Information processing methods, devices, communication system and storage medium

Abstract

Provided in the embodiments of the present disclosure are information processing methods, devices, a communication system and a storage medium. An information processing method is executed by a first device, and comprises: acquiring positioning information of at least one second device; determining a performance result of an AI model on the basis of the positioning information; and determining a first operation on the basis of the performance result, wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and maintaining an AI positioning method or switching from an AI-based positioning method to a non-AI-based positioning method or switching from an AI positioning method to another AI positioning method.

Description

Information processing method, device, communication system and storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to an information processing method, device, communication system and storage medium. BACKGROUND

[0002] In the technical field of communication, a Location Services (LCS) technology increases an Artificial Intelligence (AI) based positioning.

[0003] SUMMARY

[0004] Embodiments of the present disclosure need to solve the problem of monitoring the performance of an AI model.

[0005] According to a first aspect of embodiments of the present disclosure, an information processing method is provided, executed by a first device, comprising: obtaining positioning information of at least one second device; determining a performance result of an AI model based on the positioning information; determining a first operation based on the performance result; wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and retaining an AI positioning method or switching from an AI based positioning method to a non-AI based positioning method or switching from an AI positioning method to another AI positioning method.

[0006] According to a second aspect of embodiments of the present disclosure, an information processing method is provided, executed by a fourth device, comprising: providing a second device supporting a first capability for the first device, the first capability being a capability of monitoring the performance of an AI model; wherein the positioning information of the second device is used by the first device to determine a performance result of the AI model, the performance result is used by the first device to determine a first operation, the first operation comprises at least one of the following: training the AI model or not training the AI model; retaining an AI positioning method or switching from an AI based positioning method to a non-AI based positioning method or switching from an AI positioning method to another AI positioning method.

[0007] According to a third aspect of embodiments of the present disclosure, a first device is provided, comprising: a first transceiver module configured to obtain positioning information of at least one second device; a first processing module configured to determine a performance result of an AI model based on the positioning information; determine a first operation based on the performance result; wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and retaining an AI positioning method or switching from an AI based positioning method to a non-AI based positioning method or switching from an AI positioning method to another AI positioning method.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a fourth device is provided, comprising: a second transceiver module configured to provide a second device supporting a first capability for a first device, the first capability being a capability of monitoring performance of an AI model; wherein positioning information of the second device is used by the first device to determine a performance result of the AI model, the performance result being used by the first device to determine a first operation, the first operation comprising at least one of: training the AI model or not training the AI model; retaining an AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from an AI positioning method to another AI positioning method.

[0009] According to a fifth aspect of the embodiments of the present disclosure, a communication device is provided, comprising one or more processors; wherein the communication device is configured to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0010] According to a sixth aspect of the embodiments of the present disclosure, a communication system is provided, comprising: a first device and a fourth device; wherein the first device is configured to perform the method described in the optional implementation of the first aspect, and the fourth device is configured to perform the method described in the optional implementation of the second aspect.

[0011] According to a seventh aspect of the embodiments of the present disclosure, a storage medium is provided, the storage medium storing instructions, when the instructions are executed on a communication device, causing the communication device to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0012] According to an eighth aspect of the embodiments of the present disclosure, a computer program product is provided, the computer program product comprising a computer program or instructions, the computer program or instructions being executed by a processor to implement the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0013] The embodiments of the present disclosure can clearly monitor the performance of the AI model. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following describes the drawings required for the embodiments, and the following drawings are only some embodiments of the present disclosure, and do not specifically limit the protection scope of the present disclosure.

[0015] FIG. 1A is a structural schematic diagram of an information processing system according to an embodiment of the present disclosure.

[0016] FIG. 1B is a flow schematic diagram of an information processing method according to an embodiment of the present disclosure.

[0017] FIG. 2 is an interaction diagram of an information processing method according to an embodiment of the present disclosure.

[0018] FIG. 3A is a flow diagram of an information processing method according to an embodiment of the present disclosure.

[0019] FIG. 3B is a flow diagram of an information processing method according to an embodiment of the present disclosure.

[0020] FIG. 4A is a flow diagram of an information processing method according to an embodiment of the present disclosure.

[0021] FIG. 4B is a flow diagram of an information processing method according to an embodiment of the present disclosure.

[0022] FIG. 5 is a flow diagram of an information processing method according to an embodiment of the present disclosure.

[0023] FIG. 6A is a structural diagram of a first device according to an embodiment of the present disclosure.

[0024] FIG. 6B is a structural diagram of a fourth device according to an embodiment of the present disclosure.

[0025] FIG. 7A is a structural diagram of a communication device according to an embodiment of the present disclosure.

[0026] FIG. 7B is a structural diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure provide an information processing method, device, communication system and storage medium.

[0028] In a first aspect, embodiments of the present disclosure provide an information processing method, performed by a first device, comprising: obtaining positioning information of at least one second device; determining a performance result of an AI model based on the positioning information; determining a first operation based on the performance result; wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and retaining an AI positioning method or switching from the AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

[0029] In the above embodiments, it is clear that the performance of the AI model can be monitored, so that the performance of the AI model used for AI positioning can be guaranteed, and the accuracy of obtaining the position of the second device based on the AI model for AI positioning is improved, or the position of the second device based on AI positioning can not be obtained when the performance of the AI model cannot be guaranteed.

[0030] In some embodiments of the first aspect, in some embodiments, the positioning information comprises: first position information and second position information; wherein the first position information is determined based on AI positioning, and the second position information is determined based on at least one positioning manner other than AI positioning.

[0031] In the above embodiments, the first position information obtained based on AI positioning can be compared with the second position information obtained based on other positioning manners other than AI positioning to determine the monitoring of the performance of the AI model, so that an accurate performance result of the AI model can be obtained.

[0032] In some embodiments of the first aspect, in some embodiments, before obtaining the positioning information of the at least one second device, the method further comprises: triggering monitoring of the performance of the AI model; or obtaining first information of a third device, and triggering monitoring of the performance of the AI model based on the first information.

[0033] In the above embodiments, it is clear that the performance of the AI model is monitored only when the AI model performance monitoring is triggered, so that the performance of the AI model used for AI positioning is guaranteed. Moreover, various ways of triggering the AI model performance monitoring are provided to adapt to more application scenarios.

[0034] In some embodiments of the first aspect, in some embodiments, the method further comprises: determining a second device supporting a first capability, wherein the first capability is a capability of monitoring the performance of the AI model.

[0035] In the above embodiments, the second device used for monitoring the performance of the AI model is determined.

[0036] In some embodiments of the first aspect, in some embodiments, the second device supporting the first capability is determined by: sending a first request to a fourth device; and receiving a first response sent by the fourth device; wherein the first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information, the area information is used to indicate a predetermined area supporting the first capability, the first request is used to request the second device in the predetermined area, and the first response is used to indicate the second device in the predetermined area; or obtaining second information stored by the first device, the second information being used to indicate the second device supporting the first capability.

[0037] In the above embodiments, various ways of determining the second device are provided to adapt to more application scenarios.

[0038] In some embodiments of the first aspect, in some embodiments, the positioning information comprises first location information; and the obtaining the positioning information of the at least one second device comprises: obtaining, for each of the at least one second device, measurement data based on AI positioning; and determining, based on the measurement data and the AI model, the first location information of each of the at least one second device.

[0039] In the above embodiments, when the at least one second device is two or more second devices, the first location information of each of the second devices can be determined based on the measurement data of the second devices, thereby facilitating subsequent determination of the performance result based on the first location information and the second location information of the second devices, and improving the accuracy of determining the performance of the AI model.

[0040] In some embodiments of the first aspect, in some embodiments, the positioning information comprises first location information and second location information; and the determining the first operation based on the positioning information comprises: determining, based on the first location information and the second location information of each of the at least one second device, the performance result of the AI model.

[0041] In the above embodiments, when the at least one second device is a plurality of second devices, the performance of the AI model can be determined based on the plurality of second devices, thereby improving the accuracy of determining the performance of the AI model.

[0042] In some embodiments of the first aspect, in some embodiments, the first device is a Location Management Function (LMF) or a Model Training Logical Function (MTLF); and / or, the second device is a terminal or a Positioning Reference Unit (PRU); and / or, the third device is a Network Function (NF) of a core network; and / or, the fourth device is an Access and Mobility Management Function (AMF) or a Unified Data Management (UDM).

[0043] In a second aspect, the embodiments of the present disclosure provide an information processing method, executed by a fourth device, comprising: providing, for a first device, a second device supporting a first capability, the first capability being a capability of monitoring performance of an AI model; wherein positioning information of the second device is used by the first device to determine a performance result of the AI model, and the performance result is used by the first device to determine a first operation, the first operation including at least one of the following: training the AI model or not training the AI model; retaining an AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from an AI positioning method to another AI positioning method.

[0044] In some embodiments in combination with the second aspect, in some embodiments, the positioning information includes: first position information and second position information; wherein the first position information is determined based on AI positioning, and the second position information is determined based on at least one positioning method other than AI positioning.

[0045] In some embodiments in combination with the second aspect, in some embodiments, the method further comprises: receiving a first request sent by the first device; providing, for the first device, the first device supporting the first capability includes: sending a first response to the first device; wherein the first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information, the area information is used to indicate a predetermined area supporting the first capability, the first request is used to request the second device within the area information, and the first response is used to indicate the second device within the predetermined area.

[0046] In a third aspect, the embodiments of the present disclosure provide a first device, comprising: a first transceiver module configured to obtain positioning information of at least one second device; a first processing module configured to determine a performance result of an AI model based on the positioning information; and determine a first operation based on the performance result; wherein the first operation includes at least one of the following: training the AI model or not training the AI model; and retaining an AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from an AI positioning method to another AI positioning method.

[0047] In a fourth aspect, the embodiments of the present disclosure provide a fourth device, comprising: a second transceiver module configured to: provide, for a first device, a second device supporting a first capability, the first capability being a capability of monitoring performance of an AI model; wherein positioning information of the second device is used by the first device to determine a performance result of the AI model; and the performance result is used by the first device to determine a first operation, the first operation including at least one of the following: training the AI model or not training the AI model; retaining an AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from an AI positioning method to another AI positioning method.

[0048] In a fifth aspect, an embodiment of the present disclosure provides a communication device, comprising one or more processors; wherein the communication device is configured to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0049] In a sixth aspect, an embodiment of the present disclosure provides a communication system, comprising: a first device and a fourth device; wherein the first device is configured to perform the method described in the optional implementation of the first aspect, and the fourth device is configured to perform the method described in the optional implementation of the second aspect.

[0050] In a seventh aspect, an embodiment of the present disclosure provides a storage medium, which stores instructions, when the instructions are run on a communication device, causing the communication device to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0051] In an eighth aspect, an embodiment of the present disclosure provides a computer program product, which comprises a computer program or instructions, and the computer program or instructions are executed by a processor to implement the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0052] In a ninth aspect, an embodiment of the present disclosure provides a program product, which is executed by a communication device, causing the communication device to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0053] In a tenth aspect, an embodiment of the present disclosure provides a computer program, which, when run on a computer, causes the computer to perform the information processing method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0054] In an eleventh aspect, an embodiment of the present disclosure provides a chip or chip system, which comprises processing circuitry configured to perform the method described in the first aspect, the second aspect, or the optional implementation of the first aspect and the second aspect.

[0055] It can be understood that the above-mentioned devices (such as the first device, the second device, the third device, and / or the fourth device, etc.), the communication system, the storage medium, the program product, the computer program, the chip or the chip system are all used to perform the method provided by the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0056] The embodiments of the present disclosure provide an information processing method, an information processing device, a communication system and a storage medium. In some embodiments, the information processing method and the information processing method and the like can be replaced with each other, the information processing device and the communication device and the like can be replaced with each other, and the information processing system and the communication system and the like can be replaced with each other.

[0057] The embodiments of the present disclosure are not exhaustive, but only illustrate some embodiments, and are not specific limitations on the protection scope of the present disclosure. In the case of no contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily, for example, the scheme after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily, in addition, the optional implementation manners in an embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, some or all steps of different embodiments can be combined arbitrarily, and an embodiment can be combined with optional implementation manners of other embodiments.

[0058] In each embodiment of the present disclosure, the terms and / or descriptions between the embodiments are consistent and can be used with each other if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0059] The terms used in the embodiments of the present disclosure are only for the purpose of describing the specific embodiments, and not as a limitation on the present disclosure.

[0060] In the embodiments of the present disclosure, unless otherwise specified, the elements expressed in singular form, such as "one", "one", "the", "the above", "the", "the above", "this", etc. can represent "one and only one", and can also represent "one or more", "at least one", etc. For example, in the case of using articles such as "a", "an", "the" in English, the noun after the article can be understood as singular expression, and can also be understood as plural expression.

[0061] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0062] In some embodiments, the terms "at least one of", "one or more", "a plurality of", "multiple" and the like can be replaced with each other.

[0063] In some embodiments, "at least one of A, B", "A and / or B", "in one case A, in another case B", "responsive to case A, responsive to case B" and the like, can be used to represent one or more of the following technical solutions: in some embodiments, A (A is executed regardless of B); in some embodiments, B (B is executed regardless of A); in some embodiments, A and B are selected from (A and B are selectively executed); in some embodiments, A and B (A and B are executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0064] In some embodiments, "A or B" and the like can include the following technical solutions according to the case: in some embodiments, A (A is executed regardless of B); in some embodiments, B (B is executed regardless of A); in some embodiments, A and B are selected from (A and B are selectively executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0065] In the embodiments of the present disclosure, the prefix words "first", "second" and the like are only used to distinguish different description objects, and do not constitute limitation on the position, order, priority, quantity or content of the description objects. The description of the description objects should be referred to the description in the context of the claims or embodiments, and should not be limited by the prefix words. For example, the description object is "field", and the ordinal words before "field" in "first field" and "second field" do not limit the position or order between "fields", and "first" and "second" do not limit whether the "fields" modified thereby are in the same message or not, nor limit the order of "first field" and "second field". For another example, the description object is "level", and the ordinal words before "level" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description object is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description object is "device", and "first device" and "second device" can be the same device or different devices, and the types thereof can be the same or different; for another example, the description object is "information", and "first information" and "second information" can be the same information or different information, and the content thereof can be the same or different.

[0066] In some embodiments, "including A", "containing A", "for indicating A", "carrying A" can be interpreted as directly carrying A, or indirectly indicating A.

[0067] In some embodiments, the terms "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0068] In some embodiments, the terms "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above", and the like can be replaced with each other, and the terms "less than", "less than or equal to", "not greater than", "fewer than", "fewer than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below", and the like can be replaced with each other.

[0069] In some embodiments, an apparatus and the like can be interpreted as an entity, and can also be interpreted as virtual, and the name thereof is not limited to the name recited in the embodiments, and the terms "apparatus", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", and the like can be replaced with each other.

[0070] In some embodiments, "network" can be interpreted as an apparatus (for example, an access network device, a core network device, and the like) included in the network.

[0071] In some embodiments, the terms “access network device (AN device),” “radio access network device (RAN device),” “base station (BS),” “radio base station,” “fixed station,” “node,” “access point,” “transmission point (TP),” “reception point (RP),” “transmission / reception point (TRP),” “panel,” “antenna panel,” “antenna array,” “cell,” “macro cell,” “small cell,” “femto cell,” “pico cell,” “sector,” “cell group,” “carrier,” “component carrier,” “bandwidth part (BWP),” and the like can be used interchangeably.

[0072] In some embodiments, the terms "terminal," "terminal device," "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," and so on can be replaced with each other.

[0073] In some embodiments, an access network device, a core network device, or a network device can be replaced with a terminal. For example, for a structure in which communication between an access network device, a core network device, or a network device and a terminal is replaced with communication between a plurality of terminals (for example, also referred to as device-to-device (D2D), vehicle-to-everything (V2X), and so on), embodiments of the present disclosure can also be applied. In this case, a structure in which a terminal has all or part of the functions of an access network device can also be provided. Furthermore, the language of "uplink," "downlink," and so on can also be replaced with language corresponding to communication between terminals (for example, "side"). For example, an uplink channel, a downlink channel, and so on can be replaced with a side channel, and an uplink, a downlink, and so on can be replaced with a side link.

[0074] In some embodiments, a terminal can be replaced with an access network device, a core network device, or a network device. In this case, a structure in which an access network device, a core network device, or a network device has all or part of the functions of a terminal can also be provided.

[0075] In some embodiments, obtaining data, information, and the like can comply with laws and regulations of the country in which the location is situated.

[0076] In some embodiments, data, information, and the like can be obtained after obtaining consent from a user.

[0077] Furthermore, each element, each row, or each column in the table of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0078] FIG. 1A is a structural schematic diagram of an information processing system 100 according to an embodiment of the present disclosure. As shown in FIG. 1A, the information processing system 100 can include a terminal 101 and a network device 102.

[0079] In some embodiments, the network device 102 can include at least one of an access network device and a core network device.

[0080] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an IOT device or terminal, a car with communication function, a smart car, a Pad, a computer with wireless transceiver function, a VR terminal device, an AR terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, and the like, but is not limited thereto.

[0081] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network, and the access network device can include at least one of an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0082] In some embodiments, the technical solutions of the present disclosure can be applied to an Open RAN architecture, at this time, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be realized through software or programs.

[0083] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), wherein the CU can also be referred to as a control unit. The CU-DU structure can split the protocol layers of the access network device, and the functions of part of the protocol layers are controlled by the CU, and the functions of the remaining part or all of the protocol layers are distributed in the DU and controlled by the CU, but are not limited thereto.

[0084] In some embodiments, the core network device can be one device including the first device, the second device, the third device, the fourth device, etc., or can be a plurality of devices or device groups including all or part of the above-mentioned first device, second device, third device, and / or fourth device, etc. The first device, the second device, the third device, and the fourth device can all be virtual or physical. The core network includes at least one of an evolved packet core (EPC), a 5G core network (5GCN), a next generation core (NGC), and a 6G core network (6GCN).

[0085] In some embodiments, the first device can be any network element or entity in the core network with a location management function or a positioning function. The name of the first network element is not limited, which can be an LMF or an MTLF, etc.

[0086] In some embodiments, the second device can be a terminal or a positioning reference unit (PRU), etc.

[0087] In some embodiments, the third device can be any network function in the core network. For example, the third device can be a network element or entity in the core network with network analysis capability. The name of the third device is not limited, which can be a network data analytics function (NWDAF), an operator management entity or function, etc.

[0088] In some embodiments, the fourth device can be any network element or entity in the core network with a mobility management function. The name of the fourth device is not limited, which can be an AMF, etc.

[0089] In some embodiments, the fourth device can be any network element or entity in the core network with data storage and / or management. The name of the fourth device is not limited, which can be a UDM, etc.

[0090] It can be understood that the information processing system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. It can be known by those skilled in the art that, with the evolution of system architecture and the appearance of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0091] The following embodiments of the present disclosure can be applied to the information processing system 100 illustrated in FIG. 1A, or part of the subjects, but are not limited thereto. The subjects illustrated in FIG. 1A are examples, and the information processing system can include all or part of the subjects in FIG. 1A, or other subjects other than those in FIG. 1A. The number and form of the subjects are arbitrary, and the connection relationship between the subjects is an example. The subjects can be connected or not connected, and the connection can be in any manner, can be direct or indirect, and can be wired or wireless.

[0092] Embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), 5G New Radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (Bluetooth (registered trademark)), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), system using other communication methods, next-generation system expanded based thereon, and the like. Further, a plurality of systems can be applied in combination (for example, combination of LTE or LTE-A and 5G, combination of 5G and 5G, combination of 5G and 6G, and the like).

[0093] In some embodiments, the LMF or MTLF performs model performance monitoring for AI-based or Machine Learning (ML)-based positioning. Optionally, the result of the model performance monitoring can trigger the LMF to change the positioning method, e.g., switch from an AI- or ML-based positioning method to a non-AI- or ML-based positioning method, or switch from a non-AI- or ML-based positioning method to an AI- or ML-based positioning method. Optionally, the result of the model performance monitoring can trigger the AI- or ML model to be trained. Optionally, the detailed procedures and services of the model performance monitoring will be decided in the specification phase.

[0094] In some embodiments, the data for AI- or ML-based positioning model training, inference, and / or model performance monitoring will be decided by at least one RAN working group (WG), and standalone (SA) WG2 will align with the RAN WGs. In the specification phase, the related procedures for data collection will be coordinated with the RAN working groups. Optionally, the collection of UE-related training data needs the authorization and / or consent of the user. Optionally, the selection of whether and how the LMF is extended to support AI- or ML-based LMF.

[0095] In some embodiments, as shown in FIG. 1B, an information processing method is provided, comprising the following steps:

[0096] Step S1101, the AMF acquires a LCS request.

[0097] Optionally, the AMF receives a terminal-originated-location request (MO-LR) sent by the UE; or, the AMF receives a terminal-terminated-location request (MT-LR) sent by a location service (LCS), or the AMF acquires a network-induced location request (NI-LR) initiated by itself.

[0098] Step S1102, the AMF selects an LMF.

[0099] Step S1103, the AMF sends a location request to the LMF.

[0100] Optionally, the LMF determines to select the AI or ML based positioning method to compute the final position of the UE or PRU. The LMF can subscribe or request the AI or ML model corresponding to the AI or ML based positioning method from the NWDAF; the NWDAF can collect the data for training the AI or ML model to compute the final position of the UE or PRU.

[0101] Step S1104A, the PRU or UE, and / or the gNB sends the measurement data to the LMF.

[0102] Optionally, the measurement data is used to determine the final position of the UE.

[0103] Step S1104B, the PRU or UE sends the known position of the UE to the LMF.

[0104] Optionally, the PRU or UE can obtain its known position by, for example, GPS or other positioning methods.

[0105] Step S1105A, the LMF performs AI model inference.

[0106] Optionally, the LMF performs AI model inference to compute the final position of the UE or PRU based on the measurement data obtained from the PRU or UE, and / or the gNB.

[0107] Step S1105B, the LMF performs AI model monitoring.

[0108] Optionally, the LMF monitors and / or evaluates the performance of the AI or ML model by comparing the computed final position of the PRU or UE and the known position of the PRU or UE to determine whether the performance of the AI or ML model is still good; or the AI or ML model needs to be discarded, then switch from the AI or ML based positioning method to the non-AI or ML based positioning method to compute the final position of the UE.

[0109] Optionally, the LMF can trigger to request training of the AI or ML training model.

[0110] Step S1106, the LMF sends a Location response to the AMF.

[0111] Optionally, the final position of the UE is included in the Location response.

[0112] Step S1107, the AMF sends a LCS response.

[0113] Optionally, the AMF sends the LCS response to a LCS consumer (e.g., UE, LCS client, etc.), the LCS response including the UE position (e.g., final position).

[0114] In the embodiments of the present disclosure, how and under what conditions the model performance monitoring is triggered is not specified; and one UE or PRU can not be sufficient to determine whether the AI or ML model performance is good, and a more appropriate number of UEs or PRUs is needed to determine whether the AI or ML model performance is good.

[0115] In some embodiments, the UE can be a terminal, or the terminal can be a UE.

[0116] FIG. 2 is an interaction diagram of an information processing method according to an embodiment of the present disclosure. As shown in FIG. 2, the embodiments of the present disclosure relate to an information processing method for an information processing system 100, and the method comprises:

[0117] In step S2101, the first device triggers performance monitoring of the AI model.

[0118] Optionally, the first device is not limited in name, and it can be, for example, an LMF or an MTLF.

[0119] In some embodiments, the first device triggers performance monitoring of the AI model. In this embodiment, the first device can itself trigger performance monitoring of the AI model.

[0120] In some embodiments, the first device triggers performance monitoring of the AI model based on a core network NF.

[0121] Optionally, the first device obtains first information of a third device; and the first device triggers performance monitoring of the AI model based on the first information. For example, the first information is used to trigger monitoring of the performance of the AI model.

[0122] Optionally, the third device sends the first information to the first device.

[0123] Optionally, the third device is a network function of the core network. The third device is not limited in name, and it can be, for example, an NWDAF.

[0124] Optionally, the first information is used to indicate triggering of the AI model performance.

[0125] Optionally, the first information is not limited in name, and it can be, for example, a trigger request or a trigger monitoring request or AI model performance monitoring indication information, etc.

[0126] Optionally, the performance of the AI model can be any model performance, for example, but not limited to, at least one of the following: model positioning accuracy, model rate, and model complexity, etc.

[0127] In step S2102, the first device determines a second device.

[0128] In some embodiments, the first device determines a second device that performs performance monitoring of the AI model.

[0129] Optionally, the first device determines at least one second device to perform the performance monitoring of the AI model.

[0130] Optionally, the first device determines a number of second devices to perform the performance monitoring of the AI model.

[0131] Optionally, the first device can be a terminal and / or a PRU. The terminal can be a UE.

[0132] In some embodiments, the second device can be a second device supporting the first capability or a second device within a predetermined area.

[0133] Optionally, the first capability is a capability of monitoring the performance of the AI model.

[0134] Optionally, the predetermined area is an area supporting the first capability; that is, the second device within the predetermined area can support the first capability. The predetermined area can also refer to a specific area.

[0135] In some embodiments, the first device determines the second device supporting the first capability.

[0136] In some embodiments, the first device sends a first request to a fourth device; and the first device receives a first response sent by the fourth device.

[0137] In some embodiments, the fourth device receives the first request sent by the first device; and the fourth device sends the first response to the first device.

[0138] In some embodiments, the first device obtains second information stored by the first device, the second information being used to indicate the second device supporting the first capability. Optionally, the second information can be obtained from the fourth device before the first device triggers the performance monitoring of the AI model.

[0139] Optionally, the fourth device can be an AMF or a UDM.

[0140] Optionally, the first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability. In this way, the second device supporting the first capability can be directly obtained.

[0141] Optionally, the first request carries area information, the area information being used to indicate a predetermined area supporting the first capability, the first request being used to request the second device within the predetermined area, and the first response being used to indicate the second device within the predetermined area. In this way, the second device supporting the first capability can be obtained by obtaining the second device within the predetermined area.

[0142] Optionally, the first response comprises second information when the first request is for requesting the second device supporting the first capability.

[0143] Optionally, the first response comprises third information when the first request is for requesting the second device in a predetermined area, the third information being used for indicating the second device in the predetermined area.

[0144] Optionally, the second information and the third information are not limited in name, for example, the second information and the third information can be subscription information or subscription data, etc.

[0145] Optionally, the AMF obtains the subscription information from the UDM during registration of the second device.

[0146] In some optional embodiments, step S2103 can comprise: the first device initiating an AI positioning process for the second device. Optionally, the first device initiates an AI positioning process for each second device.

[0147] Optionally, the first device sends a positioning request to the second device, the positioning request being used for requesting AI-based positioning. For example, the positioning request can be MO-LR or MT-LR or NI-LR, etc. in the previous embodiments.

[0148] In step S2103, at least one second device sends measurement data and / or second location information to the first device.

[0149] In some embodiments, the first device receives the measurement data and / or the second location information sent by at least one second device.

[0150] In some optional embodiments, the base station sends the measurement data and / or the second location information of at least one second device to the first device.

[0151] In some optional embodiments, the first device receives the measurement data and / or the second location information of at least one second device sent by the base station.

[0152] Optionally, the first device receives the measurement data and / or the second location information sent by each of the at least one second device.

[0153] Optionally, the measurement data is used by the first device to determine the first location information. For example, the measurement data is used to determine the location of the second device, etc.

[0154] In some embodiments, the first device obtains the first location information of at least one second device.

[0155] Optionally, the first device obtains measurement data of each of the at least one second device based on AI positioning; and determines the first position information of each of the at least one second device based on the measurement data and the AI model.

[0156] Optionally, the first position information and the second position information are both positioning information.

[0157] Optionally, the positioning information is not limited in name, and is, for example, position information.

[0158] Optionally, the first position information is determined based on a first positioning manner; and the first positioning manner is an AI positioning manner.

[0159] Optionally, the second position information is determined based on a second positioning manner; and the second positioning manner is at least one positioning manner other than AI positioning. For example, the second positioning manner can include, but is not limited to, at least one of satellite positioning, base station positioning, WIFI positioning, and assisted global positioning system (AGPS) positioning.

[0160] Optionally, the first device receives second position information of the second device determined based on at least one positioning manner other than AI positioning.

[0161] Optionally, the first position information is used to indicate a first position of the second device; and the second position information is used to indicate a second position of the second device. For example, the first position can be the final position in the previous embodiments; and the second position can be the known position in the previous embodiments. For example, the first position and the second position are both positions of the second device, but the first position is determined based on AI positioning, and the second position is determined based on at least one positioning manner other than AI positioning.

[0162] In some optional embodiments, the first device obtains the positioning information of the at least one second device.

[0163] In some optional embodiments, the first device obtains the positioning information of the at least one second device when a performance of the AI model is triggered to be monitored.

[0164] In step S2104, the first device determines a performance result of the AI model based on the positioning information.

[0165] In some embodiments, the first device determines the performance result of the AI model based on the first location information and the second location information.

[0166] In some embodiments, the first device determines the performance result of the AI model based on the first location information and the second location information of each of the at least one second device.

[0167] Optionally, the performance result can be a result of performance of the AI model. For example, the performance result can be a first positioning accuracy or a second positioning accuracy of the AI model, wherein the first positioning accuracy is higher than the second positioning accuracy. For another example, the performance result can also be a rate, a load degree, etc. of the AI model.

[0168] Optionally, the name of the performance result is not limited, which can be, for example, performance information or performance parameter of the AI model, etc.

[0169] Optionally, the first device can compare the first location information and the second location information of each of the at least one second device to evaluate the performance of the AI model. For example, if the first location indicated by the first location information of most of the at least one second device is not much different from the second location indicated by the second location information, it is determined that the performance of the AI model is good, such as the first positioning accuracy of the AI model. For another example, if the first location indicated by the first location information of most of the at least one second device is much different from the second location indicated by the second location information, it is determined that the performance of the AI model is poor, such as the second positioning accuracy of the AI model. For example, if the first device determines that the difference between the first location and the second location of more than a first number of second devices is within a predetermined range, it is determined that the AI model is in the first positioning accuracy. For another example, if the first device determines that the difference between the first location and the second location of more than a second number of second devices is outside the predetermined range, it is determined that the AI model is in the second positioning accuracy.

[0170] In step S2105, the first device determines the first operation based on the performance result.

[0171] In some embodiments, the first operation can include at least one of the following: training the AI model or not training the AI model; keeping the AI positioning method or switching from the AI positioning method to a non-AI positioning method or from the AI positioning method to another AI positioning method.

[0172] Exemplarily, the AI positioning method in the embodiments of the present disclosure is a first AI positioning method, and switching the AI positioning method to another AI positioning method can include: switching the first AI positioning method to a second AI positioning method. Here, the AI models used in the first AI positioning method and the second AI positioning method are different, etc.; for example, but not limited to, the parameters in the AI models are different and / or the AI algorithms adopted are different, etc.

[0173] Exemplarily, the first operation can include one of the following: training the AI model; not training the AI model; retaining the AI positioning method and training the AI model; retaining the AI positioning method and not training the AI model; not training the AI model and switching from the AI-based positioning method to the non-AI-based positioning method; retaining the AI positioning method and switching from the first AI positioning method to the second AI positioning method; switching from the first AI positioning method to the second AI positioning method and training the AI model of the second AI positioning method; switching from the first AI positioning method to the second AI positioning method and not training the AI model of the second AI positioning method.

[0174] Exemplarily, training the AI model can train the first model to the second model, the first model being the AI model before training, and the second model being the AI model after training. For example, the positioning accuracy of the first model is the second positioning accuracy, the positioning accuracy of the second model is the first positioning accuracy, and the first positioning accuracy is higher than the second positioning accuracy. For another example, the rate of the first model is the first rate, the rate of the second model is the second rate, and the first rate is lower than the second rate. For another example, the complexity of the first model is the first complexity, the complexity of the second model is the second complexity, and the first complexity is lower than the second complexity.

[0175] Exemplarily, if the first device determines that the performance result of the AI model is that the AI model is the first positioning accuracy, it determines to retain the AI positioning method and / or not to train the AI model; or, if the first device determines that the performance result of the AI model is that the AI model is the second positioning accuracy, it determines to retain the AI positioning method and train the AI model, or to switch from the AI-based positioning method to the non-AI-based positioning method.

[0176] Exemplarily, the first device determines to switch from the AI-based positioning method to the non-AI-based positioning method, which can be: switching from the first positioning method to the second positioning method.

[0177] In some optional embodiments, the AI positioning in steps S2101 to S2105 can be replaced by ML positioning, and the AI model can be replaced by ML model. Optionally, the first model can be the ML model before training, and the second model can be the ML model after training.

[0178] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "field", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0179] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other, and can be interpreted as various meanings such as reception from another subject, acquisition from a protocol, acquisition from a higher layer, self-processing, autonomous implementation, and the like.

[0180] In some embodiments, terms such as "send", "transmit", "report", "issue", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other.

[0181] In some embodiments, terms such as "certain", "preset", "pre-set", "set", "indicated", "a certain", "arbitrary", "first", and the like can be replaced with each other, and "certain A", "preset A", "pre-set A", "set A", "indicated A", "a certain A", "arbitrary A", "first A" can be interpreted as A that is predetermined in a protocol and the like, A that is obtained by setting, configuration, or indication, and A that is certain, a certain, arbitrary, or first, but are not limited thereto.

[0182] In some embodiments, determination or judgment can be performed by a value represented by 1 bit (0 or 1), by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values (for example, comparison with a predetermined value), but is not limited thereto.

[0183] The information processing method related to the embodiments of the present disclosure can include at least one of steps S2101 to S2105. For example, step S2101 can be implemented as an independent embodiment; step S2102 can be implemented as an independent embodiment; step S2103 can be implemented as an independent embodiment; step S2104 can be implemented as an independent embodiment; step S2105 can be implemented as an independent embodiment; a combination of step S2101 and step S2102 can be implemented as an independent embodiment; a combination of step S2102 and step S2103 can be implemented as an independent embodiment; a combination of step S2104 and step S2105 can be implemented as an independent embodiment; a combination of step S2103, step S2104 and step S2105 can be implemented as an independent embodiment; a combination of step S2102, step S2103, step S2104 and step S2105 can be implemented as an independent embodiment; and a combination of steps S2101 to S2105 can be implemented as an independent embodiment.

[0184] In some embodiments, steps S2101 to S2102 can be optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0185] In the embodiments of the present disclosure, each embodiment can be implemented independently or in combination with each other, and the steps in each embodiment can be distinguished as preceding steps and subsequent steps.

[0186] FIG. 3A is a flow diagram illustrating an information processing method according to an embodiment of the present disclosure. As shown in FIG. 3A, the embodiments of the present disclosure relate to an information processing method, which is performed by a first device, and the above method comprises:

[0187] Step S3101 triggers performance monitoring of an AI model.

[0188] Optional implementation of step S3101 can refer to optional implementation of step S2101 of FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be described here.

[0189] Step S3102 determines a second device. Optionally, the first device determines a second device supporting performance monitoring of the AI model.

[0190] Optional implementation of step S3102 can refer to optional implementation of step S2102 of FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be described here.

[0191] In some optional embodiments, the first device sends a first request.

[0192] Optionally, the first device can send the first request to the fourth device, but is not limited thereto, and can send the first request to other subjects.

[0193] In some optional embodiments, the first device obtains the first response.

[0194] Optionally, the first device receives the first response sent by the fourth device, but is not limited thereto, and can receive the first response sent by other subjects.

[0195] Optionally, the first device obtains the first response as specified by a protocol.

[0196] Optionally, the first device obtains the first response from upper layer(s).

[0197] Optionally, the first device processes to obtain the first response.

[0198] Optionally, the first device autonomously implements the function indicated by the first response, or the function is default or default.

[0199] Step S3103: Obtain measurement data and / or second location information of at least one second device.

[0200] Optional implementation of step S3103 can refer to optional implementation of step S2103 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0201] In some embodiments, the first device receives the measurement data and / or second location information sent by the second device, but is not limited thereto, and can receive the measurement data and / or second location information sent by other subjects.

[0202] In some embodiments, the first device obtains the measurement data and / or second location information as specified by a protocol.

[0203] In some embodiments, the first device obtains the measurement data and / or second location information from upper layer(s).

[0204] In some embodiments, the first device processes to obtain the measurement data and / or second location information.

[0205] In some embodiments, step S3103 is omitted, and the first device autonomously implements the function indicated by the measurement data and / or second location information, or the function is default or default.

[0206] In some optional embodiments, the first device determines the first location information based on the measurement data.

[0207] At step S3104, the performance result of the AI model is determined based on the positioning information. Optionally, the first device determines the performance result based on the first position information and the second position information.

[0208] The optional implementation of step S3104 can refer to the optional implementation of step S2104 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0209] At step S3105, the first operation is determined based on the performance result.

[0210] The optional implementation of step S3105 can refer to the optional implementation of step S2105 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0211] The information processing method involved in the embodiments of the present disclosure can include at least one of steps S3101 to S3104. For example, step S3101 can be implemented as an independent embodiment; step S3102 can be implemented as an independent embodiment; step S3103 can be implemented as an independent embodiment; step S3104 can be implemented as an independent embodiment; step S3105 can be implemented as an independent embodiment; a combination of steps S3101 and S3102 can be implemented as an independent embodiment; a combination of steps S3102 and S3103 can be implemented as an independent embodiment; a combination of steps S3104 and S3105 can be implemented as an independent embodiment; a combination of steps S3103, S3104 and S3105 can be implemented as an independent embodiment; a combination of steps S3102, S3103, S3104 and S3105 can be implemented as an independent embodiment; and a combination of steps S3101 to S3105 can be implemented as an independent embodiment.

[0212] In some embodiments, steps S3101 to S3102 can be optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0213] In the embodiments of the present disclosure, each embodiment can be implemented independently or in combination with each other, and the steps in each embodiment can be distinguished as preceding steps and subsequent steps.

[0214] FIG. 3B is a flow diagram illustrating an information processing method according to an embodiment of the present disclosure. As shown in FIG. 3B, the information processing method involves a first device, and the method comprises:

[0215] At step S3201, positioning information of at least one second device is acquired. Optionally, acquiring the positioning information of the at least one second device comprises: acquiring the positioning information of the at least one second device in a case where performance of the AI model is triggered to be monitored.

[0216] The optional implementation of step S3201 can refer to the optional implementation of step S2103 in FIG. 2, or the optional implementation of step S3103 in FIG. 3A, and other associated parts in the embodiments involved in FIG. 2 and FIG. 3A, which will not be repeated here.

[0217] At step S3202, a performance result of the AI model is determined based on the positioning information.

[0218] The optional implementation of step S3202 can refer to the optional implementation of step S2104 in FIG. 2, or the optional implementation of step S3104 in FIG. 3A, and other associated parts in the embodiments involved in FIG. 2 and FIG. 3A, which will not be repeated here.

[0219] At step S3203, a first operation is determined based on the performance result, wherein the first operation comprises at least one of the following: training the AI model or not training the AI model; and keeping the AI positioning method or switching from the AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

[0220] The optional implementation of step S3203 can refer to the optional implementation of step S2105 in FIG. 2, or the optional implementation of step S3105 in FIG. 3A, and other associated parts in the embodiments involved in FIG. 2 and FIG. 3A, which will not be repeated here.

[0221] In some embodiments, the positioning information comprises: first position information and second position information; the first position information is determined based on AI positioning, and the second position information is determined based on at least one positioning method other than AI positioning.

[0222] In some embodiments, before acquiring the positioning information of the at least one second device, the method further comprises: triggering monitoring of the performance of the AI model; or acquiring first information of a third device, and triggering monitoring of the performance of the AI model based on the first information.

[0223] In some embodiments, the method further comprises: determining a second device supporting a first capability, wherein the first capability is a capability of monitoring the performance of the AI model.

[0224] In some embodiments, determining the second device supporting the first capability comprises: sending a first request to the fourth device; and receiving a first response sent by the fourth device; wherein the first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information used to indicate a predetermined area supporting the first capability, the first request is used to request the second device in the predetermined area, and the first response is used to indicate the second device in the predetermined area; or obtaining second information stored by the first device, the second information being used to indicate the second device supporting the first capability.

[0225] In some embodiments, the positioning information comprises first location information; and obtaining the positioning information of the at least one second device comprises: obtaining measurement data based on AI positioning of each of the at least one second device; and determining the first location information of each of the at least one second device based on the measurement data and the AI model.

[0226] In some embodiments, the positioning information comprises first location information and second location information; and determining the performance result of the AI model based on the positioning information comprises: determining the performance result of the AI model based on the first location information and the second location information of each of the at least one second device.

[0227] In some embodiments, the first device is an LMF or an MTLF; and / or the second device is a terminal or a PRU; and / or the third device is an NF of a core network; and / or the fourth device is an AMF or a UDM.

[0228] The above embodiments can be implemented independently or in combination with each other. For optional implementation, refer to the optional implementation of the steps in FIG. 2 and FIG. 3A, which will not be described here.

[0229] FIG. 4A is a flow diagram of an information processing method according to some embodiments of the present disclosure. As shown in FIG. 4A, the present disclosure relates to an information processing method, which is performed by a fourth device, and the method comprises:

[0230] Step S4101: receiving a first request.

[0231] For optional implementation of step S4101, refer to the optional implementation of step S2102 in FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be described here.

[0232] In some embodiments, the fourth device receives the first request sent by the first device, but is not limited thereto, and can also receive the first request sent by other subjects.

[0233] In some embodiments, the fourth device obtains the first request specified by a protocol.

[0234] In some embodiments, the fourth device obtains the first request from upper layer(s).

[0235] In some embodiments, the fourth device processes to obtain the first request.

[0236] In some embodiments, step S4101 is omitted, and the fourth device autonomously implements the function indicated by the first request, or the function is default or default.

[0237] Step S4102, sending the first response.

[0238] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0239] In some embodiments, the fourth device can send the first response to the first device, but is not limited thereto, and can also send the first response to other subjects.

[0240] The information processing method involved in the embodiments of the present disclosure can include at least one of steps S4101 to S4102. For example, step S4101 can be implemented as an independent embodiment; step S4102 can be implemented as an independent embodiment; and the combination of step S4101 and step S4102 can be implemented as an independent embodiment.

[0241] In the embodiments of the present disclosure, each embodiment can be implemented independently or in combination with each other, and the steps in each embodiment can be distinguished as preceding steps and subsequent steps.

[0242] FIG. 4B is a flow diagram illustrating an information processing method according to an embodiment of the present disclosure. As shown in FIG. 4B, the embodiments of the present disclosure involve an information processing method, which is performed by a fourth device, and the above method includes:

[0243] Step S4201, providing, for the first device, a second device supporting a first capability, the first capability being a capability of monitoring performance of an AI model; wherein positioning information of the second device is used by the first device to determine a performance result of the AI model, and the performance result is used by the first device to determine a first operation, the first operation including at least one of the following: training the AI model or not training the AI model; retaining an AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from an AI positioning method to another AI positioning method.

[0244] The optional implementation of step S4201 can refer to the optional implementation of step S2102 in FIG. 2 or step S4102 in FIG. 4A, and other associated parts in the embodiments involved in FIG. 2 and FIG. 4A, which will not be repeated here.

[0245] In some embodiments, the first location information and the second location information; wherein the first location information is determined based on AI positioning, and the second location information is determined based on at least one positioning method other than AI positioning.

[0246] In some embodiments, the method further comprises: receiving a first request sent by the first device; and providing the first device with a first device supporting the first capability, comprising: sending a first response to the first device; wherein the first request is used to request a second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information, the area information is used to indicate a predetermined area supporting the first capability, the first request is used to request a second device in the predetermined area, and the first response is used to indicate the second device in the predetermined area.

[0247] In some embodiments, the first device is: an LMF or an MTLF; and / or, the second device is: a terminal or a PRU; and / or, the third device is: an NF of a core network; and / or, the fourth device is: an AMF or a UDM.

[0248] The above embodiments can be implemented alone or in combination with each other, and the optional implementation manners can refer to the optional implementation manners of the steps of FIG. 2 and FIG. 4A, which are not described herein again.

[0249] The embodiments of the present disclosure propose an information processing method, which relates to a performance monitoring process of an AI model; the performance monitoring process of the AI model can include the following: the LMF triggers the performance monitoring of the AI model or the LMF receives a triggering request of an NF of a core network to trigger the performance monitoring of the AI model; the LMF determines which PRUs or UEs support the performance monitoring of the AI model; the LMF triggers to initiate AI positioning for each PRU or UE, obtains measurement data from the UE or gNB, and calculates a first location of the PRU or UE based on the measurement data; the LMF obtains a second location (for example, a known location) of the PRU or UE; the LMF evaluates the performance of the AI model based on the first location and the second location to determine whether to change the positioning method (for example, switching from an AI positioning-based method to a non-AI positioning-based method, or keeping the AI positioning-based method) or whether to train the AI model used by the AI positioning.

[0250] FIG. 5 is a flow diagram of an information processing method according to an embodiment of the present disclosure. As shown in FIG. 5, the embodiments of the present disclosure relate to an information processing method, which includes:

[0251] In step S5101, the LMF triggers the performance monitoring of the AI model.

[0252] Optionally, the LMF itself triggers the performance monitoring of the AI model, or the LMF receives a triggering request sent by a network function of the core network to trigger the performance monitoring of the AI model. The triggering request can be the first information in the previous embodiment.

[0253] Optionally, the performance monitoring of the AI model is limited to a specific area; the specific area can be the predetermined area or the specific area in the previous embodiment.

[0254] Step S5102, the LMF determines the PRU and / or UE supporting the performance monitoring of the AI model.

[0255] Optionally, the LMF can request the number of PRUs and / or UEs supporting the performance monitoring of the AI model through the AMF or the UDM.

[0256] Optionally, the LMF can request the PRU and / or UE supporting the performance monitoring of the AI model through the AMF or the UDM. For example, the UDM includes subscription data, which at least indicates whether the UE and / or PRU supports the performance monitoring of the AI model. For example, during registration, the AMF creates the context of the PRU or UE by retrieving the subscription data from the UDM.

[0257] Optionally, the LMF sends the area information to the AMF, and the AMF selects the PRU and / or UE in the area indicated by the area information (e.g., the predetermined area or the specific area in the previous embodiment) to send to the LMF.

[0258] Optionally, the LMF acquires the PRU and / or UE configured to support the performance monitoring of the AI model. In this case, the LMF can not need to request the AMF to determine the PRU and / or UE supporting the performance monitoring of the AI model.

[0259] Step S5103, the LMF triggers the AI positioning of the PRU and / or UE.

[0260] Optionally, the LMF triggers the AI positioning initiated for each PRU or UE based on the PRU and / or UE provided by the AMF or the UDM or configured in the LMF.

[0261] Step S5104A, the LMF acquires measurement data from the UE and / or base station, and determines the first position of each PRU or UE based on the AI model of the AI positioning.

[0262] Optionally, the measurement data is the measurement data of each PRU or UE.

[0263] Step S5104B, the LMF acquires the second position of each PRU or UE.

[0264] Optionally, the LMF also acquires a known position (i.e., a second position) of each PRU or UE via a control plane or a user plane; the second position is determined based on at least one positioning method other than the AI positioning.

[0265] At step S5105, the LMF determines a performance result based on the first position and the second position of each PRU or UE.

[0266] At step S5106, the LMF determines whether to train the AI model or whether to change the positioning method based on the performance result.

[0267] Optionally, whether to change the positioning method can be switching from a method based on AI positioning to a method not based on AI positioning.

[0268] In the embodiments of the present disclosure, part or all of the steps, and optional implementation manners thereof, can be combined with part or all of the steps in other embodiments, or can be combined with optional implementation manners of other embodiments.

[0269] The embodiments of the present disclosure also propose an apparatus for implementing any of the above methods, for example, an apparatus including units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is also proposed, including units or modules for implementing each step performed by a network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0270] It should be understood that the division of each unit or module in the above apparatus is only a logical function division, and all or part of them can be integrated into a physical entity or physically separated in actual implementation. In addition, the units or modules in the apparatus can be implemented in the form of processor calling software: for example, the apparatus includes a processor, the processor is connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit or module of the above apparatus, wherein the processor is a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the apparatus or a memory outside the apparatus. Alternatively, the units or modules in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be realized by the design of hardware circuit. The above hardware circuit can be understood as one or more processors; for example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units or modules are realized by the design of the logical relationship of elements in the circuit; for another example, in another implementation, the above hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the above units or modules. All units or modules of the above apparatus can be implemented in the form of processor calling software, or all units or modules can be implemented in the form of hardware circuit, or part of the units or modules are implemented in the form of processor calling software, and the remaining part is implemented in the form of hardware circuit.

[0271] In embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), and the like. In another implementation, the processor can implement certain functions through a logical relationship of hardware circuits, and the logical relationship of the hardware circuits is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the configuration of the hardware circuit. It can be understood that the processor loads instructions to implement the functions of some or all of the units or modules described above. In addition, the hardware circuit can also be designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), and the like.

[0272] FIG. 6A is a structural schematic diagram of a first device 6100 according to an embodiment of the present disclosure. As shown in FIG. 6A, the first device 6100 includes a first receiving and transmitting module 6101 and a first processing module 6102. In some embodiments, the first receiving and transmitting module 6101 is configured to receive measurement data and / or second location information. Optionally, the first receiving and transmitting module 6101 is configured to perform at least one of the steps of transmitting and / or receiving performed by the first device 6100 in any of the methods described above (for example, steps S2101 and / or S2102 and / or S2103, but not limited thereto), which will not be described herein again. In some embodiments, the first processing module 6102 is configured to determine a first operation. Optionally, the first processing module 6102 performs at least one of the processing steps performed by the first device 6100 in any of the methods described above (for example, steps S2101 and / or S2102 and / or S2104, but not limited thereto), which will not be described herein again.

[0273] FIG. 6B is a structural schematic diagram of the fourth device 6200. As shown in FIG. 6B, the fourth device 6200 includes a second transceiver module 6201. In some embodiments, the second transceiver module 6201 is configured to receive the first request and / or send the first response. Optionally, the second transceiver module 6201 is configured to perform at least one of the sending and / or receiving steps (for example, the step S2102, but not limited thereto) performed by the fourth device 6200 in any of the above methods, which will not be described here.

[0274] In some embodiments, the transceiver module can include a sending module and / or a receiving module, which can be separate or integrated together. Optionally, the transceiver module can be replaced by a transceiver. For example, the first transceiver module includes a first sending module and / or a first receiving module. For example, the second transceiver module includes a second sending module and / or a second receiving module.

[0275] In some embodiments, the processing module can be one module or include multiple sub-modules. Optionally, the multiple sub-modules perform all or part of the steps required to be performed by the processing module. Optionally, the processing module can be replaced by a processor.

[0276] FIG. 7A is a structural schematic diagram of a communication device 7100. The communication device 7100 can be a network device (for example, an access network device, a core network device, etc.), a terminal, a chip, a chip system, or a processor supporting the network device to implement any of the above methods, or a chip, a chip system, or a processor supporting the terminal to implement any of the above methods. The communication device 7100 can be used to implement the methods described in the above method embodiments, and specific implementation can be referred to the descriptions in the above method embodiments.

[0277] As shown in FIG. 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a special-purpose processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (for example, a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process data of the programs. Optionally, the communication device 7100 is configured to perform any of the above methods. Optionally, the one or more processors 7101 are configured to invoke instructions to cause the communication device 7100 to perform any of the above methods.

[0278] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps (e.g., steps S2101 and / or step S2102 and / or step S2103, etc., but not limited to) in the above-described methods, and the processor 7101 performs at least one of the other steps (e.g., steps S2101 and / or step S2102 and / or step S2104, etc., but not limited to). In alternative embodiments, the transceiver can include a receiver and / or a transmitter, which can be separate or integrated together. Alternatively, the terms transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be replaced with each other, the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced with each other.

[0279] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memory 7103 can also be outside the communication device 7100. In alternative embodiments, the communication device 7100 can include one or more interface circuits 7104. Alternatively, the interface circuit 7104 is connected with the memory 7103, and the interface circuit 7104 can be used to receive data from the memory 7103 or other devices, and can be used to send data to the memory 7103 or other devices. For example, the interface circuit 7104 can read the data stored in the memory 7103 and send the data to the processor 7101.

[0280] The communication device 7100 described in the above embodiments can be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 can not be limited by FIG. 7A. The communication device can be a standalone device or can be part of a larger device. For example, the communication device can be: (1) a standalone integrated circuit (IC), or a chip, or a chip system or subsystem; (2) a set of one or more ICs, which can optionally include storage components for storing data, programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0281] FIG. 7B is a structural schematic diagram of a chip 7200 according to an embodiment of the present disclosure. For the case where the communication device 7100 can be a chip or a chip system, the structural schematic diagram of the chip 7200 shown in FIG. 7B can be referred to, but is not limited thereto.

[0282] The chip 7200 comprises one or more processors 7201. The chip 7200 is configured to execute any of the above methods.

[0283] In some embodiments, the chip 7200 further comprises one or more interface circuits 7202. Optionally, the terms interface circuit, interface, transceiver pin, etc. can be replaced by each other. In some embodiments, the chip 7200 further comprises one or more memories 7203 for storing data. Optionally, all or part of the memory 7203 can be outside the chip 7200. Optionally, the interface circuit 7202 is connected with the memory 7203, the interface circuit 7202 can be configured to receive data from the memory 7203 or other devices, and the interface circuit 7202 can be configured to send data to the memory 7203 or other devices. For example, the interface circuit 7202 can read the data stored in the memory 7203 and send the data to the processor 7201.

[0284] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (such as step S2101 and / or step S2102 and / or step S2103, but not limited thereto) in the above methods. The interface circuit 7202 performing the communication steps in the above methods, for example, means that the interface circuit 7202 performs data interaction between the processor 7201, the chip 7200, the memory 7203 or the transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (such as step S2101 and / or step S2102 and / or step S2104, but not limited thereto).

[0285] The modules and / or devices described in each of the embodiments of the virtual device, the physical device, the chip, etc. can be combined or separated as appropriate. Optionally, part or all of the steps can also be performed by multiple modules and / or devices in cooperation, which is not limited herein.

[0286] The present disclosure further proposes a storage medium having instructions stored thereon, which, when executed on the communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but is not limited thereto, and can also be a transitory storage medium.

[0287] The disclosure also proposes a program product which, when executed by the communication device 7100, causes the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0288] The disclosure also proposes a computer program which, when running on a computer, causes the computer to perform any of the above methods.

Claims

1. An information processing method characterized by comprising: The method is performed by a first device, comprising: obtaining positioning information of at least one second device; determining a performance result of an artificial intelligence (AI) model based on the positioning information; determining a first operation based on the performance result, wherein the first operation comprises at least one of the following: training or not training the AI model; retaining the AI positioning method or switching from the AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

2. The method of claim 1, wherein, The positioning information comprises first position information and second position information; wherein the first position information is determined based on AI positioning, and the second position information is determined based on at least one positioning method other than AI positioning.

3. The method according to claim 1 or 2, characterized in that, Before the obtaining of the positioning information of the at least one second device, the method further comprises: triggering monitoring of the performance of the AI model; or obtaining first information of a third device; and triggering monitoring of the performance of the AI model based on the first information.

4. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: determining the second device supporting a first capability, wherein the first capability is a capability of monitoring the performance of the AI model.

5. The method of claim 4, wherein, The determining of the second device supporting the first capability comprises one of the following: sending a first request to a fourth device; and receiving a first response sent by the fourth device, wherein the first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information, the area information is used to indicate a predetermined area supporting the first capability, the first request is used to request the second device in the predetermined area, and the first response is used to indicate the second device in the predetermined area; obtaining second information stored by the first device, the second information being used to indicate the second device supporting the first capability.

6. The method according to any one of claims 1 to 5, characterized in that, The positioning information comprises first position information; and the obtaining of the positioning information of the at least one second device comprises: obtaining measurement data based on AI positioning of each of the at least one second device; determining the first position information of each of the at least one second device based on the measurement data and the AI model.

7. The method according to any one of claims 1 to 6, characterized in that, The positioning information comprises first position information and second position information; and the determining of the performance result of the AI model based on the positioning information comprises: determining the performance result of the AI model based on the first position information and the second position information of each of the at least one second device.

8. The method of any one of claims 1 to 7, wherein: the first device is a location management function (LMF) or a model training logic function (MTLF); and / or, the second device is a terminal or a positioning reference unit (PRU); and / or, the third device is a network function (NF) of a core network; and / or, the fourth device is an access and mobility management function (AMF) or a unified data management (UDM).

9. An information processing method characterized by comprising: The method is performed by a fourth device, comprising: The second device supports a first capability of the first device, the first capability being an ability to monitor performance of an artificial intelligence (AI) model; The positioning information of the second device is used by the first device to determine a performance result of the AI model, and the performance result is used by the first device to determine a first operation, the first operation including at least one of the following: training the AI model or not training the AI model; retaining the AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

10. The method of claim 9, wherein, The positioning information includes first position information and second position information; The first position information is determined based on AI positioning, and the second position information is determined based on at least one positioning method other than AI positioning.

11. The method of claim 9 or 10, wherein The method further includes receiving a first request sent by the first device. The first device supporting the first capability of the first device includes sending a first response to the first device. The first request is used to request the second device supporting the first capability, and the first response is used to indicate the second device supporting the first capability, or the first request carries area information, the area information is used to indicate a predetermined area supporting the first capability, the first request is used to request the second device in the predetermined area, and the first response is used to indicate the second device in the predetermined area.

12. A first device, comprising: The method includes: The first transceiver module is configured to obtain positioning information of at least one second device. The first processing module is configured to determine a performance result of an artificial intelligence (AI) model based on the positioning information, and determine a first operation based on the performance result, the first operation including at least one of the following: Training the AI model or not training the AI model; Retaining the AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

13. A fourth device, comprising: The method includes: The second transceiver module is configured to provide a second device supporting a first capability of a first device, the first capability being an ability to monitor performance of an artificial intelligence (AI) model; The positioning information of the second device is used by the first device to determine a performance result of the AI model, and the performance result is used by the first device to determine a first operation, the first operation including at least one of the following: training the AI model or not training the AI model; retaining the AI positioning method or switching from an AI positioning method to a non-AI positioning method or switching from the AI positioning method to another AI positioning method.

14. A communication device, characterized by The method includes: One or more processors; The communication device is configured to perform the information processing method of any one of claims 1 to 8 or claims 9 to 11.

15. A communication system, characterized by The method includes: a first device and a fourth device; wherein the first device is configured to implement the information processing method of any one of claims 1 to 8, and the fourth device is configured to implement the information processing method of any one of claims 9 to 11.

16. A storage medium, the storage medium storing instructions, wherein, The instructions, when executed on the communication device, cause the communication device to perform the information processing method of any one of claims 1 to 8, or claims 9 to 11.

17. A computer program product comprising a computer program or instructions, characterized in that, The computer program or instructions, when executed on the processor, implement the information processing method of any one of claims 1 to 8, or claims 9 to 11.

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