AI positioning model.

33. A device for acquiring positioning performance, It is characterized in that include: A first acquisition module is used to acquire target positioning data corresponding to at least one first terminal, wherein the target positioning data includes at least one of the following: artificial intelligence 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 to indicate the deviation between the AI ​​positioning information and the tag positioning information; The second acquisition module is used to obtain AI positioning performance information based on the target positioning data.

34. A device for reporting positioning data, It is characterized in that include: A first receiving module is used to receive a first request message sent by a target network side device, wherein the first request message includes at least one of the following information: an AI positioning information request indication, used to indicate reporting of positioning information generated by reasoning based on an AI positioning model; a tag positioning information request indication, used to indicate reporting of tag positioning information; a positioning deviation request indication, used to indicate reporting of a positioning deviation between the AI ​​positioning information and the tag positioning information; a positioning performance monitoring indication, used to indicate the AI ​​positioning performance information to be monitored by the target network side device; and positioning information type information, used to indicate whether the specific type of the AI ​​positioning information or the tag positioning information to be reported is positioning result data or positioning intermediate data. A sending module is used to send a first response message to the target network side device, wherein the first response message includes at least one of the following information: AI positioning information, tag positioning information, and a positioning information deviation between the AI ​​positioning information and the tag positioning information, wherein the AI ​​positioning information is the positioning information generated by the first terminal through reasoning based on the AI ​​positioning model.

35. A device for acquiring positioning performance, It is characterized in that include: A second receiving module is used to receive AI positioning performance information or first information corresponding to the AI ​​positioning model sent by the target network side device, wherein the first information is used to indicate that the performance of the AI-based positioning method is degraded or insufficient; The third acquisition module is used to obtain AI positioning performance information or the first information.

36. A terminal, It is characterized in that It includes a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, it implements the steps of the method for reporting positioning data as described in any one of claims 15 to 23, or implements the steps of the method for acquiring positioning performance as described in any one of claims 24 to 32.

37. A network side device, It is characterized in that It includes a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, it implements the steps of the method for acquiring positioning performance as described in any one of claims 1 to 14, or implements the steps of the method for acquiring positioning performance as described in any one of claims 24 to 32.

38. A readable storage medium, It is characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the steps of the method for acquiring positioning performance as described in any one of claims 1 to 14, the steps of the method for reporting positioning data as described in any one of claims 15 to 23, or the steps of the method for acquiring positioning performance as described in any one of claims 24 to 32.

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