Model monitoring method and apparatus, and communication device, communication system and storage medium

Abstract

Disclosed in the embodiments of the present disclosure are a model monitoring method and apparatus. The method comprises: sending first information to an access network device; and / or sending second information to a terminal by means of the access network device, wherein an artificial intelligence (AI) model or AI function is deployed in a first network element, and the AI model or AI function is not activated. An inactivated AI model or AI function deployed on a network-element side can be monitored, such that whether the AI model or AI function is suitable for the current communication environment can be learned accurately in a timely manner, thereby effectively improving the communication and processing efficiency of a system.

Description

Model monitoring method and device, communication equipment, communication system, and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a model monitoring method and apparatus, communication equipment, a communication system, and a storage medium. Background Art

[0002] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in various fields. AI is also rapidly interpenetrating other disciplines. In the field of mobile communications, how to integrate AI into wireless air interfaces and how AI can assist and improve wireless air interface transmission have become topics for further research.

[0003] Summary of the Invention

[0004] The embodiments of the present disclosure provide a model monitoring method and apparatus, a communication device, a communication system, and a storage medium.

[0005] An embodiment of the first aspect of the present disclosure proposes a model monitoring method, which includes: sending first information to an access network device; and / or sending second information to a terminal through the access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0006] The second aspect embodiment of the present disclosure proposes a model monitoring method, which includes: receiving first information sent by a first network element; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0007] An embodiment of the third aspect of the present disclosure proposes a model monitoring method, which includes: receiving second information sent by a first network element through an access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0008] An embodiment of the fourth aspect of the present disclosure proposes a model monitoring method, which includes: a first network element sends first information to an access network device; and / or the first network element sends second information to a terminal through the access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0009] The fifth aspect embodiment of the present disclosure proposes a first network element, which includes: a transceiver module for sending first information to an access network device; and / or for sending second information to a terminal through the access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0010] The sixth aspect embodiment of the present disclosure proposes an access network device, which includes: a transceiver module for receiving first information sent by a first network element; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0011] The seventh aspect embodiment of the present disclosure proposes a terminal, which includes: a transceiver module for receiving second information sent by a first network element through an access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0012] The solution proposed in the embodiment of the present disclosure is to send first information to an access network device; and / or send second information to a terminal through the access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated, and the inactivated AI model or AI function deployed on the network element side can be monitored, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the background technology, the drawings required for use in the embodiments of the present disclosure or the background technology will be described below.

[0014] FIG1 is a schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure;

[0015] FIG2A is an interactive schematic diagram of a model monitoring method provided by an embodiment of the present disclosure;

[0016] FIG2B is an interactive diagram of a model monitoring method provided by an embodiment of the present disclosure;

[0017] FIG3A is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0018] FIG3B is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0019] FIG3C is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0020] FIG3D is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0021] FIG3E is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0022] FIG4A is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0023] FIG4B is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0024] FIG4C is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0025] FIG5A is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0026] FIG5B is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0027] FIG5C is a flow chart of a model monitoring method provided by an embodiment of the present disclosure;

[0028] FIG6A is a schematic structural diagram of a first network element provided by an embodiment of the present disclosure;

[0029] FIG6B is a schematic structural diagram of an access network device provided by an embodiment of the present disclosure;

[0030] FIG6C is a schematic structural diagram of a terminal provided by an embodiment of the present disclosure;

[0031] FIG6D is a schematic structural diagram of a core network device provided in an embodiment of the present disclosure;

[0032] FIG7A is a schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0033] FIG7B is a schematic structural diagram of a chip provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The embodiments of the present disclosure provide a model monitoring method and apparatus, a communication device, a communication system, and a storage medium.

[0035] In a first aspect, an embodiment of the present disclosure proposes a model monitoring method, which includes: sending first information to an access network device; and / or sending second information to a terminal through the access network device; wherein the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0036] In the above embodiment, the inactivated AI model or AI function deployed on the network element side can be monitored so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0037] In combination with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following information: a first request, the first request is used to request the access network device to monitor the AI ​​model or the AI ​​function; first type information, the first type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the access network device; first configuration information, the first configuration information is used to configure the access network device to monitor the AI ​​model or the AI ​​function; the second information includes at least one of the following information: a second request, the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; the second type information, the second type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the terminal; second configuration information, the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

[0038] In the above embodiment, the configuration of the first information and / or the second information can enable the terminal and / or the access network device to assist in monitoring the inactivated AI model or AI function deployed on the network element side, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0039] In combination with some embodiments of the first aspect, in some embodiments, the above-mentioned first information includes the above-mentioned first request, and the above-mentioned method also includes: receiving a first response sent by the above-mentioned access network device, and the above-mentioned first response is used to indicate whether the above-mentioned access network device monitors the above-mentioned AI model or the above-mentioned AI function.

[0040] In the above embodiment, a request can be sent to the access network device to inquire whether the access network device is allowed to assist in completing the monitoring of the inactivated AI model or AI function deployed on the network element side, thereby improving the efficiency of monitoring.

[0041] In combination with some embodiments of the first aspect, in some embodiments, the above-mentioned second information includes the above-mentioned second request, and the above-mentioned method also includes: receiving a second response sent by the above-mentioned terminal through the above-mentioned access network device, and the above-mentioned second response is used to indicate whether the above-mentioned terminal monitors the above-mentioned AI model or the above-mentioned AI function.

[0042] In the above embodiment, a request can be sent to the terminal to inquire whether the terminal is allowed to assist in completing the monitoring of the inactivated AI model or AI function deployed on the network element side, thereby improving the efficiency of monitoring.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the type of monitoring includes at least one of the following: sending a reference signal; measuring the reference signal; sending measurement information, where the measurement information is obtained by measuring the reference signal.

[0044] In the above embodiment, the monitoring type can be configured for the terminal and / or access network equipment, so that the terminal and / or access network equipment assists in completing the monitoring of the inactivated AI model or AI function deployed on the network element side, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0045] In combination with some embodiments of the first aspect, in some embodiments, the above-mentioned first configuration information is used to configure at least one of the following: the above-mentioned access network device sends the above-mentioned reference signal; the above-mentioned access network device measures the above-mentioned reference signal; the above-mentioned access network device sends the above-mentioned measurement information.

[0046] In the above embodiment, it is possible to configure information related to access network device measurements so that the terminal and / or access network device assist in monitoring the inactivated AI model or AI function deployed on the network element side, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the second configuration information is used to configure at least one of the following: the terminal sends the reference signal; the terminal measures the reference signal; the terminal sends the measurement information.

[0048] In the above embodiment, terminal measurement-related information can be configured so that the terminal and / or access network equipment assists in monitoring the inactivated AI model or AI function deployed on the network element side, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0049] In combination with some embodiments of the first aspect, in some embodiments, the above-mentioned reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

[0050] In the above embodiment, the reference signal used for measurement can be flexibly selected based on the communication device that is more suitable for measurement, thereby improving the flexibility of the solution.

[0051] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: receiving measurement information sent by the above access network device and / or the above terminal; based on the above measurement information, performing inference testing on the above AI model or the above AI function to obtain inference information; based on the above inference information, determining whether to activate the above AI model or the above AI function.

[0052] In the above embodiment, based on the measurement information sent by the terminal and / or access network device, the inactivated AI model or AI function deployed on the network element side can be monitored, so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, and activate the AI ​​model or AI function in time, which can effectively improve the communication and processing efficiency of the system.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: sending a third request to the second network element, the above third request is used to request the above second network element to send fourth information, and the above fourth information is used to trigger the above first network element to send the above first information and / or the above second information.

[0054] In the above embodiment, the process of model monitoring can be integrated into the existing process, thereby enhancing the universality of the solution, reducing signaling overhead, and improving efficiency.

[0055] In a second aspect, an embodiment of the present disclosure proposes a model monitoring method, which includes: receiving first information sent by a first network element; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0056] In the above embodiment, the inactivated AI model or AI function deployed on the network element side can be monitored so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0057] In combination with some embodiments of the second aspect, in some embodiments, the above-mentioned first information includes at least one of the following information: a first request, the above-mentioned first request is used to request the above-mentioned access network device to monitor the above-mentioned AI model or the above-mentioned AI function; first type information, the above-mentioned first type information is used to indicate the type of monitoring of the above-mentioned AI model or the above-mentioned AI function by the above-mentioned access network device; first configuration information, the above-mentioned first configuration information is used to configure the above-mentioned access network device to monitor the above-mentioned AI model or the above-mentioned AI function.

[0058] In combination with some embodiments of the second aspect, in some embodiments, the above-mentioned first information includes the above-mentioned first request, and the above-mentioned method also includes: sending a first response to the above-mentioned first network element, and the above-mentioned first response is used to indicate whether the above-mentioned access network device monitors the above-mentioned AI model or the above-mentioned AI function.

[0059] In combination with some embodiments of the second aspect, in some embodiments, the type of monitoring includes at least one of the following: sending a reference signal; measuring the reference signal; sending measurement information, where the measurement information is obtained by measuring the reference signal.

[0060] In combination with some embodiments of the second aspect, in some embodiments, the above-mentioned first configuration information is used to configure at least one of the following: the above-mentioned access network device sends the above-mentioned reference signal; the above-mentioned access network device measures the above-mentioned reference signal; the above-mentioned access network device sends the above-mentioned measurement information.

[0061] In combination with some embodiments of the second aspect, in some embodiments, the above-mentioned reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

[0062] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: sending measurement information to the above-mentioned first network element; the above-mentioned measurement information is used by the above-mentioned first network element to perform inference testing on the above-mentioned AI model or the above-mentioned AI function to obtain inference information, and the above-mentioned inference information is used by the above-mentioned first network element to determine whether to activate the above-mentioned AI model or the above-mentioned AI function.

[0063] In a third aspect, an embodiment of the present disclosure proposes a model monitoring method, which includes: receiving second information sent by a first network element through an access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0064] In the above embodiment, the inactivated AI model or AI function deployed on the network element side can be monitored so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0065] In combination with some embodiments of the third aspect, in some embodiments, the second information includes at least one of the following information: a second request, the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; second type information, the second type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the terminal; second configuration information, the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

[0066] In combination with some embodiments of the third aspect, in some embodiments, the above-mentioned second information includes the above-mentioned second request, and the above-mentioned method also includes: sending a second response to the above-mentioned first network element through the above-mentioned access network device, and the above-mentioned second response is used to indicate whether the above-mentioned terminal monitors the above-mentioned AI model or the above-mentioned AI function.

[0067] In combination with some embodiments of the third aspect, in some embodiments, the type of monitoring includes at least one of the following: sending a reference signal; measuring the reference signal; sending measurement information, where the measurement information is obtained by measuring the reference signal.

[0068] In combination with some embodiments of the third aspect, in some embodiments, the above-mentioned second configuration information is used to configure at least one of the following: the above-mentioned terminal sends the above-mentioned reference signal; the above-mentioned terminal measures the above-mentioned reference signal; the above-mentioned terminal sends the above-mentioned measurement information.

[0069] In combination with some embodiments of the third aspect, in some embodiments, the above-mentioned reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

[0070] In combination with some embodiments of the third aspect, in some embodiments, the above method also includes: sending measurement information to the above-mentioned first network element; the above-mentioned measurement information is used by the above-mentioned first network element to perform inference testing on the above-mentioned AI model or the above-mentioned AI function to obtain inference information, and the above-mentioned inference information is used by the above-mentioned first network element to determine whether to activate the above-mentioned AI model or the above-mentioned AI function.

[0071] In a fourth aspect, an embodiment of the present disclosure proposes a model monitoring method, which includes: a first network element sends first information to an access network device; and / or the first network element sends second information to a terminal through the access network device; wherein, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

[0072] In the above embodiment, the inactivated AI model or AI function deployed on the network element side can be monitored so as to timely and accurately know whether the current communication environment is suitable for the AI ​​model or AI function, which can effectively improve the communication and processing efficiency of the system.

[0073] In combination with some embodiments of the fourth aspect, in some embodiments, the first information includes at least one of the following information: a first request, the first request is used to request the access network device to monitor the AI ​​model or the AI ​​function; first type information, the first type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the access network device; first configuration information, the first configuration information is used to configure the access network device to monitor the AI ​​model or the AI ​​function; the second information includes at least one of the following information: a second request, the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; the second type information, the second type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the terminal; second configuration information, the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

[0074] In combination with some embodiments of the fourth aspect, in some embodiments, the above-mentioned first information includes the above-mentioned first request, and the above-mentioned method also includes: the above-mentioned access network device sends a first response to the above-mentioned first network element, and the above-mentioned first response is used to indicate whether the above-mentioned access network device monitors the above-mentioned AI model or the above-mentioned AI function.

[0075] In combination with some embodiments of the fourth aspect, in some embodiments, the above-mentioned second information includes the above-mentioned second request, and the above-mentioned method also includes: the above-mentioned terminal responds to the above-mentioned first network element for a second time through the above-mentioned access network device, and the above-mentioned second response is used to indicate whether the above-mentioned terminal monitors the above-mentioned AI model or the above-mentioned AI function.

[0076] In combination with some embodiments of the fourth aspect, in some embodiments, the type of monitoring includes at least one of the following: sending a reference signal; measuring the reference signal; sending measurement information, where the measurement information is obtained by measuring the reference signal.

[0077] In combination with some embodiments of the fourth aspect, in some embodiments, the above-mentioned first configuration information is used to configure at least one of the following: the above-mentioned access network device sends the above-mentioned reference signal; the above-mentioned access network device measures the above-mentioned reference signal; the above-mentioned access network device sends the above-mentioned measurement information.

[0078] In combination with some embodiments of the fourth aspect, in some embodiments, the above-mentioned second configuration information is used to configure at least one of the following: the above-mentioned terminal sends the above-mentioned reference signal; the above-mentioned terminal measures the above-mentioned reference signal; the above-mentioned terminal sends the above-mentioned measurement information.

[0079] In combination with some embodiments of the fourth aspect, in some embodiments, the above-mentioned reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

[0080] In combination with some embodiments of the fourth aspect, in some embodiments, the above method also includes: the above access network device and / or the above terminal sends measurement information to the first network element; the first network element performs an inference test on the above AI model or the above AI function based on the above measurement information to obtain inference information; the first network element determines whether to activate the above AI model or the above AI function based on the above inference result.

[0081] In combination with some embodiments of the fourth aspect, in some embodiments, the above method also includes: the first network element sends a third request to the second network element, the above third request is used to request the above second network element to send fourth information, and the above fourth information is used to trigger the above first network element to send the above first information and / or the above second information.

[0082] In a fifth aspect, an embodiment of the present disclosure proposes a first network element, which includes a transceiver module; wherein the first network element is used to execute the first aspect and the optional implementation method of the first aspect.

[0083] In a sixth aspect, an embodiment of the present disclosure proposes an access network device, which includes a transceiver module; wherein the access network device is used to execute the second aspect and the optional implementation method of the second aspect.

[0084] In a seventh aspect, an embodiment of the present disclosure proposes a terminal, which includes a transceiver module; wherein the terminal is used to execute the third aspect and the optional implementation method of the third aspect.

[0085] In an eighth aspect, an embodiment of the present disclosure proposes a first network element, which includes: one or more processors; wherein the first network element is used to execute the first aspect and the optional implementation method of the first aspect.

[0086] In a ninth aspect, an embodiment of the present disclosure proposes an access network device, which includes: one or more processors; wherein the access network device is used to execute the second aspect and the optional implementation method of the second aspect.

[0087] In a tenth aspect, an embodiment of the present disclosure proposes a terminal, comprising: one or more processors; wherein the terminal is used to execute the third aspect and the optional implementation method of the third aspect.

[0088] In the eleventh aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal, an access network device, and a first network element; wherein the first network element is configured to execute the method described in the first aspect and the optional implementation of the first aspect, the network device is configured to execute the method described in the second aspect and the optional implementation of the second aspect, and the terminal is configured to execute the method described in the third aspect and the optional implementation of the third aspect.

[0089] In the twelfth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first aspect and the optional implementation of the first aspect, the second aspect and the optional implementation of the second aspect, and the third aspect and the optional implementation of the third aspect.

[0090] In the thirteenth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the first aspect and its optional implementation, the second aspect and its optional implementation, and the third aspect and its optional implementation.

[0091] In the fourteenth aspect, an embodiment of the present disclosure proposes a computer program, which, when running on a computer, enables the computer to execute the method described in the first aspect and its optional implementation, the second aspect and its optional implementation, and the third aspect and its optional implementation.

[0092] In a fifteenth aspect, an embodiment of the present disclosure provides a chip or chip system. The chip or chip system includes a processing circuit configured to execute the method described in accordance with the first aspect and its optional implementation, the second aspect and its optional implementation, and the third aspect and its optional implementation.

[0093] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0094] The present disclosure provides a model monitoring method and apparatus, a communication device, a communication system, and a storage medium. In some embodiments, the terms "model monitoring method" and "communication method" are interchangeable; the terms "model monitoring apparatus" and "communication apparatus" are interchangeable; and the terms "model monitoring system" and "communication system" are interchangeable.

[0095] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0096] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0097] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0098] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0099] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0100] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0101] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0102] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0103] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0104] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0105] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0106] In some embodiments, terms such as "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 less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0107] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0108] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0109] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0110] In some embodiments, "terminal" or "terminal device" may be referred to as "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, etc.

[0111] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0112] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0113] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0114] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0115] As shown in FIG1 , a communication system 100 includes a terminal 101 , an access network device 102 , and a core network device 103 .

[0116] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (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 a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0117] In some embodiments, the access network device 102 is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include a node in a model monitoring network, an evolved NodeB (eNB) in a 5G communication system, a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (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, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0118] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0119] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0120] In some embodiments, the core network device 103 may be a single device, including the first network element 1031, etc., or may be a plurality of devices or a device group, each including all or part of the first network element 1031, etc. The network element may be virtual or physical. The core network may include, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0121] In some embodiments, the first network element 1031 is, for example, a location management function (LMF).

[0122] In some embodiments, the first network element 1031 is used to manage the overall coordination and scheduling of resources for positioning activities, and can also be used to calculate or verify the final position and any speed estimates, and can estimate the accuracy achieved, can expose location services to AMF, interact with the terminal to exchange location information, etc., the name is not limited to this.

[0123] In some embodiments, the core network device 103 may further include a second network element 1032, where the second network element 1032 is, for example, an access and mobility management function (AMF).

[0124] In some embodiments, the second network element 1032 is used to manage the mobility of the terminal, can authorize and authenticate the access of the terminal, provide transmission for location service messages between the terminal and LMF and between the access network device and LMF, etc., and the name is not limited to this.

[0125] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0126] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0127] The embodiments of the present disclosure can be applied to a model monitoring system, 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), 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 (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0128] With the continuous development of mobile communications technology, 5G has been widely adopted in various sectors of society, bringing profound changes to every aspect of people's lives. 5G will permeate every aspect of future society, building a comprehensive, user-centric information ecosystem. For example, 5G user experience rates can reach 100Mbit / s to 1Gbit / s, enabling premium services like mobile virtual reality. 5G peak rates can reach 10Gbit / s to 20Gbit / s, with traffic density reaching 10Mbit / s / m², supporting the more than 1,000-fold increase in mobile traffic in the future. 5G connection density can reach 1 million per m², effectively supporting massive IoT devices. 5G transmission latency can reach milliseconds, meeting the stringent requirements of connected vehicles and industrial control. 5G can support speeds of 500km / h, ensuring a superior user experience even in high-speed rail environments. It is foreseeable that 5G, as a representative of new infrastructure, will reshape the future information society.

[0129] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in various fields. The continued development of fields such as intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home furnishings, healthcare, retail, security, and other fields. While bringing convenience to people's lives, it is also promoting industrial upgrading across various industries.

[0130] AI technology is also accelerating its cross-penetration with other disciplines. In the field of mobile communication technology, how to introduce AI technology into the wireless air interface and how to assist and improve the transmission of the wireless air interface have also become topics for further research.

[0131] For example, in the research of wireless communication technology, application cases of artificial intelligence (AI) include: AI-based CSI enhancement, AI-based beam management, AI-based positioning, and so on.

[0132] In the related art, most AI-based communications can only be used in specific occasions. When the wireless communication environment changes and is no longer suitable for AI-based communication or processing, the AI-based processing performance will decline, leading to a decline in the performance of the entire system. Therefore, it is necessary to monitor the performance of AI-based processing in order to shut down AI-based communication or switch AI functions / AI models. That is, the related art mainly monitors the running (or activated) AI model / AI function so that when the communication performance declines, the AI-based processing can be shut down or the AI ​​function / AI model can be switched.

[0133] On the other hand, when the terminal is in a non-AI processing state (i.e., not processing based on AI functions / AI models), performance monitoring is also required to enable AI-based processing when the wireless communication environment is suitable for AI processing, thereby enabling AI-based processing to improve processing efficiency. In other words, it is also necessary to monitor inactive AI models / AI functions to determine whether to activate the model / function and which model / function to activate.

[0134] The model monitoring method and device provided by the present disclosure are described in detail below with reference to the accompanying drawings.

[0135] FIG2A is an interactive diagram of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG2A , the embodiment of the present disclosure relates to a model monitoring method, which includes:

[0136] Step S2101: The first network element 1031 sends a third request.

[0137] In some embodiments, the first network element 1031 sends the third request to the second network element 1032 .

[0138] In some embodiments, the first network element 1031 is deployed with an AI model or AI functionality, and the AI ​​model or AI functionality is inactive.

[0139] In some embodiments, the third request is used to request the second network element to send fourth information.

[0140] In some embodiments, the name of the third request is not limited, and may be, for example, a "positioning request", a "model monitoring request", or the like.

[0141] Optionally, the third request may be a request message in an existing positioning process, used to request the second network element 1032 to initiate a positioning process.

[0142] Furthermore, the fourth information may be information that triggers the first network element 1031 to execute the positioning procedure when the node of the positioning procedure reaches the first network element 1031 .

[0143] In some embodiments, the fourth information is used to trigger the first network element 1031 to send the first information and / or the second information.

[0144] Step S2102: The second network element 1032 sends fourth information.

[0145] In some embodiments, the second network element 1032 sends the fourth information to the first network element 1031 .

[0146] In some embodiments, the fourth information is used to trigger the first network element 1031 to send the first information and / or the second information.

[0147] In some embodiments, the name of the fourth information is not limited, and it can be, for example, "positioning message", "positioning response", "model monitoring response", etc.

[0148] Step S2103: The first network element 1031 sends the first information.

[0149] In some embodiments, the first network element 1031 sends the above-mentioned first information to the access network device 102.

[0150] In some embodiments, the first information includes at least one of the following information: a first request, first type information, and first configuration information.

[0151] The first request is used to request the access network device 102 to assist the first network element 1031 in monitoring the AI ​​model or the AI ​​function. The first type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the access network device 102. The first configuration information is used to configure the access network device 102 to monitor the AI ​​model or the AI ​​function.

[0152] Optionally, the types of monitoring performed by the access network device 102 on the above-mentioned AI model or the above-mentioned AI function include: sending a reference signal, measuring a reference signal, and sending measurement information.

[0153] The above measurement information is obtained by measuring the above reference signal.

[0154] Optionally, the reference signal may be a positioning reference signal (PRS) or a channel sounding reference signal (SRS).

[0155] Optionally, the first configuration information is used to configure at least one of the following: the access network device 102 sends the reference signal; the access network device 102 measures the reference signal; the access network device 102 sends the measurement information.

[0156] Optionally, the first configuration information can be used to configure the access network device 102 to send a PRS.

[0157] Optionally, the first configuration information can be used to configure the access network device 102 to measure SRS.

[0158] Optionally, the first configuration information can be used to configure the access network device 102 to send measurement information, such as configuring the format of the measurement information, such as configuring the type of measurement information to be a channel impulse response (Channel Impulse Response, CIR) or a power delay profile (Power Delay Profile, PDP), etc., or configuring the number of sampling points retained in the measurement information, the number of access network devices retained, etc., or configuring the resources used to send the above measurement information, etc.

[0159] The above measurement information is obtained by the access network device 102 based on the measurement of the SRS.

[0160] In some embodiments, the first network element 1031 may directly send the first information instead of being triggered by the fourth information. The first network element 1031 may trigger itself when it determines that a model or function needs to be monitored.

[0161] Step S2104: The access network device 102 sends a first response.

[0162] In some embodiments, the access network device 102 sends a first response to the first network element 1031 .

[0163] In some embodiments, the first response is sent based on the first request included in the first information, and is used to inform the first network element 1031 to allow or reject the first request.

[0164] Step S2105: The first network element 1031 sends the second information.

[0165] In some embodiments, the first network element 1031 sends the second information to the terminal 101 through the access network device 102 .

[0166] In some embodiments, the second information includes at least one of the following information: a second request, second type information, and second configuration information.

[0167] The second request is used to request terminal 101 to assist first network element 1031 in monitoring the AI ​​model or the AI ​​function. The second type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by terminal 101. The second configuration information is used to configure terminal 101 to monitor the AI ​​model or the AI ​​function.

[0168] Optionally, the types of monitoring performed by the terminal 101 on the above-mentioned AI model or the above-mentioned AI function include: sending a reference signal, measuring a reference signal, and sending measurement information.

[0169] The above measurement information is obtained by measuring the above reference signal.

[0170] Optionally, the reference signal may be a positioning reference signal PRS or a channel sounding reference signal SRS.

[0171] Optionally, the second configuration information is used to configure at least one of the following: the terminal 101 sends the reference signal; the terminal 101 measures the reference signal; the terminal 101 sends the measurement information.

[0172] Optionally, the second configuration information can be used to configure the terminal 101 to send an SRS.

[0173] Optionally, the second configuration information can be used to configure the terminal 101 to measure the PRS.

[0174] Optionally, the second configuration information can be used to configure the terminal 101 to send measurement information, such as configuring the format of the measurement information, such as configuring the type of measurement information to be channel impulse response CIR or power delay spectrum PDP, etc., or configuring the number of sampling points retained in the measurement information, the number of retained access network devices, etc., or configuring the resources used to send the above measurement information, etc.

[0175] The above measurement information is obtained by the terminal 101 based on the measurement of the PRS.

[0176] In some embodiments, the first network element 1031 may directly send the second information instead of being triggered by the fourth information. The first network element 1031 may trigger itself when it determines that a model or function needs to be monitored.

[0177] Step S2106: Terminal 101 sends a second response.

[0178] In some embodiments, the terminal 101 sends a second response to the first network element 1031 through the access network device 102 .

[0179] In some embodiments, the second response is sent based on the second request included in the second information, and is used to inform the first network element 1031 to allow or reject the second request.

[0180] Step S2107: The access network device 102 sends a positioning reference signal PRS.

[0181] In some embodiments, the access network device 102 sends a PRS to the terminal 101 .

[0182] In some embodiments, the access network device 102 may send the PRS based on the first configuration information in the first information.

[0183] Step S2108: Terminal 101 obtains PRS measurement information.

[0184] In some embodiments, the terminal 101 receives the PRS sent by the access network device 102 .

[0185] In some embodiments, the terminal 101 measures the received PRS to obtain measurement information.

[0186] In some embodiments, the terminal 101 may measure the PRS based on the second configuration information in the second information.

[0187] In some embodiments, the terminal 101 may process the result of measuring the PRS based on the second configuration information in the second information to obtain the measurement information in a format that complies with the configuration.

[0188] In step S2109, the terminal 101 sends the above measurement information.

[0189] In some embodiments, the terminal 101 feeds back the above measurement information to the first network element 1031 through the access network device 102 .

[0190] In some embodiments, the above-mentioned measurement information is used by the first network element 1031 to obtain reasoning information of the above-mentioned AI model or AI function.

[0191] In step S2110 , the first network element 1031 determines inference information.

[0192] In some embodiments, the first network element 1031 determines the inference information based on the above measurement information.

[0193] In some embodiments, the first network element 1031 performs an inference test on the above-mentioned AI model or the above-mentioned AI function deployed in the first network element 1031 based on the above-mentioned measurement information to obtain inference information.

[0194] In step S2111 , the first network element 1031 determines whether to activate the AI ​​model or AI function.

[0195] In some embodiments, the first network element 1031 determines whether to activate the above-mentioned AI model or the above-mentioned AI function based on the above-mentioned reasoning information.

[0196] In some embodiments, the names of information, etc. 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", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0197] In some embodiments, the names of information, etc. 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", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0198] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, and terms such as "physical uplink shared channel (PUSCH)" and "UL data" can be used interchangeably.

[0199] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0200] In some embodiments, terms such as "resource block (RB)", "physical resource block (PRB)", "sub-carrier group (SCG)", "resource element group (REG)", "PRB pair", "RB pair", "resource element (RE)", and "sub-carrier" can be used interchangeably.

[0201] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0202] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0203] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0204] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or 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.

[0205] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2111. For example, steps 2101-2102 may be implemented as independent embodiments, steps 2103-2111 may be implemented as independent embodiments, steps 2103+2104 may be implemented as independent embodiments, steps 2105+2106 may be implemented as independent embodiments, steps 2107-2111 may be implemented as independent embodiments, etc., but the present invention is not limited thereto.

[0206] In some embodiments, steps 2101-2102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0207] In some embodiments, step 2103 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0208] In some embodiments, step 2104 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0209] In some embodiments, step 2105 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0210] In some embodiments, step 2106 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0211] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0212] FIG2B is an interactive diagram of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG2B , the embodiment of the present disclosure relates to a model monitoring method, which includes:

[0213] Step S2201: The first network element 1031 sends a third request.

[0214] In some embodiments, the first network element 1031 sends the third request to the second network element 1032 .

[0215] In some embodiments, the first network element 1031 is deployed with an AI model or AI functionality, and the AI ​​model or AI functionality is inactive.

[0216] In some embodiments, the third request is used to request the second network element to send fourth information.

[0217] In some embodiments, the name of the third request is not limited, and may be, for example, a "positioning request", a "model monitoring request", or the like.

[0218] Optionally, the third request may be a request message in an existing positioning process, used to request the second network element 1032 to initiate a positioning process.

[0219] Furthermore, the fourth information may be information that triggers the first network element 1031 to execute the positioning procedure when the node of the positioning procedure reaches the first network element 1031 .

[0220] In some embodiments, the fourth information is used to trigger the first network element 1031 to send the first information and / or the second information.

[0221] Step S2202: The second network element 1032 sends fourth information.

[0222] In some embodiments, the second network element 1032 sends the fourth information to the first network element 1031 .

[0223] In some embodiments, the fourth information is used to trigger the first network element 1031 to send the first information and / or the second information.

[0224] In some embodiments, the name of the fourth information is not limited, and it can be, for example, "positioning message", "positioning response", "model monitoring response", etc.

[0225] Step S2203: The first network element 1031 sends the first information.

[0226] In some embodiments, the first network element 1031 sends the above-mentioned first information to the access network device 102.

[0227] In some embodiments, the first information includes at least one of the following information: a first request, first type information, and first configuration information.

[0228] The first request is used to request the access network device 102 to assist the first network element 1031 in monitoring the AI ​​model or the AI ​​function. The first type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by the access network device 102. The first configuration information is used to configure the access network device 102 to monitor the AI ​​model or the AI ​​function.

[0229] Optionally, the types of monitoring performed by the access network device 102 on the above-mentioned AI model or the above-mentioned AI function include: sending a reference signal, measuring a reference signal, and sending measurement information.

[0230] The above measurement information is obtained by measuring the above reference signal.

[0231] Optionally, the reference signal may be a positioning reference signal PRS or a channel sounding reference signal SRS.

[0232] Optionally, the first configuration information is used to configure at least one of the following: the access network device 102 sends the reference signal; the access network device 102 measures the reference signal; the access network device 102 sends the measurement information.

[0233] Optionally, the first configuration information can be used to configure the access network device 102 to send a PRS.

[0234] Optionally, the first configuration information can be used to configure the access network device 102 to measure SRS.

[0235] Optionally, the first configuration information can be used to configure the access network device 102 to send measurement information, such as configuring the format of the measurement information, such as configuring the type of measurement information to be channel impulse response CIR or power delay spectrum PDP, etc., or configuring the number of sampling points retained in the measurement information, the number of retained access network devices, etc., or configuring the resources used to send the above measurement information, etc.

[0236] The above measurement information is obtained by the access network device 102 based on the measurement of the SRS.

[0237] In some embodiments, the first network element 1031 may directly send the first information instead of being triggered by the fourth information. The first network element 1031 may trigger itself when it determines that a model or function needs to be monitored.

[0238] Step S2204: The access network device 102 sends a first response.

[0239] In some embodiments, the access network device 102 sends a first response to the first network element 1031 .

[0240] In some embodiments, the first response is sent based on the first request included in the first information, and is used to inform the first network element 1031 to allow or reject the first request.

[0241] Step S2205: The first network element 1031 sends the second information.

[0242] In some embodiments, the first network element 1031 sends the second information to the terminal 101 through the access network device 102 .

[0243] In some embodiments, the second information includes at least one of the following information: a second request, second type information, and second configuration information.

[0244] The second request is used to request terminal 101 to assist first network element 1031 in monitoring the AI ​​model or the AI ​​function. The second type information is used to indicate the type of monitoring of the AI ​​model or the AI ​​function by terminal 101. The second configuration information is used to configure terminal 101 to monitor the AI ​​model or the AI ​​function.

[0245] Optionally, the types of monitoring performed by the terminal 101 on the above-mentioned AI model or the above-mentioned AI function include: sending a reference signal, measuring a reference signal, and sending measurement information.

[0246] The above measurement information is obtained by measuring the above reference signal.

[0247] Optionally, the reference signal may be a positioning reference signal PRS or a channel sounding reference signal SRS.

[0248] Optionally, the second configuration information is used to configure at least one of the following: the terminal 101 sends the reference signal; the terminal 101 measures the reference signal; the terminal 101 sends the measurement information.

[0249] Optionally, the second configuration information can be used to configure the terminal 101 to send an SRS.

[0250] Optionally, the second configuration information can be used to configure the terminal 101 to measure the PRS.

[0251] Optionally, the second configuration information can be used to configure the terminal 101 to send measurement information, such as configuring the format of the measurement information, such as configuring the type of measurement information to be channel impulse response CIR or power delay spectrum PDP, etc., or configuring the number of sampling points retained in the measurement information, the number of retained access network devices, etc., or configuring the resources used to send the above measurement information, etc.

[0252] The above measurement information is obtained by the terminal 101 based on the measurement of the PRS.

[0253] In some embodiments, the first network element 1031 may directly send the second information instead of being triggered by the fourth information. The first network element 1031 may trigger itself when it determines that a model or function needs to be monitored.

[0254] Step S2206: Terminal 101 sends a second response.

[0255] In some embodiments, the terminal 101 sends a second response to the first network element 1031 through the access network device 102 .

[0256] In some embodiments, the second response is sent based on the second request included in the second information, and is used to inform the first network element 1031 to allow or reject the second request.

[0257] Step S2207: Terminal 101 sends a channel sounding reference signal SRS.

[0258] In some embodiments, the terminal 101 sends an SRS to the access network device 102 .

[0259] In some embodiments, the terminal 101 may send the SRS based on the second configuration information in the second information.

[0260] Step S2208: The access network device 102 obtains SRS measurement information.

[0261] In some embodiments, the access network device 102 receives the SRS sent by the terminal 101 .

[0262] In some embodiments, the access network device 102 measures the received SRS to obtain measurement information.

[0263] In some embodiments, the access network device 102 may measure the SRS based on the first configuration information in the first information.

[0264] In some embodiments, the access network device 102 may process the result of measuring the SRS based on the first configuration information in the first information to obtain the measurement information in a format that complies with the configuration.

[0265] Step S2209: The access network device 102 sends the above measurement information.

[0266] In some embodiments, the access network device 102 feeds back the above measurement information to the first network element 1031 .

[0267] In some embodiments, the above-mentioned measurement information is used by the first network element 1031 to obtain reasoning information of the above-mentioned AI model or AI function.

[0268] In step S2210 , the first network element 1031 determines inference information.

[0269] In some embodiments, the first network element 1031 determines the inference information based on the above measurement information.

[0270] In some embodiments, the first network element 1031 performs an inference test on the above-mentioned AI model or the above-mentioned AI function deployed in the first network element 1031 based on the above-mentioned measurement information to obtain inference information.

[0271] In step S2111 , the first network element 1031 determines whether to activate the AI ​​model or AI function.

[0272] In some embodiments, the first network element 1031 determines whether to activate the above-mentioned AI model or the above-mentioned AI function based on the above-mentioned reasoning information.

[0273] In some embodiments, the names of information, etc. 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", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0274] In some embodiments, the names of information, etc. 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", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0275] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, and terms such as "physical uplink shared channel (PUSCH)" and "UL data" can be used interchangeably.

[0276] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0277] In some embodiments, terms such as "resource block (RB)", "physical resource block (PRB)", "sub-carrier group (SCG)", "resource element group (REG)", "PRB pair", "RB pair", "resource element (RE)", and "sub-carrier" can be used interchangeably.

[0278] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0279] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0280] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0281] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or 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.

[0282] The communication method involved in the embodiments of the present disclosure may include at least one of steps S2201 to S2211. For example, steps 2201-2202 may be implemented as independent embodiments, steps 2203-2211 may be implemented as independent embodiments, steps 2203+2204 may be implemented as independent embodiments, steps 2205+2206 may be implemented as independent embodiments, steps 2207-2211 may be implemented as independent embodiments, etc., but the present invention is not limited thereto.

[0283] In some embodiments, steps 2201-2202 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0284] In some embodiments, step 2203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0285] In some embodiments, step 2204 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0286] In some embodiments, step 2205 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0287] In some embodiments, step 2206 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0288] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2B .

[0289] FIG3A is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the first network element 1031 and includes:

[0290] Step S3101, sending a third request.

[0291] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0292] Step S3102, receiving the fourth information.

[0293] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0294] Step S3103, sending the first information.

[0295] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0296] Step S3104, receiving the first response.

[0297] The optional implementation of step S3104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0298] Step S3105, sending the second information.

[0299] The optional implementation of step S3105 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0300] Step S3106, receiving a second response.

[0301] The optional implementation of step S3106 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0302] Step S3107: Receive PRS measurement information.

[0303] The optional implementation of step S3107 can refer to the optional implementation of step S2109 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0304] Step S3108, determine the inference information.

[0305] The optional implementation of step S3108 can refer to the optional implementation of step S2110 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0306] Step S3109: Determine whether to activate the AI ​​model or AI function.

[0307] The optional implementation of step S3109 can refer to the optional implementation of step S2111 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0308] The communication method according to the embodiments of the present disclosure may include at least one of steps S3101 to S3109. For example, steps 3101 and 3102 may be implemented as independent embodiments, steps 3103 to 3109 may be implemented as independent embodiments, steps 3107, 3108, and 3109 may be implemented as independent embodiments, steps 3103, 3105, 3107, 3108, and 3109 may be implemented as independent embodiments, steps 3103, 3107, 3108, and 3109 may be implemented as independent embodiments, and steps 3105, 3107, 3108, and 3109 may be implemented as independent embodiments, etc., but the present invention is not limited thereto.

[0309] In some embodiments, steps 3101-3102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0310] In some embodiments, step 3103 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0311] In some embodiments, step 3104 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0312] In some embodiments, step 3105 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0313] In some embodiments, step 3106 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0314] FIG3B is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the first network element 1031 and includes:

[0315] Step S3201, sending a third request.

[0316] The optional implementation of step S3201 can refer to the optional implementation of step S2101 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0317] Step S3202, receiving the fourth information.

[0318] The optional implementation of step S3202 can refer to the optional implementation of step S2102 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0319] Step S3203, sending the first information.

[0320] The optional implementation of step S3203 can refer to the optional implementation of step S2103 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0321] Step S3204, receiving the first response.

[0322] The optional implementation of step S3204 can refer to the optional implementation of step S2104 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0323] Step S3205, sending the second information.

[0324] The optional implementation of step S3205 can refer to the optional implementation of step S2105 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0325] Step S3206, receiving a second response.

[0326] The optional implementation of step S3206 can refer to the optional implementation of step S2106 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0327] Step S3207: Receive SRS measurement information.

[0328] The optional implementation of step S3207 can refer to the optional implementation of step S2109 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0329] Step S3208, determine the inference information.

[0330] The optional implementation of step S3208 can refer to the optional implementation of step S2110 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0331] Step S3209: Determine whether to activate the AI ​​model or AI function.

[0332] The optional implementation of step S3209 can refer to the optional implementation of step S2111 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0333] The communication method according to the embodiments of the present disclosure may include at least one of steps S3201 to S3209. For example, steps 3201 and 3202 may be implemented as independent embodiments, steps 3203 to 3209 may be implemented as independent embodiments, steps 3207, 3208, and 3209 may be implemented as independent embodiments, steps 3203, 3205, 3207, 3208, and 3209 may be implemented as independent embodiments, steps 3203, 3207, 3208, and 3209 may be implemented as independent embodiments, and steps 3205, 3207, 3208, and 3209 may be implemented as independent embodiments, etc., but the present invention is not limited thereto.

[0334] In some embodiments, steps 3201-3202 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0335] In some embodiments, step 3203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0336] In some embodiments, step 3204 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0337] In some embodiments, step 3205 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0338] In some embodiments, step 3206 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0339] FIG3C is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the first network element 1031 and includes:

[0340] Step S3301, sending the first information.

[0341] The optional implementation of step S3301 can refer to the optional implementation of step S2103 in Figure 2A, step S3103 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0342] Step S3302, receiving the first response.

[0343] The optional implementation of step S3302 can refer to the optional implementation of step S2104 in Figure 2A, step S3104 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0344] Step S3303, sending the second information.

[0345] The optional implementation of step S3303 can refer to the optional implementation of step S2105 in Figure 2A, step S3105 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0346] Step S3304, receiving a second response.

[0347] The optional implementation of step S3304 can refer to the optional implementation of step S2106 in Figure 2A, step S3106 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0348] Step S3305: Receive PRS measurement information.

[0349] The optional implementation of step S3305 can refer to the optional implementation of step S2109 in Figure 2A, step S3107 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0350] Step S3306, determine the inference information.

[0351] The optional implementation of step S3306 can refer to the optional implementation of step S2110 in Figure 2A, step S3108 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0352] Step S3307, determine whether to activate the AI ​​model or AI function.

[0353] The optional implementation of step S3307 can refer to the optional implementation of step S2111 in Figure 2A, step S3109 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0354] The communication method according to the embodiments of the present disclosure may include at least one of steps S3301 to S3307. For example, steps 3301-3307 may be implemented as independent embodiments, steps 3305+3306+3307 may be implemented as independent embodiments, steps 3301+3303+3305+3306+3307 may be implemented as independent embodiments, steps 3301+3305+3306+3307 may be implemented as independent embodiments, steps 3303+3305+3306+3307 may be implemented as independent embodiments, and so on, but the present invention is not limited thereto.

[0355] In some embodiments, step 3301 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0356] In some embodiments, step 3302 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0357] In some embodiments, step 3303 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0358] In some embodiments, step 3304 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0359] FIG3D is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the first network element 1031 and includes:

[0360] Step S3401, sending the first information.

[0361] The optional implementation of step S3401 can refer to the optional implementation of step S2203 in Figure 2B, step S3203 in Figure 3B, and other related parts in the embodiments involved in Figures 2B and 3B, which will not be repeated here.

[0362] Step S3402: Receive a first response.

[0363] Optional implementations of step S3402 can be found in step S2204 of FIG. 2B , optional implementations of step S3204 of FIG. 3B , and other related parts of the embodiments involved in FIG. 2B and FIG. 3B , which will not be described in detail here.

[0364] Step S3403, sending the second information.

[0365] The optional implementation of step S3403 can refer to the optional implementation of step S2205 in Figure 2B, step S3205 in Figure 3B, and other related parts in the embodiments involved in Figures 2B and 3B, which will not be repeated here.

[0366] Step S3404, receiving a second response.

[0367] Optional implementations of step S3404 can be found in step S2206 of FIG. 2B , optional implementations of step S3206 of FIG. 3B , and other related parts of the embodiments involved in FIG. 2B and FIG. 3B , which will not be described in detail here.

[0368] Step S3405: ​​Receive SRS measurement information.

[0369] The optional implementation of step S3405 can refer to the optional implementation of step S2209 in Figure 2B, step S3207 in Figure 3B, and other related parts in the embodiments involved in Figures 2B and 3B, which will not be repeated here.

[0370] Step S3406, determine the inference information.

[0371] The optional implementation of step S3406 can refer to the optional implementation of step S2210 in Figure 2B, step S3208 in Figure 3B, and other related parts in the embodiments involved in Figures 2B and 3B, which will not be repeated here.

[0372] Step S3407: Determine whether to activate the AI ​​model or AI function.

[0373] The optional implementation of step S3407 can refer to the optional implementation of step S2211 in Figure 2B, step S3209 in Figure 3B, and other related parts in the embodiments involved in Figures 2B and 3B, which will not be repeated here.

[0374] The communication method according to the embodiments of the present disclosure may include at least one of steps S3401 to S3407. For example, steps 3401-3407 may be implemented as independent embodiments, steps 3405+3406+3407 may be implemented as independent embodiments, steps 3401+3403+3405+3406+3407 may be implemented as independent embodiments, steps 3401+3405+3406+3407 may be implemented as independent embodiments, steps 3403+3405+3406+3407 may be implemented as independent embodiments, and so on, but the present invention is not limited thereto.

[0375] In some embodiments, step 3401 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0376] In some embodiments, step 3402 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0377] In some embodiments, step 3403 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0378] In some embodiments, step 3404 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0379] FIG3E is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the first network element 1031 and includes:

[0380] Step S3501, sending the first information.

[0381] For the optional implementation of step S3501, please refer to the optional implementation of step S2103 in Figure 2A, step S2203 in Figure 2B, step S3103 in Figure 3A, step S3203 in Figure 3B, step S3301 in Figure 3C, and step S3401 in Figure 3D, as well as other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, 3C, and 3D, which will not be repeated here.

[0382] Step S3502: Send the second information.

[0383] For the optional implementation of step S3503, please refer to the optional implementation of step S2105 in Figure 2A, step S2205 in Figure 2B, step S3105 in Figure 3A, step S3205 in Figure 3B, step S3303 in Figure 3C, and step S3403 in Figure 3D, as well as other related parts in the embodiments involved in Figures 2A, 2B, 3A, 3B, 3C, and 3D, which will not be repeated here.

[0384] The communication method involved in the embodiments of the present disclosure may include at least one of steps S3501 to S3502. For example, step 3501 may be implemented as an independent embodiment, step 3502 may be implemented as an independent embodiment, steps 3501+3502 may be implemented as independent embodiments, etc., but the present disclosure is not limited thereto.

[0385] In some embodiments, step 3501 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0386] In some embodiments, step 3502 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0387] FIG4A is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0388] Step S4101, receiving first information.

[0389] The optional implementation of step S4101 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0390] Step S4102, sending a first response.

[0391] The optional implementation of step S4102 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0392] Step S4103: Send a positioning reference signal PRS.

[0393] The optional implementation of step S4103 can refer to the optional implementation of step S2107 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0394] The communication method involved in the embodiments of the present disclosure may include at least one of steps S4101 to S4103. For example, step 4101 can be implemented as an independent embodiment, step 4102 can be implemented as an independent embodiment, step 4103 can be implemented as an independent embodiment, steps 4101+4102 can be implemented as an independent embodiment, steps 4101+4103 can be implemented as an independent embodiment, steps 4101+4102+4103 can be implemented as an independent embodiment, and so on, but the present invention is not limited thereto.

[0395] In some embodiments, step 4102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0396] FIG4B is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0397] Step S4201, receiving first information.

[0398] The optional implementation of step S4201 can refer to the optional implementation of step S2203 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0399] Step S4202, sending a first response.

[0400] The optional implementation of step S4202 can refer to the optional implementation of step S2204 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0401] Step S4203: Acquire SRS measurement information.

[0402] The optional implementation of step S4203 can refer to the optional implementation of step S2208 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0403] Step S4204: Send the above measurement information.

[0404] The optional implementation of step S4204 can refer to the optional implementation of step S2209 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0405] The communication method involved in the embodiments of the present disclosure may include at least one of steps S4201 to S4204. For example, step 4201 can be implemented as an independent embodiment, step 4202 can be implemented as an independent embodiment, step 4203 can be implemented as an independent embodiment, step 4204 can be implemented as an independent embodiment, steps 4201+4202 can be implemented as an independent embodiment, steps 4201+4203+4204 can be implemented as an independent embodiment, steps 4201+4202+4203+4204 can be implemented as an independent embodiment, and so on, but the present invention is not limited thereto.

[0406] In some embodiments, step 4202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0407] FIG4C is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0408] Step S4301, receiving first information.

[0409] The optional implementation of step S4301 can be found in step S2103 of Figure 2A, step S2203 of Figure 2B, step S4101 of Figure 4A, the optional implementation of step S4201 of Figure 4B, and other related parts in the embodiments involved in Figures 2A, 2B, 4A, and 4B, which will not be repeated here.

[0410] FIG5A is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG5A , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0411] Step S5101, receiving second information.

[0412] The optional implementation of step S5101 can refer to the optional implementation of step S2105 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0413] Step S5102: Send a first response.

[0414] The optional implementation of step S5102 can refer to the optional implementation of step S2106 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0415] Step S5103: Acquire PRS measurement information.

[0416] The optional implementation of step S5103 can refer to the optional implementation of step S2108 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0417] Step S5104: Send the above measurement information.

[0418] The optional implementation of step S5104 can refer to the optional implementation of step S2109 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0419] The communication method involved in the embodiments of the present disclosure may include at least one of steps S5101 to S5104. For example, step 5101 can be implemented as an independent embodiment, step 5102 can be implemented as an independent embodiment, step 5103 can be implemented as an independent embodiment, step 5104 can be implemented as an independent embodiment, steps 5101+5102 can be implemented as an independent embodiment, steps 5101+5103+5104 can be implemented as an independent embodiment, steps 5101+5102+5103+5104 can be implemented as an independent embodiment, and so on, but the present invention is not limited thereto.

[0420] In some embodiments, step 5102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0421] FIG5B is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG5B , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0422] Step S5201, receiving second information.

[0423] The optional implementation of step S5201 can refer to the optional implementation of step S2205 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0424] Step S5202, sending a second response.

[0425] The optional implementation of step S5202 can refer to the optional implementation of step S2206 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0426] Step S5203: Send a channel sounding reference signal SRS.

[0427] The optional implementation of step S5203 can refer to the optional implementation of step S2207 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0428] The communication method involved in the embodiments of the present disclosure may include at least one of steps S5101 to S5103. For example, step 5101 can be implemented as an independent embodiment, step 5102 can be implemented as an independent embodiment, step 5103 can be implemented as an independent embodiment, steps 5101+5102 can be implemented as an independent embodiment, steps 5101+5103 can be implemented as an independent embodiment, steps 5101+5102+5103 can be implemented as an independent embodiment, and so on, but the present invention is not limited thereto.

[0429] In some embodiments, step 5102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0430] FIG5C is a flow chart of a model monitoring method according to an embodiment of the present disclosure. As shown in FIG5C , the embodiment of the present disclosure relates to a model monitoring method, which is executed by the access network device 102 and includes:

[0431] Step S5301, receiving second information.

[0432] The optional implementation of step S5301 can be found in step S2105 of Figure 2A, step S2205 of Figure 2B, step S5101 of Figure 5A, the optional implementation of step S5201 of Figure 5B, and other related parts in the embodiments involved in Figures 2A, 2B, 5A, and 5B, which will not be repeated here.

[0433] The following is an exemplary introduction to the methods described in the above embodiments.

[0434] In some embodiments, in response to the AI ​​model or AI function being deployed on the first network unit, the first network unit initiates a first process. The first process includes: the first network unit sending first information to the first device and / or sending second information to the second device.

[0435] The first information and / or the second information may include at least one of the following:

[0436] Assist the network with requests to deactivate AI models or AI functions;

[0437] Assisting the network in monitoring inactive AI models or AI functions, such as sending SRS / PRS, measuring SRS / PRS, and reporting measurement results;

[0438] Measurements or reporting configurations performed to assist the network in monitoring inactive AI models or AI functions, such as the configuration of the measured reference signal (RS), the reporting format, and the channel.

[0439] Among them, the above-mentioned first network unit mainly refers to LMF, the first device refers to the base station, and the second device refers to the terminal.

[0440] In some embodiments, in response to the first information and / or the second information including a request to assist the network in performing an inactivated AI model or AI function, the first device sends a first response message and / or the second device sends a second response message, wherein the response message is used to determine whether the first device / or the second device assists the network in performing monitoring of the inactivated AI model or AI function.

[0441] In some embodiments, in response to the first message and / or the second message including the type of assisting the network to perform inactivated AI model or AI function monitoring or the measurement or reporting configuration performed to assist the network to perform inactivated AI model or AI function monitoring, the first device sends a signal or measures or reports the measurement result according to the configuration of the first information, and the first device sends a signal or measures or reports the measurement result according to the configuration of the first information.

[0442] In some embodiments, the first network unit receives measurement information fed back by the first device or measurement information fed back by the second device, and uses the measurement information to test the target unactivated AI model or AI function, and determines whether to activate the target AI model or AI function based on the test results.

[0443] As an example, the AI ​​model is deployed on the LMF side. The model performs inference based on the PRS measurement results fed back by the terminal. The process is as follows:

[0444] 1. The LMF sends a request to the target terminal and the base station associated with the terminal to assist in testing the inactivated AI model or AI function.

[0445] 2. The target terminal and / or associated base station allows or rejects the request.

[0446] 3. In response to the permission request, the LMF sends the PRS configuration information to the associated base station, and the base station sends the PRS configuration information to the target terminal. The LMF sends the measurement result format information to the terminal, such as the measurement result type (CIR or PDP, etc.) and the reporting format, such as the number of base stations to retain and the number of sampling points to retain.

[0447] 4. The base station sends the PRS to the terminal, which measures the PRS and processes the PRS measurement results according to the requirements of the LMF. Finally, the PRS measurement results are reported according to the specified reporting format.

[0448] 5. The LMF receives the measurement results of the PRS sent by the terminal and tests the inactive AI model or AI function of the target based on the feedback measurement results.

[0449] As another example, the model is deployed on the LMF side. The model performs inference based on the SRS measurement results fed back by the base station. The process includes the following:

[0450] 1. The LMF sends a request to the target terminal and the base station associated with the terminal to assist in testing the inactivated AI model or AI function.

[0451] 2. The target terminal and / or associated base station allows or rejects the request.

[0452] 3. In response to the permission request, the LMF sends the SRS configuration information to the associated base station, and the base station sends the SRS configuration information to the target terminal. The LMF sends the measurement result format information to the base station, such as the measurement result type (CIR or PDP, etc.) and the reporting format, such as the number of base stations to retain and the number of sampling points to retain.

[0453] 4. The terminal sends SRS information to the base station based on the configuration. The base station measures the SRS, processes the SRS measurement information according to the configuration information, and reports it to the LMF.

[0454] 6. LMF receives the measurement information sent by the base station and tests the target's inactive AI model or AI function based on the feedback measurement results.

[0455] In some embodiments, the first process may be an existing positioning process or a newly defined process for monitoring an inactive AI model or AI function.

[0456] In some embodiments, the first process can be directly triggered by the first network device, i.e., the LMF itself triggers the process when it determines that an inactive AI model or AI function needs to be monitored, or the first network device initiates a request to the second network device, which then initiates the second process. The first device receives the trigger for the second process and then initiates the first process. For example, when the first network device wants to monitor an inactive AI model or AI function, it initiates a location request to the AMF, which in turn initiates the location process. When the location process node reaches the LMF, the LMF begins the process of initiating the first process in the aforementioned embodiment.

[0457] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0458] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0459] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. 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 a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit 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), etc.

[0460] Figure 6A is a schematic diagram of the structure of a first network element proposed in an embodiment of the present disclosure. As shown in Figure 6A, the first network element 6100 may include at least one of a transceiver module 6101 and a processing module 6102. In some embodiments, the transceiver module is configured to send first information to an access network device and / or to send second information to a terminal via the access network device. The first network element is deployed with an artificial intelligence (AI) model or AI function, and the AI ​​model or AI function is inactive.

[0461] Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving performed by the first network element 1031 in any of the above methods (for example, step S2101, step S2102, step S2103, step S2104, step S2105, step S2106, step S2109, step S2201, step S2202, step S2203, step S2204, step S2205, step S2206, step S2209, but not limited to these), which are not repeated here.

[0462] Optionally, the above-mentioned processing module is used to execute at least one of the other steps (for example, step S2110, step S2111, step S2210, step S2211, but not limited to these) performed by the first network element 1031 in any of the above methods, which are not repeated here.

[0463] Figure 6B is a schematic diagram of the structure of an access network device proposed in an embodiment of the present disclosure. As shown in Figure 6B, access network device 6200 may include at least one of a transceiver module 6201 and a processing module 6202. In some embodiments, the transceiver module is configured to receive first information sent by a first network element; wherein the first network element is deployed with an artificial intelligence (AI) model or AI function, and the AI ​​model or AI function is inactive.

[0464] Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving performed by the access network device 102 in any of the above methods (for example, step S2103, step S2104, step S2107, step S2109, step S2203, step S2204, step S2207, step S2209, but not limited to these), which will not be repeated here.

[0465] Optionally, the processing module is used to execute at least one of the other steps (such as step S2208, but not limited thereto) executed by the access network device 102 in any of the above methods, which will not be described in detail here.

[0466] Figure 6C is a schematic diagram of the structure of a terminal proposed in an embodiment of the present disclosure. As shown in Figure 6C, terminal 6300 may include at least one of a transceiver module 6301 and a processing module 6302. In some embodiments, the transceiver module is configured to receive second information sent by a first network element via an access network device; wherein the first network element is deployed with an artificial intelligence (AI) model or AI function, and the AI ​​model or AI function is inactive.

[0467] Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving performed by the terminal 101 in any of the above methods (for example, step S2103, step S2104, step S2107, step S2109, step S2203, step S2204, step S2207, step S2209, but not limited to these), which will not be repeated here.

[0468] Optionally, the processing module is used to execute at least one of the other steps (such as step S2108, but not limited thereto) executed by the terminal 101 in any of the above methods, which will not be described in detail here.

[0469] Figure 6D is a schematic diagram of the structure of a core network device proposed in an embodiment of the present disclosure. As shown in Figure 6D, core network device 6400 may include at least one of a transceiver module 6401 and a processing module 6402. In some embodiments, the transceiver module is configured to send first information to an access network device and / or to send second information to a terminal via the access network device. The first network element is deployed with an artificial intelligence (AI) model or AI function, and the AI ​​model or AI function is inactive.

[0470] Optionally, the above-mentioned transceiver module is used to execute at least one of the communication steps such as sending and / or receiving performed by the core network device 103 in any of the above methods (for example, step S2101, step S2102, step S2103, step S2104, step S2105, step S2106, step S2109, step S2201, step S2202, step S2203, step S2204, step S2205, step S2206, step S2209, but not limited to these), which will not be repeated here.

[0471] Optionally, the above-mentioned processing module is used to execute at least one of the other steps (such as step S2110, step S2111, step S2210, step S2211, but not limited to these) performed by the core network device 103 in any of the above methods, which are not repeated here.

[0472] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0473] In some embodiments, the processing module can be a single module or include multiple submodules. Optionally, the multiple submodules each execute all or part of the steps required to be executed by the processing module. Optionally, the processing module and the processor can be interchangeable.

[0474] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0475] As shown in FIG7A , the communication device 7100 includes one or more processors 7101. The processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control a communication device (e.g., a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. The communication device 7100 is used to perform any of the above methods.

[0476] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

[0477] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S2102, step S2103, step S2104, step S2105, step S2106, step S2107, step S2109, step S2201, step S2202, step S2203, step S2204, step S2205, step S2206, step S2207, step S2209, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2108, step S2110, step S2111, step S2208, step S2210, step S2211, but not limited thereto).

[0478] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0479] In some embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected to the memory 7102. The interface circuit 7104 may be configured to receive signals from the memory 7102 or other devices, and may be configured to send signals to the memory 7102 or other devices. For example, the interface circuit 7104 may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0480] The communication device 7100 described in the above embodiment may 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 may not be limited by FIG. 7A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: (1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0481] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0482] The chip 7200 includes one or more processors 7201 , and the chip 7200 is configured to execute any of the above methods.

[0483] In some embodiments, the chip 7200 further includes one or more interface circuits 7202. Optionally, the interface circuit 7202 is connected to the memory 7203. The interface circuit 7202 can be used to receive signals from the memory 7203 or other devices, and can be used to send signals to the memory 7203 or other devices. For example, the interface circuit 7202 can read instructions stored in the memory 7203 and send the instructions to the processor 7201.

[0484] In some embodiments, the interface circuit 7202 executes at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S2102, step S2103, but not limited thereto), and the processor 7201 executes at least one of the other steps.

[0485] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0486] In some embodiments, the chip 7200 further includes one or more memories 7203 for storing instructions. Alternatively, all or part of the memories 7203 may be located outside the chip 7200.

[0487] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes 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 may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0488] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0489] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

[0490] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0491] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0492] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0493] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A model monitoring method, characterized in that: The method is performed by a first network element, and the method includes: Sending first information to the access network device; and / or, Sending second information to the terminal through the access network device; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

2. The method according to claim 1, characterized in that: The first information includes at least one of the following information: A first request, where the first request is used to request the access network device to monitor the AI ​​model or the AI ​​function; First type information, where the first type information is used to indicate a type of monitoring performed by the access network device on the AI ​​model or the AI ​​function; first configuration information, where the first configuration information is used to configure the access network device to monitor the AI ​​model or the AI ​​function; The second information includes at least one of the following information: a second request, where the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; second type information, where the second type information is used to indicate a type of monitoring performed by the terminal on the AI ​​model or the AI ​​function; Second configuration information, where the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

3. The method according to claim 2, characterized in that The first information includes the first request, and the method further includes: Receive a first response sent by the access network device, where the first response is used to indicate whether the access network device monitors the AI ​​model or the AI ​​function.

4. The method according to claim 2, characterized in that: The second information includes the second request, and the method further includes: Receive a second response sent by the terminal through the access network device, where the second response is used to indicate whether the terminal monitors the AI ​​model or the AI ​​function.

5. The method according to any one of claims 2 to 4, characterized in that: The type of monitoring includes at least one of the following: Sending a reference signal; measuring the reference signal; Send measurement information, where the measurement information is obtained by measuring the reference signal.

6. The method according to claim 5, characterized in that The first configuration information is used to configure at least one of the following: The access network device sends the reference signal; The access network device measures the reference signal; The access network device sends the measurement information.

7. The method according to claim 5, characterized in that The second configuration information is used to configure at least one of the following: The terminal sends the reference signal; The terminal measures the reference signal; The terminal sends the measurement information.

8. The method according to any one of claims 5 to 7, characterized in that: The reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: Receiving measurement information sent by the access network device and / or the terminal; Based on the measurement information, performing an inference test on the AI ​​model or the AI ​​function to obtain inference information; Based on the inference information, determine whether to activate the AI ​​model or the AI ​​function.

10. The method according to any one of claims 1 to 9, characterized in that: The method further comprises: A third request is sent to the second network element, where the third request is used to request the second network element to send fourth information, and the fourth information is used to trigger the first network element to send the first information and / or the second information.

11. A model monitoring method, characterized in that: The method is performed by an access network device, and the method includes: Receiving first information sent by a first network element; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

12. The method according to claim 11, characterized in that The first information includes at least one of the following information: A first request, where the first request is used to request the access network device to monitor the AI ​​model or the AI ​​function; First type information, where the first type information is used to indicate a type of monitoring performed by the access network device on the AI ​​model or the AI ​​function; First configuration information, where the first configuration information is used to configure the access network device to monitor the AI ​​model or the AI ​​function.

13. The method according to claim 12, characterized in that The first information includes the first request, and the method further includes: A first response is sent to the first network element, where the first response is used to indicate whether the access network device monitors the AI ​​model or the AI ​​function.

14. The method according to claim 12 or 13, characterized in that The type of monitoring includes at least one of the following: Sending a reference signal; measuring the reference signal; Send measurement information, where the measurement information is obtained by measuring the reference signal.

15. The method according to claim 14, characterized in that The first configuration information is used to configure at least one of the following: The access network device sends the reference signal; The access network device measures the reference signal; The access network device sends the measurement information.

16. The method according to claim 14 or 15, characterized in that The reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

17. The method according to any one of claims 11 to 16, characterized in that: The method further comprises: Sending measurement information to the first network element; The measurement information is used by the first network element to perform an inference test on the AI ​​model or the AI ​​function to obtain inference information, and the inference information is used by the first network element to determine whether to activate the AI ​​model or the AI ​​function.

18. A model monitoring method, characterized in that: The method is executed by a terminal, and includes: Receiving second information sent by the first network element through the access network device; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

19. The method according to claim 18, characterized in that The second information includes at least one of the following information: a second request, where the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; second type information, where the second type information is used to indicate a type of monitoring performed by the terminal on the AI ​​model or the AI ​​function; Second configuration information, where the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

20. The method according to claim 19, characterized in that The second information includes the second request, and the method further includes: A second response is sent to the first network element through the access network device, where the second response is used to indicate whether the terminal monitors the AI ​​model or the AI ​​function.

21. The method according to claim 19 or 20, characterized in that The type of monitoring includes at least one of the following: Sending a reference signal; measuring the reference signal; Send measurement information, where the measurement information is obtained by measuring the reference signal.

22. The method according to claim 21, characterized in that The second configuration information is used to configure at least one of the following: The terminal sends the reference signal; The terminal measures the reference signal; The terminal sends the measurement information.

23. The method according to claim 21 or 22, characterized in that The reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

24. The method according to any one of claims 18 to 23, characterized in that: The method further comprises: Sending measurement information to the first network element; The measurement information is used by the first network element to perform an inference test on the AI ​​model or the AI ​​function to obtain inference information, and the inference information is used by the first network element to determine whether to activate the AI ​​model or the AI ​​function.

25. A model monitoring method, characterized in that: The method comprises: The first network element sends first information to the access network device; and / or, The first network element sends second information to the terminal through the access network device; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

26. The method according to claim 25, characterized in that The first information includes at least one of the following information: A first request, where the first request is used to request the access network device to monitor the AI ​​model or the AI ​​function; First type information, where the first type information is used to indicate a type of monitoring performed by the access network device on the AI ​​model or the AI ​​function; first configuration information, where the first configuration information is used to configure the access network device to monitor the AI ​​model or the AI ​​function; The second information includes at least one of the following information: a second request, where the second request is used to request the terminal to monitor the AI ​​model or the AI ​​function; second type information, where the second type information is used to indicate a type of monitoring performed by the terminal on the AI ​​model or the AI ​​function; Second configuration information, where the second configuration information is used to configure the terminal to monitor the AI ​​model or the AI ​​function.

27. The method according to claim 26, characterized in that The first information includes the first request, and the method further includes: The access network device sends a first response to the first network element, where the first response is used to indicate whether the access network device monitors the AI ​​model or the AI ​​function.

28. The method according to claim 26, characterized in that The second information includes the second request, and the method further includes: The terminal sends a second response to the first network element through the access network device, where the second response is used to indicate whether the terminal monitors the AI ​​model or the AI ​​function.

29. The method according to any one of claims 26 to 28, characterized in that: The type of monitoring includes at least one of the following: Sending a reference signal; measuring the reference signal; Send measurement information, where the measurement information is obtained by measuring the reference signal.

30. The method according to claim 29, characterized in that The first configuration information is used to configure at least one of the following: The access network device sends the reference signal; The access network device measures the reference signal; The access network device sends the measurement information.

31. The method according to claim 29, characterized in that The second configuration information is used to configure at least one of the following: The terminal sends the reference signal; The terminal measures the reference signal; The terminal sends the measurement information.

32. The method according to any one of claims 29 to 31, characterized in that: The reference signal includes a positioning reference signal PRS and / or a channel sounding reference signal SRS.

33. The method according to any one of claims 25 to 32, characterized in that: The method further comprises: The access network device and / or the terminal sends measurement information to the first network element; The first network element performs an inference test on the AI ​​model or the AI ​​function based on the measurement information to obtain inference information; The first network element determines whether to activate the AI ​​model or the AI ​​function based on the inference result.

34. The method according to any one of claims 25 to 33, characterized in that: The method further comprises: The first network element sends a third request to the second network element, where the third request is used to request the second network element to send fourth information, and the fourth information is used to trigger the first network element to send the first information and / or the second information.

35. A first network element, characterized in that: The first network element includes: a transceiver module, configured to send first information to an access network device; and / or, Used to send second information to the terminal through the access network device; The first network element is deployed with an artificial intelligence AI model or an AI function, and the AI ​​model or the AI ​​function is not activated. live.

36. An access network device, characterized in that: The access network equipment includes: A transceiver module, configured to receive first information sent by a first network element; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

37. A terminal, characterized in that: The terminal comprises: A transceiver module, configured to receive second information sent by the first network element through the access network device; Among them, the first network element is deployed with an artificial intelligence AI model or AI function, and the AI ​​model or the AI ​​function is not activated.

38. A first network element, characterized in that: The first network element includes: one or more processors; Wherein, the terminal is used to execute the model monitoring method described in any one of claims 1-10.

39. A network device, characterized in that: The network equipment includes: one or more processors; Wherein, the network device is used to execute the model monitoring method described in any one of claims 11-17.

40. A terminal, characterized in that: The terminal comprises: one or more processors; Wherein, the terminal is used to execute the model monitoring method described in any one of claims 18-24.

41. A communication system, characterized in that: It includes a first network element, an access network device, and a terminal, wherein the first network element is configured to implement the model monitoring method described in any one of claims 1-10, the access network device is configured to implement the model monitoring method described in any one of claims 11-17, and the terminal is configured to implement the model monitoring method described in any one of claims 18-24.

42. A storage medium storing instructions, characterized in that: When the instructions are executed on a communication device, the communication device is caused to execute the model monitoring method according to any one of claims 1-10, 11-17 or 18-24.

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