Monitoring configuration method, terminal, and network device

Abstract

The present invention relates to a monitoring configuration method, a terminal, and a network device. The method performed by a terminal comprises: receiving first instruction information sent by a network device, wherein the first instruction information is used for instructing the terminal to monitor an AI operation, and the AI operation is whether to enable a non-activated AI model or not. Therefore, on the basis of the first instruction information of the network device, the terminal can monitor whether the non-activated AI model is enabled or not, thereby conveniently managing the AI model, and improving the processing efficiency of using the AI model.

Description

Monitoring configuration methods, terminals, and network equipment Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a monitoring configuration method, a terminal, and a network device. Background Art

[0002] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in numerous fields. The continued development of AI-based technologies 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 living, healthcare, retail, security, and other fields, bringing convenience to people's lives while also promoting industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines, integrating knowledge from different disciplines while also providing new directions and methods for their development.

[0003] Summary of the Invention

[0004] The monitoring configuration method, terminal, and network device provided by the embodiments of the present disclosure are used to solve the problem of how to monitor whether an inactivated AI model is enabled.

[0005] The embodiments of the present disclosure provide a monitoring configuration method, a terminal, and a network device.

[0006] According to a first aspect of an embodiment of the present disclosure, a monitoring configuration method is proposed, which is executed by a terminal, including: receiving first indication information sent by a network device, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0007] In the above embodiment, the terminal can monitor whether the inactivated AI model is enabled based on the first indication information of the network device, so as to facilitate the management of the AI ​​model and improve the efficiency of the AI ​​model processing.

[0008] According to the second aspect of an embodiment of the present disclosure, a monitoring configuration method is proposed, which is executed by a network device, including: sending first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0009] In the above embodiment, the network device can send a first indication message to the terminal, instructing the terminal to monitor whether the inactivated AI model is enabled, so as to facilitate the management of the AI ​​model and improve the efficiency of the AI ​​model processing.

[0010] According to the third aspect of an embodiment of the present disclosure, a monitoring configuration method is proposed, wherein a network device sends first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model; the terminal receives the first indication information sent by the network device.

[0011] According to a fourth aspect of an embodiment of the present disclosure, a terminal is provided, comprising: a transceiver module for receiving first indication information sent by a network device, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0012] According to a fifth aspect of an embodiment of the present disclosure, a network device is provided, including: a transceiver module, used to send first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0013] According to a sixth aspect of an embodiment of the present disclosure, a terminal is provided, comprising: one or more processors; wherein the terminal is configured to execute the method described in the first aspect.

[0014] According to a seventh aspect of an embodiment of the present disclosure, a network device is provided, comprising: one or more processors; wherein the network device is used to execute the method described in the second aspect.

[0015] According to an eighth aspect of an embodiment of the present disclosure, a communication system is provided, including a terminal and a network device, wherein the terminal is configured to implement the method described in the first aspect, and the network device is configured to implement the method described in the second aspect.

[0016] According to a ninth aspect of an embodiment of the present disclosure, a storage medium is provided, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure;

[0018] FIG2 is a flow chart of a monitoring configuration method provided by an embodiment of the present disclosure;

[0019] FIG3A is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0020] FIG3B is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0021] FIG3C is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0022] FIG3D is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0023] FIG3E is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0024] FIG3F is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0025] FIG4A is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0026] FIG4B is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0027] FIG4C is a flowchart of another monitoring configuration method provided by an embodiment of the present disclosure;

[0028] FIG5A is a structural diagram of a terminal provided by an embodiment of the present disclosure;

[0029] FIG5B is a structural diagram of a network device provided by an embodiment of the present disclosure;

[0030] FIG6A is a structural diagram of a communication device provided by an embodiment of the present disclosure;

[0031] FIG6B is a schematic structural diagram of a chip provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The embodiments of the present disclosure provide a monitoring configuration method, a terminal, and a network device.

[0033] In a first aspect, an embodiment of the present disclosure proposes a monitoring configuration method, which is executed by a terminal, including: receiving first indication information sent by a network device, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0034] In the above embodiment, the terminal can monitor whether the inactivated AI model is enabled based on the first indication information of the network device, so as to facilitate the management of the AI ​​model and improve the efficiency of the AI ​​model processing.

[0035] In conjunction with some embodiments of the first aspect, in some embodiments, the terminal sends a first message to the network device;

[0036] The first message includes at least one of the following:

[0037] Monitoring results, where the monitoring results are determined by the terminal through monitoring of the AI ​​operation according to the first indication information;

[0038] First information, wherein the first information is used to indicate whether to enable an inactive AI model;

[0039] Processing result, where the processing result is that the terminal determines to activate an inactivated AI model, which is determined based on the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal;

[0040] Statistical results, where the statistical results are statistical results that the terminal determines meet the first condition based on the monitoring results.

[0041] In the above embodiment, the terminal can send a first message to the network device so that the network device can determine whether to enable the inactivated AI model based on the first message, thereby facilitating the management of the AI ​​model and improving the efficiency of processing using the AI ​​model.

[0042] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal monitors the AI ​​operation according to the first indication information and determines the monitoring result.

[0043] In the above embodiment, the terminal may monitor the AI ​​operation and determine the monitoring result, so as to determine whether to enable the inactivated AI model according to the monitoring result.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the above method further includes: the terminal sending the monitoring result to the network device.

[0045] In the above embodiment, the terminal can send the monitoring results to the network device, so that the network device determines whether to enable the inactivated AI model based on the monitoring results.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal determines whether to enable the inactivated AI model based on the monitoring results.

[0047] In the above embodiment, the terminal can determine whether to enable the inactivated AI model based on the monitoring results, so as to manage the inactivated AI model and improve the efficiency of processing using the inactivated AI model.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal sends first information to the network device, wherein the first information is used to indicate whether to enable the inactivated AI model.

[0049] In the above embodiment, the terminal can instruct the network device whether to enable the inactivated AI model so that the network device can manage the inactivated AI model and improve the efficiency of processing using the inactivated AI model.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the terminal determines whether to enable an inactivated AI model based on the monitoring results, including: determining the current usage environment based on the monitoring results; and determining whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the current usage environment.

[0051] In the above embodiment, the terminal can determine whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the current usage environment, so as to facilitate the management of the inactivated AI model and improve the efficiency of processing using the inactivated AI model.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the inactivated AI model includes a local AI model of the terminal, and the above method also includes: the terminal determines to enable the inactivated AI model, determines the processing result based on the monitoring results and the local AI model of the terminal; and sends the processing result to the network device.

[0053] In the above embodiment, when the terminal enables an inactivated AI model and the inactivated AI model includes a local AI model of the terminal, the terminal can determine the processing result based on the monitoring result and the local AI model of the terminal and send the processing result to the network device.

[0054] In combination with some embodiments of the first aspect, in some embodiments, the above method further includes: the terminal determines the statistical results that meet the first condition based on the monitoring results; and sends the statistical results to the network device.

[0055] In the above embodiment, the terminal can determine the statistical results that meet the first condition based on the monitoring results and report them to the network device, so that the network device can determine whether to enable the inactivated AI model based on the statistical results.

[0056] In combination with some embodiments of the first aspect, in some embodiments, the first condition is that the reference signal received power RSRP is greater than a first value within the measurement time period.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: the terminal receiving second indication information sent by the network device, where the second indication information is used to indicate at least one of the following:

[0058] Enable inactive AI models;

[0059] Continue to monitor AI operations;

[0060] Stop monitoring AI operations.

[0061] In the above embodiment, the terminal can receive the second indication information sent by the network device, determine to enable the inactivated AI model, or continue to monitor the AI ​​operation, or stop monitoring the AI ​​operation, so as to facilitate the management of the AI ​​model and improve the efficiency of using the AI ​​model processing.

[0062] In conjunction with some embodiments of the first aspect, in some embodiments, the first indication information includes at least one of the following:

[0063] First signaling, where the first signaling is used to instruct the terminal on measurement resources used to monitor the AI ​​operation;

[0064] Second signaling, where the second signaling is used to instruct the terminal to send a reporting resource used for monitoring the AI ​​operation;

[0065] The third signaling is used to indicate a trigger condition for the terminal to send a monitoring result of monitoring the AI ​​operation.

[0066] In the above embodiment, the terminal can determine whether to enable monitoring of the inactivated AI model based on at least one of the first signaling, the second signaling and the third signaling, so as to facilitate the management of the AI ​​model and improve the efficiency of processing using the AI ​​model.

[0067] In conjunction with some embodiments of the first aspect, in some embodiments, the monitoring result includes at least one of the following:

[0068] RSRP measurement results;

[0069] CSI-RS measurement results;

[0070] PRS measurement results.

[0071] In conjunction with some embodiments of the first aspect, in some embodiments, the triggering condition includes at least one of the following:

[0072] The monitoring result is lower than the first threshold;

[0073] The moving speed of the terminal in the monitoring result is greater than the second threshold.

[0074] With reference to some embodiments of the first aspect, in some embodiments, the measurement resource is a periodic resource or a non-periodic resource.

[0075] In combination with some embodiments of the first aspect, in some embodiments, the first indication information includes first signaling, and the above method further includes: the terminal monitoring the AI ​​operation on the measurement resource according to the first signaling.

[0076] In combination with some embodiments of the first aspect, in some embodiments, the first indication information includes second signaling, and the above method further includes: the terminal sends the monitoring result to the network device on the reporting resource.

[0077] In combination with some embodiments of the first aspect, in some embodiments, the first indication information includes third signaling, and the above method further includes: the terminal determines that the trigger condition is met, and sends the monitoring result to the network device.

[0078] In the second aspect, an embodiment of the present disclosure proposes a monitoring configuration method, which is executed by a network device, including: sending a first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor the AI ​​operation, and the AI ​​operation is whether to enable an inactivated AI model.

[0079] In the above embodiment, the network device can send a first indication message to the terminal, instructing the terminal to monitor whether the inactivated AI model is enabled, so as to facilitate the management of the AI ​​model and improve the efficiency of the AI ​​model processing.

[0080] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: the network device receiving a first message sent by the terminal; determining a target operation according to the first message;

[0081] The first message includes at least one of the following:

[0082] Monitoring results, where the monitoring results are determined by the terminal through monitoring of the AI ​​operation according to the first indication information;

[0083] First information, wherein the first information is used to indicate whether to enable an inactive AI model;

[0084] Processing result, where the processing result is that the terminal determines to activate an inactivated AI model, which is determined based on the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal;

[0085] Statistical results, where the statistical results are statistical results that the terminal determines meet the first condition based on the monitoring results.

[0086] In combination with some embodiments of the second aspect, in some embodiments, the first condition is that RSRP is greater than a first value within a measurement time period.

[0087] In conjunction with some embodiments of the second aspect, in some embodiments, the target operation includes at least one of the following:

[0088] Whether to enable inactive AI models;

[0089] Whether AI operations need to continue to be monitored;

[0090] Whether to stop monitoring AI operations.

[0091] In combination with some embodiments of the second aspect, in some embodiments, the inactivated AI model includes an AI model of a network device, and the above method also includes: the network device determines to enable the inactivated AI model, and determines to use the AI ​​model of the network device for processing.

[0092] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: the network device sending second indication information to the terminal, where the second indication information is used to indicate at least one of the following:

[0093] Enable inactive AI models;

[0094] Continue to monitor AI operations;

[0095] Stop monitoring AI operations.

[0096] In conjunction with some embodiments of the second aspect, in some embodiments, the first indication information includes at least one of the following:

[0097] First signaling, where the first signaling is used to instruct the terminal on measurement resources used to monitor the AI ​​operation;

[0098] Second signaling, where the second signaling is used to instruct the terminal to use a reporting resource for reporting a monitoring result of monitoring the AI ​​operation;

[0099] The third signaling is used to indicate a trigger condition for the terminal to report a monitoring result of the AI ​​operation.

[0100] In conjunction with some embodiments of the second aspect, in some embodiments, the monitoring result includes at least one of the following:

[0101] RSRP measurement results;

[0102] CSI-RS measurement results;

[0103] PRS measurement results.

[0104] In conjunction with some embodiments of the second aspect, in some embodiments, the triggering condition includes at least one of the following:

[0105] The monitoring result is lower than the first threshold;

[0106] The moving speed of the terminal in the monitoring result is greater than the second threshold.

[0107] In combination with some embodiments of the second aspect, in some embodiments, the measurement resource is a periodic resource or a non-periodic resource.

[0108] In combination with some embodiments of the second aspect, in some embodiments, the first indication information includes second signaling, and the above method further includes: the network device receives the monitoring result sent by the terminal on the reporting resource.

[0109] In combination with some embodiments of the second aspect, in some embodiments, the first indication information includes third signaling, and the above method also includes: the network device receives the monitoring results sent by the terminal when it is determined that the trigger condition is met.

[0110] On the third aspect, an embodiment of the present disclosure proposes a monitoring configuration method, where a network device sends a first indication message to a terminal, wherein the first indication message is used to instruct the terminal to monitor an AI operation, where the AI ​​operation is whether to enable an inactive AI model; the terminal receives the first indication message sent by the network device.

[0111] In the above embodiment, the network device can instruct the terminal to monitor whether the inactivated AI model is enabled, so as to facilitate the management of the AI ​​model and improve the efficiency of processing using the AI ​​model.

[0112] In a fourth aspect, an embodiment of the present disclosure proposes a terminal, which includes at least one of a transceiver module and a processing module; wherein the terminal is used to execute the optional implementation method of the first aspect.

[0113] In a fifth aspect, an embodiment of the present disclosure proposes a network device, which includes at least one of a transceiver module and a processing module; wherein the network device is used to execute the optional implementation method of the second aspect.

[0114] In a sixth aspect, an embodiment of the present disclosure proposes a terminal, comprising: one or more processors; wherein the terminal is used to execute the optional implementation method of the first aspect.

[0115] In a seventh aspect, an embodiment of the present disclosure proposes a network device, which includes: one or more processors; wherein the network device is used to execute the optional implementation method of the second aspect.

[0116] In the eighth aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

[0117] In a ninth 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 optional implementation of the first and second aspects.

[0118] In a tenth 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 optional implementation of the first and second aspects.

[0119] In an eleventh aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first and second aspects.

[0120] In a twelfth aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0121] 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.

[0122] The embodiments of the present disclosure provide a monitoring configuration method, a terminal, and a network device. In some embodiments, the monitoring configuration method, information processing method, communication method, and other terms can be used interchangeably.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0128] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0136] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0137] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0138] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0139] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0140] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0141] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0142] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0143] 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.

[0144] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure.

[0145] As shown in FIG1 , a communication system 100 includes a terminal 101 and a network device 102 .

[0146] 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.

[0147] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0148] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), 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.

[0149] In some embodiments, the core network device may be a single device including a first network function, a second network function, etc., or may be a plurality of devices or a group of devices each including all or part of the first network function, the second network function, etc. The network function 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).

[0150] In some embodiments, the first network function is, for example, an access and mobility management function (AMF).

[0151] In some embodiments, the first network function is used for access control and mobility management of the terminal accessing the operator network, for example, including functions such as mobile status management, allocation of user temporary identity, authentication and authorization of users, etc., and its name is not limited to this.

[0152] 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.

[0153] 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.

[0154] The embodiments of the present disclosure may be applied to long term evolution (LTE), LTE-advanced (LTE-A), LTE-beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 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), and the like. band, UWB), Bluetooth (registered trademark), public land mobile network (PLMN) network, device-to-device (D2D) system, machine-to-machine (M2M) system, Internet of Things (IoT) system, vehicle-to-everything (V2X), systems using other communication methods, and next-generation systems based on them. In addition, multiple systems can also be combined (for example, a combination of LTE or LTE-A and 5G) for application.

[0155] The widespread adoption of fifth-generation (5G) technology is bringing profound changes to every aspect of our lives. According to the International Telecommunication Union (ITU), 5G will permeate every aspect of future society, building a comprehensive, user-centric information ecosystem. 5G user experience data 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 a future growth of more than a thousand-fold in mobile traffic. 5G connection density can reach 1 million connections / 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. As a representative of new infrastructure, 5G will undoubtedly reshape the future information society.

[0156] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in numerous fields. The continued development of AI-based technologies in areas such as intelligent speech 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, bringing convenience to people's lives while also promoting industrial upgrades across various industries. AI technology is also rapidly intersecting with other disciplines, integrating knowledge from different disciplines while also providing new directions and methods for their development.

[0157] During Release 18 of the 3rd Generation Partnership Project (3GPP), a research project on artificial intelligence (AI) technology in the radio access network (RAN) was established. This project aims to investigate how to integrate AI into the radio air interface and explore how AI can assist in improving transmission technologies within the radio air interface.

[0158] In some embodiments, in the research of wireless AI technology, application cases of artificial intelligence include at least one of the following:

[0159] -AI-based CSI enhancement;

[0160] -AI-based beam management;

[0161] -AI-based positioning.

[0162] In some embodiments, since most AI-based communications can only be used in specific situations, when the wireless communication environment changes and is no longer suitable for AI-based communication or processing, the processing performance of the AI ​​model (or AI function) will decrease, resulting in a decrease 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 the AI ​​model (or AI function). On the other hand, when the terminal is in a non-AI state, when the communication environment is suitable for AI model (or AI function) processing, it is also necessary to perform performance monitoring in advance in order to turn on the processing based on the AI ​​model (or AI function) in order to improve the processing efficiency of the AI ​​model (or AI function).

[0163] In related technologies, how to monitor whether an inactive AI model is enabled is an urgent problem that needs to be solved.

[0164] Based on this, embodiments of the present disclosure provide a monitoring configuration method, a terminal, and a network device. The method, performed by the terminal, includes receiving first indication information sent by the network device, wherein the first indication information is used to instruct the terminal to monitor an AI operation, where the AI ​​operation is whether to enable an inactive AI model. Thus, the terminal can monitor whether the inactive AI model is enabled based on the first indication information from the network device, thereby facilitating the management of the AI ​​model and improving the efficiency of AI model processing.

[0165] Figure 2 is an interactive diagram of a monitoring configuration method according to an embodiment of the present disclosure. As shown in Figure 2, the embodiment of the present disclosure relates to a monitoring configuration method, which includes:

[0166] S201: A network device sends first indication information to a terminal.

[0167] In some embodiments, the terminal receives first indication information sent by the network device.

[0168] In some embodiments, the first indication information is used to instruct the terminal to monitor the AI ​​operation, where the AI ​​operation is whether to enable an inactive AI model.

[0169] In some embodiments, the first indication information is used to indicate whether to enable monitoring of an inactive AI model.

[0170] In some embodiments, the inactive AI model includes one or more AI models, and the one or more AI models are in an unused state.

[0171] In some embodiments, the inactivated AI model includes one or more AI models, and the one or more AI models are AI models configured on the terminal side, or the one or more AI models are AI models configured on the network device, or some of the multiple AI models are AI models configured on the terminal side, and some are AI models configured on the network device.

[0172] Exemplarily, the inactivated AI models include AI model #1, AI model #2 and AI model #3, wherein AI model #1 and AI model #2 are configured on the terminal side, AI model #3 is configured on the network device side, or AI model #1, AI model #2 and AI model #3 are all configured on the terminal side, or AI model #1, AI model #2 and AI model #3 are all configured on the network device side.

[0173] In some embodiments, the name of the first indication information is not limited, and it can be, for example, "first information", "first indication", etc.

[0174] In some embodiments, the first indication information includes an inactivated AI model to instruct the terminal to monitor whether the inactivated AI model is enabled.

[0175] In some embodiments, the inactive AI model is an unused AI model, or an inactive AI model, or an AI model that is suspended, or an AI model that is prohibited from use, and the like.

[0176] In some embodiments, the first indication information includes at least one of the following:

[0177] First signaling, where the first signaling is used to instruct the terminal on measurement resources used to monitor the AI ​​operation;

[0178] Second signaling, where the second signaling is used to instruct the terminal to use a reporting resource for reporting a monitoring result of monitoring the AI ​​operation;

[0179] The third signaling is used to indicate a trigger condition for the terminal to report a monitoring result of the AI ​​operation.

[0180] In an embodiment of the present disclosure, a network device sends first indication information to a terminal. The first indication information includes first signaling, and the first signaling is used to indicate measurement resources used by the terminal to monitor AI operations. Thus, upon receiving the first indication information, the terminal can monitor the AI ​​operations and use the measurement resources indicated by the first signaling to monitor the AI ​​operations.

[0181] In an embodiment of the present disclosure, a network device sends first indication information to a terminal. The first indication information includes second signaling, and the second signaling is used to instruct the terminal to use a reporting resource for reporting a monitoring result of an AI operation. Thus, upon receiving the first indication information, the terminal can monitor the AI ​​operation to determine a monitoring result, and can report the monitoring result of the AI ​​operation to the network device using the reporting resource indicated by the second signaling.

[0182] In an embodiment of the present disclosure, a network device sends first indication information to a terminal. The first indication information includes third signaling, and the third signaling is used to indicate a trigger condition for the terminal to report a monitoring result of an AI operation. Thus, upon receiving the first indication information, the terminal can monitor the AI ​​operation to determine a monitoring result, and can report the monitoring result of the AI ​​operation to the network device if the trigger condition indicated by the third signaling is met.

[0183] In some embodiments, the measurement resource is a periodic resource or an aperiodic resource.

[0184] In the embodiment of the present disclosure, the first signaling is used to indicate measurement resources used by the terminal to monitor the AI ​​operation, wherein the measurement resources are periodic resources or non-periodic resources.

[0185] In some embodiments, when the measurement resource is a periodic resource, the terminal may periodically monitor the AI ​​operation on the periodic resource.

[0186] In some embodiments, when the measurement resource is a non-periodic resource, the terminal may monitor the AI ​​operation aperiodically on the non-periodic resource.

[0187] In some embodiments, the monitoring results include at least one of the following:

[0188] RSRP measurement results;

[0189] CSI-RS measurement results;

[0190] PRS measurement results.

[0191] In the embodiment of the present disclosure, the monitoring result includes a measurement result of Reference Signal Received Power (RSRP).

[0192] In the embodiment of the present disclosure, the monitoring result includes a measurement result of a channel state information-reference signal (CSI-RS).

[0193] In the embodiment of the present disclosure, the monitoring result includes a measurement result of a positioning reference signal (PRS).

[0194] In some embodiments, the network device sends first indication information to the terminal, the first indication information including second signaling, the second signaling being used to instruct the terminal on reporting resources to be used for reporting RSRP measurement results. Thus, after receiving the first indication information, the terminal can monitor the AI ​​operation, and if the monitoring result of the AI ​​operation is determined to be the RSRP measurement result, the terminal can use the reporting resources to report the monitoring result of the RSRP measurement result to the network device.

[0195] In some embodiments, the network device sends first indication information to the terminal, the first indication information including second signaling, the second signaling being used to instruct the terminal on reporting resources to be used for reporting CSI-RS measurement results. Thus, after receiving the first indication information, the terminal can monitor the AI ​​operation, and if the AI ​​operation determines that the monitoring result is a CSI-RS measurement result, the terminal can use the reporting resources to report the monitoring result of the CSI-RS measurement result to the network device.

[0196] In some embodiments, the network device sends first indication information to the terminal, the first indication information including second signaling, the second signaling being used to instruct the terminal on reporting resources to be used for reporting PRS measurement results. Thus, after receiving the first indication information, the terminal can monitor the AI ​​operation, and, if the AI ​​operation is determined to be a monitoring result of the PRS measurement result, can use the reporting resources to report the monitoring result of the PRS measurement result to the network device.

[0197] In some embodiments, the triggering condition includes at least one of the following:

[0198] The monitoring result is lower than the first threshold;

[0199] The moving speed of the terminal in the monitoring result is greater than the second threshold.

[0200] In the embodiment of the present disclosure, the trigger condition includes a monitoring result being lower than a first threshold.

[0201] It should be noted that the value of the first threshold can be set as needed, and the embodiment of the present disclosure does not impose any specific limitation on this.

[0202] In some embodiments, when the monitoring result is a measurement result of RSRP, the trigger condition may include that the measurement result of RSRP is lower than a first threshold.

[0203] In some embodiments, when the monitoring result is a CSI-RS measurement result, the trigger condition may include that the RSRP measurement result is lower than a first threshold.

[0204] In some embodiments, when the monitoring result is a measurement result of a PRS, the trigger condition may include that the measurement result of an RSRP is lower than a first threshold.

[0205] In the embodiment of the present disclosure, the trigger condition includes that the moving speed of the terminal in the monitoring result is greater than the second threshold.

[0206] It should be noted that the value of the second threshold can be set as needed, and the embodiment of the present disclosure does not impose any specific limitation on this.

[0207] In some embodiments, the network device sends first indication information to the terminal, the first indication information including third signaling, the third signaling being used to indicate a trigger condition for the terminal to report a monitoring result of monitoring the AI ​​operation, where the trigger condition is that the monitoring result is below a first threshold. Thus, after receiving the first indication information, the terminal can monitor the AI ​​operation to determine the monitoring result, and report the monitoring result of the AI ​​operation to the network device if the trigger condition indicated by the third signaling is met, i.e., the monitoring result is below the first threshold.

[0208] In some embodiments, a network device sends first indication information to a terminal, the first indication information including third signaling, the third signaling being used to indicate a trigger condition for the terminal to report a monitoring result of monitoring an AI operation, the trigger condition being that the terminal's movement speed in the monitoring result is greater than a second threshold. Thus, after receiving the first indication information, the terminal can monitor the AI ​​operation to determine a monitoring result, and report the monitoring result of the AI ​​operation to the network device if the trigger condition indicated by the third signaling is met, i.e., if the terminal's movement speed in the monitoring result is greater than the second threshold.

[0209] In some embodiments, the first indication information includes first signaling, and the above method further includes: the terminal monitoring the AI ​​operation on the measurement resource according to the first signaling.

[0210] In an embodiment of the present disclosure, a network device sends a first signaling to a terminal, where the first signaling is used to indicate the measurement resources used by the terminal to monitor the AI ​​operation. Thus, upon receiving the first signaling sent by the network device, the terminal can determine the measurement resources for monitoring the AI ​​operation, and thus the terminal can monitor the AI ​​operation on the measurement resources.

[0211] In some embodiments, the first indication information includes second signaling, and the above method further includes: the terminal sending the monitoring result to the network device on the reporting resource.

[0212] In an embodiment of the present disclosure, the network device sends a second signaling to the terminal, and the second signaling is used to instruct the terminal to use a reporting resource for reporting the monitoring results of the monitoring AI operation. Thus, after receiving the second signaling, the terminal can determine the reporting resource used to send the monitoring results to the network device, and then the terminal can send the monitoring results to the network device on the reporting resource.

[0213] In some embodiments, the first indication information includes third signaling, and the above method further includes: the terminal determines that the trigger condition is met and sends the monitoring result to the network device.

[0214] In an embodiment of the present disclosure, the network device sends a first indication message to the terminal, and the first indication message includes a third signaling. The third signaling is used to indicate the triggering condition for the terminal to report the monitoring result of the monitoring AI operation. Thus, when the terminal receives the third signaling sent by the network device, it can determine the triggering condition that needs to be met to report the monitoring result of the monitoring AI operation. Then, when it is determined that the triggering condition is met, the terminal can send the monitoring result to the network device.

[0215] In some embodiments, the terminal receives first indication information sent by the network device. When the first indication information is used to instruct the terminal to monitor the AI ​​operation, and the AI ​​operation is whether to enable an inactivated AI model, it can be determined to monitor the AI ​​operation and whether to enable the inactivated AI model.

[0216] In some embodiments, the network device sends the first indication information to the terminal on its own, or sends the first indication information to the terminal based on a request of the terminal, or sends the first indication information to the terminal when it is determined that a specified condition is met.

[0217] In some embodiments, the network device sends a radio resource control (RRC) to the terminal, where the RRC includes first indication information.

[0218] In some embodiments, the network device sends downlink control information (DCI) to the terminal, where the DCI includes first indication information.

[0219] In some embodiments, the network device sends a Medium Access Control Control Element (MAC CE) to the terminal, where the MAC CE includes first indication information.

[0220] In some embodiments, AI model and AI function have the same meaning and can be interchangeable.

[0221] In some embodiments, the terminal monitors the AI ​​operation according to the first indication information and determines the monitoring result.

[0222] In the embodiment of the present disclosure, the terminal receives the first indication information sent by the network device and can determine to monitor the AI ​​operation based on the first indication information. Based on this, the monitoring result of the AI ​​operation can be determined.

[0223] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be monitoring the position coordinates measured by the terminal, and determining the error between the position coordinates measured by the terminal and the specified position coordinates.

[0224] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be a non-line of sight (NLOS) path determined by monitoring a channel impulse response measured by the terminal.

[0225] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be a measurement result of the terminal measuring RSRP in AI-based beam management.

[0226] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be a measurement result of the terminal measuring the CSI-RS in the AI-based CSI.

[0227] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be a measurement result of the terminal measuring the PRS during AI positioning.

[0228] Of course, the above examples are only for illustration, and the monitoring results of the terminal monitoring the AI ​​operation can also be other results, and the embodiments of the present disclosure do not impose specific limitations on this.

[0229] In some embodiments, the terminal determines whether to enable an inactivated AI model based on the monitoring results.

[0230] In an embodiment of the present disclosure, when the terminal device receives the first indication information sent by the network device and monitors the AI ​​operation to determine the monitoring result, it can determine whether to enable the inactivated AI model based on the monitoring result.

[0231] In some embodiments, the terminal determines to enable an inactivated AI model based on the monitoring results.

[0232] Exemplarily, the inactivated AI model is AI model #1, that is, when AI model #1 is in an unused state, the terminal determines to activate the inactivated AI model and may determine to activate AI model #1.

[0233] It should be noted that enabling AI model #1 can be understood as activating AI model #1, that is, it can be understood as using AI model #1.

[0234] In some embodiments, the terminal determines not to enable the inactivated AI model based on the monitoring results.

[0235] In some embodiments, when an inactivated AI model is configured on the terminal side, if the terminal determines to enable the inactivated AI model based on monitoring results, the terminal can directly enable the inactivated AI model configured on the terminal side.

[0236] In some embodiments, when an inactivated AI model is configured on the terminal side, if the terminal determines not to enable the inactivated AI model based on monitoring results, the terminal may not enable the inactivated AI model configured on the terminal side.

[0237] In some embodiments, when the inactivated AI model is not configured on the terminal side but on the network device side, if the terminal determines to enable the inactivated AI model based on the monitoring results, the terminal can send an instruction to the network device to instruct the network device to enable the inactivated AI model.

[0238] In some embodiments, when the inactivated AI model is not configured on the terminal side but on the network device side, if the terminal determines not to enable the inactivated AI model based on the monitoring results, the terminal can send an instruction to the network device, instructing the network device not to enable the inactivated AI model.

[0239] In some embodiments, there are multiple inactivated AI models, and some are configured on the terminal side and some are configured on the network device side. If the terminal determines to enable the inactivated AI model based on the monitoring results, the terminal can enable the AI ​​model configured on the terminal side among the inactivated AI models, and send an indication to the network device, instructing the network device to enable the AI ​​model configured on the network device side among the inactivated AI models.

[0240] In some embodiments, there are multiple inactivated AI models, and some are configured on the terminal side and some are configured on the network device side. If the terminal determines not to enable the inactivated AI model based on the monitoring results, the terminal may not enable the AI ​​model configured on the terminal side among the inactivated AI models, and send an indication to the network device, instructing the network device not to enable the AI ​​model configured on the network device side among the inactivated AI models.

[0241] In some embodiments, the terminal determines to enable an inactive AI model at a specific moment based on the monitoring results.

[0242] In some embodiments, the terminal determines, based on monitoring results, to enable an inactive AI model under specific circumstances.

[0243] In some embodiments, the terminal determines whether to enable the inactivated AI model based on the monitoring results, including: determining the current usage environment based on the monitoring results; and determining whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the current usage environment.

[0244] In the embodiment of the present disclosure, the terminal determines whether to enable the inactivated AI model based on the monitoring results, and can determine the current usage environment based on the monitoring results; and determines whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the current usage environment.

[0245] In some embodiments, when the terminal determines that the inactivated AI model is applicable to the current usage environment, the terminal determines to enable the inactivated AI model.

[0246] In some embodiments, when the terminal determines that the inactivated AI model is not applicable to the current usage environment, it determines not to enable the inactivated AI model.

[0247] In some embodiments, when the terminal determines that the inactivated AI model is not applicable to the current usage environment, the terminal determines to activate the inactivated AI model.

[0248] In some embodiments, when the terminal determines that the inactivated AI model is applicable to the current usage environment, it determines not to enable the inactivated AI model.

[0249] Exemplarily, the terminal determines the current usage environment based on the monitoring result, for example, determines whether the current usage environment is an indoor environment or an outdoor environment.

[0250] Among them, when it is determined that the current usage environment is an indoor environment, it can be determined whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the indoor environment. If the inactivated AI model is applicable to the indoor environment, it can be determined to enable the inactivated AI model, or if the inactivated AI model is not applicable to the indoor environment, it can be determined not to enable the inactivated AI model.

[0251] Among them, when it is determined that the current usage environment is an outdoor environment, it can be determined whether to enable the inactivated AI model based on whether the inactivated AI model is applicable to the outdoor environment. If the inactivated AI model is applicable to the outdoor environment, it can be determined to enable the inactivated AI model, or if the inactivated AI model is not applicable to the outdoor environment, it can be determined not to enable the inactivated AI model.

[0252] Exemplarily, the monitoring result of the terminal monitoring the AI ​​operation may be a non-line of sight (NLOS) path determined by monitoring a channel impulse response measured by the terminal.

[0253] Among them, the current use environment is determined according to the NLOS path situation in the detection result. For example, when there are many NLOS paths, the current use environment is determined to be an indoor environment. When there are relatively few NLOS paths, the current use environment is determined to be an outdoor environment.

[0254] In some embodiments, the terminal determines to enable an inactivated AI model, and determines a processing result based on the monitoring result and the local AI model of the terminal, wherein the inactivated AI model includes the local AI model of the terminal.

[0255] In an embodiment of the present disclosure, when the inactivated AI model includes a local AI model of the terminal, if the terminal determines to enable the inactivated AI model, it can determine the processing result based on the monitoring result and the local AI model of the terminal.

[0256] In some embodiments, the terminal determines the processing result based on the monitoring result and the local AI model of the terminal. The monitoring result can be input into the local AI model of the terminal to obtain the output result of the AI ​​model. The terminal can determine the output result of the AI ​​model as the processing result.

[0257] In some embodiments, the monitoring results include at least one of the RSRP measurement results, the CSI-RS measurement results, and the PRS measurement results. The terminal can input at least one of the RSRP measurement results, the CSI-RS measurement results, and the PRS measurement results into the local AI model of the terminal to obtain the output result of the local AI model, and determine that the output result is the processing result.

[0258] In some embodiments, the terminal determines statistical results that meet the first condition based on the monitoring results.

[0259] In the embodiment of the present disclosure, when the terminal monitors the AI ​​operation according to the first indication information sent by the receiving network device and obtains the monitoring results, the terminal can determine the statistical results that meet the first condition based on the monitoring results.

[0260] In some embodiments, the first condition is that a reference signal received power RSRP is greater than a first value during a measurement period.

[0261] In the embodiment of the present disclosure, the terminal may determine, based on the monitoring result, a statistical result satisfying that the RSRP is greater than the first value.

[0262] S202: The terminal sends a first message to the network device.

[0263] In some embodiments, the network device receives a first message sent by the terminal.

[0264] In some embodiments, the first message is used to indicate whether to enable an inactive AI model.

[0265] In some embodiments, the first message is used to instruct activation of an inactive AI model.

[0266] In some embodiments, when an inactivated AI model is configured on the network device side, the terminal monitors the AI ​​operation and determines the monitoring results based on the first indication information sent by the network device. When it is determined to enable the inactivated AI model based on the monitoring results, the terminal sends a first message to the network device, and the first message is used to indicate the activation of the inactivated AI model.

[0267] In some embodiments, the first message is used to indicate that an inactive AI model is not to be enabled.

[0268] In some embodiments, when an inactivated AI model is configured on the network device side, the terminal monitors the AI ​​operation and determines the monitoring results based on the first indication information sent by the network device. When it is determined not to enable the inactivated AI model based on the monitoring results, the terminal sends a first message to the network device, and the first message is used to indicate that the inactivated AI model is not enabled.

[0269] It should be noted that when an inactivated AI model is configured on the terminal side, the terminal monitors the AI ​​operation based on the first indication information sent by the network device to determine the monitoring results. When it is determined to enable the inactivated AI model based on the monitoring results, the inactivated AI model configured on the terminal side can be enabled. At this time, the terminal may not send the first message to the network device to indicate whether to enable the inactivated AI model.

[0270] It should be noted that when an inactivated AI model is configured on the terminal side, the terminal monitors the AI ​​operation according to the first indication information sent by the network device to determine the monitoring results. If it is determined not to enable the inactivated AI model according to the monitoring results, the inactivated AI model configured on the terminal side may not be enabled. At this time, the terminal may not send the first message to the network device to indicate whether to enable the inactivated AI model.

[0271] In some embodiments, the first message includes an inactivated AI model to indicate that the inactivated AI model is enabled.

[0272] In some embodiments, the name of the first message is not limited, and may be, for example, "specific information", "designated message", etc.

[0273] In some embodiments, the first message includes at least one of the following:

[0274] Monitoring results, where the monitoring results are determined by the terminal through monitoring of the AI ​​operation according to the first indication information;

[0275] First information, wherein the first information is used to indicate whether to enable an inactive AI model;

[0276] Processing result, where the processing result is that the terminal determines to activate an inactivated AI model, which is determined based on the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal;

[0277] Statistical results, where the statistical results are statistical results that the terminal determines meet the first condition based on the monitoring results.

[0278] In an embodiment of the present disclosure, a terminal sends a first message to a network device, the first message including a monitoring result, wherein the monitoring result is determined by the terminal by monitoring the AI ​​operation according to the first indication information. Thus, upon receiving the first message including the monitoring result sent by the terminal, the network device can determine whether to enable an inactive AI model, and / or whether to continue monitoring the AI ​​operation, and / or whether to stop monitoring the AI ​​operation.

[0279] In an embodiment of the present disclosure, a terminal sends a first message to a network device, the first message including first information, wherein the first information is used to indicate whether to enable an inactive AI model. Thus, upon receiving the first message including the first information sent by the terminal, the network device can determine whether to enable the inactive AI model, and / or whether to continue monitoring the AI ​​operation, and / or whether to stop monitoring the AI ​​operation.

[0280] In some embodiments, the terminal determines whether to enable an inactivated AI model based on the monitoring results.

[0281] In an embodiment of the present disclosure, a terminal sends a first message to a network device, the first message including a processing result, wherein the processing result is a determination by the terminal to enable an inactive AI model, determined based on a monitoring result and a local AI model of the terminal, where the inactive AI model includes the local AI model of the terminal. Thus, upon receiving the first message including the processing result sent by the terminal, the network device can determine whether to enable the inactive AI model, and / or whether to continue monitoring the AI ​​operation, and / or whether to stop monitoring the AI ​​operation.

[0282] In some embodiments, the terminal determines the processing result based on the monitoring result and the local AI model of the terminal. The monitoring result may, for example, include at least one of the measurement result of RSRP, the measurement result of CSI-RS, and the measurement result of PRS. The terminal may input at least one of the measurement result of RSRP, the measurement result of CSI-RS, and the measurement result of PRS into the local AI model of the terminal to obtain the output result of the local AI model, and determine that the output result is the processing result. Furthermore, the terminal may send the processing result to the network device, wherein the processing result may be used as input data of the AI ​​model on the network device side. The network device may obtain the output result of the AI ​​model on the network device side based on the processing result, and then determine whether to enable the inactivated AI model according to the output result of the AI ​​model on the network device side, and / or determine whether to continue monitoring the AI ​​operation, and / or determine whether to stop monitoring the AI ​​operation.

[0283] In an embodiment of the present disclosure, a terminal sends a first message to a network device, the first message including statistical results, wherein the statistical results are statistical results determined by the terminal to satisfy a first condition based on monitoring results. Thus, upon receiving the first message including the statistical results sent by the terminal, the network device can determine whether to enable an inactive AI model, and / or whether to continue monitoring the AI ​​operation, and / or whether to stop monitoring the AI ​​operation.

[0284] In some embodiments, the first condition is that a reference signal received power RSRP is greater than a first value during a measurement period.

[0285] In the embodiment of the present disclosure, the terminal determines, based on the monitoring result, a statistical result that satisfies RSRP being greater than a first value.

[0286] It should be noted that the above embodiments are not exhaustive and are only illustrations of some embodiments. The first message sent by the terminal to the network device in the embodiment of the present disclosure may also include the above multiple embodiments, and the embodiment of the present disclosure does not impose specific limitations on this.

[0287] S203: The network device determines a target operation according to the first message.

[0288] In some embodiments, the network device receives the first message sent by the terminal, or the network device may also receive the first message sent by other entities.

[0289] In the embodiment of the present disclosure, when the network device determines the first message, it can determine the target operation according to the first message.

[0290] In some embodiments, the first message includes at least one of the following:

[0291] Monitoring results, where the monitoring results are determined by the terminal through monitoring of the AI ​​operation according to the first indication information;

[0292] First information, wherein the first information is used to indicate whether to enable an inactive AI model;

[0293] Processing result, where the processing result is that the terminal determines to activate an inactivated AI model, which is determined based on the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal;

[0294] Statistical results, where the statistical results are statistical results that the terminal determines meet the first condition based on the monitoring results.

[0295] In some embodiments, the network device may determine the target operation based on a first message including the monitoring result sent by the terminal.

[0296] In some embodiments, the network device may determine the target operation based on a first message including first information sent by the terminal.

[0297] In some embodiments, the network device may determine the target operation according to the first message including the processing result sent by the terminal.

[0298] In some embodiments, the network device may determine the target operation based on the first message including the statistical result sent by the terminal.

[0299] In some embodiments, the target operation includes at least one of the following:

[0300] Whether to enable inactive AI models;

[0301] Whether AI operations need to continue to be monitored;

[0302] Whether to stop monitoring AI operations.

[0303] In the embodiment of the present disclosure, the network device determines the target operation based on the first message and can determine whether to enable the inactivated AI model.

[0304] In some embodiments, when the inactivated AI model is configured on the network device side, the network device receives the first message sent by the terminal and can determine whether to enable the inactivated AI model.

[0305] In some embodiments, when the inactivated AI model is a plurality of AI models and some of the AI ​​models are configured on the network device side, the network device receives the first message sent by the terminal and can determine whether to enable the AI ​​model configured on the network device side among the inactivated AI models.

[0306] In the embodiment of the present disclosure, the network device determines the target operation based on the first message and can determine whether it is necessary to continue monitoring the AI ​​operation.

[0307] In some embodiments, when the network device determines to enable an inactivated AI model based on the first message, it may determine that it is not necessary to continue monitoring the AI ​​operation.

[0308] In some embodiments, the network device may determine that it is necessary to continue monitoring the AI ​​operation if it determines not to enable the inactivated AI model based on the first message.

[0309] In some embodiments, the network device may determine that it is necessary to continue monitoring the AI ​​operation if it determines not to enable the inactivated AI model based on the first message.

[0310] In the embodiment of the present disclosure, the network device determines the target operation based on the first message and can determine whether it is necessary to stop monitoring the AI ​​operation.

[0311] In some embodiments, when the network device determines, based on the first message, that an inactivated AI model is enabled, it may determine that it is necessary to stop monitoring the AI ​​operation.

[0312] In some embodiments, when the network device determines not to enable the inactivated AI model based on the first message, it may determine that it is not necessary to stop monitoring the AI ​​operation.

[0313] In some embodiments, the network device determines to enable an inactive AI model based on the first message.

[0314] In some embodiments, the network device determines not to enable the inactive AI model based on the first message.

[0315] In some embodiments, the network device determines, based on the first message, that it is necessary to continue monitoring the AI ​​operation.

[0316] In some embodiments, the network device determines, based on the first message, that it is necessary to stop monitoring the AI ​​operation.

[0317] In some embodiments, the inactivated AI model includes an AI model of a network device, and the above method further includes: the network device determines to enable the inactivated AI model, and determines to use the AI ​​model of the network device for processing.

[0318] In an embodiment of the present disclosure, when the inactivated AI model includes an AI model of a network device, if the network device determines to enable the inactivated AI model, the network device may determine to use the AI ​​model of the network device for processing.

[0319] In some embodiments, when an inactivated AI model is configured on the network device side, the network device receives a first message sent by the terminal. When the first message indicates to enable the inactivated AI model, the network device determines to enable the inactivated AI model and uses the inactivated AI model configured on the network device side for processing.

[0320] In some embodiments, the inactivated AI model is a plurality of AI models, some of which are configured on the terminal side and some of which are configured on the network device side. The network device receives a first message sent by the terminal. When the first message indicates to enable the inactivated AI model, the network device determines to enable the AI ​​model configured on the network device side among the inactivated AI models and uses the AI ​​model configured on the network device side for processing.

[0321] S204: The network device sends second indication information to the terminal.

[0322] In some embodiments, the terminal receives second indication information sent by the network device.

[0323] In some embodiments, the second indication information is used to indicate at least one of the following:

[0324] Enable inactive AI models;

[0325] Continue to monitor AI operations;

[0326] Stop monitoring AI operations.

[0327] In an embodiment of the present disclosure, the network device sends second indication information to the terminal, where the second indication information is used to instruct the terminal to enable an inactivated AI model.

[0328] In an embodiment of the present disclosure, the network device sends second indication information to the terminal, where the second indication information is used to instruct the terminal to continue monitoring the AI ​​operation.

[0329] In an embodiment of the present disclosure, the network device sends second indication information to the terminal, where the second indication information is used to instruct the terminal to stop monitoring the AI ​​operation.

[0330] In some embodiments, the network device may send the second indication information to the terminal on its own, or may send the second indication information to the terminal based on relevant indications of the terminal, or may send the second indication information to the terminal when the first condition is met. The embodiments of the present disclosure do not impose specific restrictions on this.

[0331] In some embodiments, the network device sends second indication information to the terminal according to the target operation.

[0332] In some embodiments, the network device may determine the target operation based on an instruction from the terminal, or may determine the target operation on its own, or may determine the target operation based on a protocol agreement.

[0333] In some embodiments, the network device determines the target operation according to the first message sent by the terminal.

[0334] In some embodiments, when the target operation is to enable an inactivated AI model, the second indication information sent by the network device to the terminal may indicate to enable the inactivated AI model.

[0335] In some embodiments, when the target operation is to continue monitoring the AI ​​operation, the second indication information sent by the network device to the terminal may indicate to continue monitoring the AI ​​operation.

[0336] In some embodiments, when the target operation is to stop monitoring the AI ​​operation, the second indication information sent by the network device to the terminal may indicate to stop monitoring the AI ​​operation.

[0337] 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.

[0338] In some embodiments, the terms "downlink control information (DCI)", "downlink (DL) assignment", "DL DCI", "uplink (UL) grant", "UL DCI" and the like may be used interchangeably.

[0339] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0340] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0341] 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.

[0342] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0343] 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.

[0344] 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.

[0345] The communication method involved in the embodiments of the present disclosure may include at least one of S201 to S204. For example, S201 can be implemented as an independent embodiment, S202 can be implemented as an independent embodiment, S203 can be implemented as an independent embodiment, S204 can be implemented as an independent embodiment, S201+S202 can be implemented as an independent embodiment, S201+S202+S203 can be implemented as an independent embodiment, and S201+S204 can be implemented as an independent embodiment, but the present invention is not limited thereto.

[0346] In some embodiments, S202, S203 and S204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0347] In some embodiments, S202 and S203 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0348] In some embodiments, S203 and S204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0349] In some embodiments, S204 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0350] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 .

[0351] FIG3A is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0352] S301A, obtain first indication information.

[0353] Among them, the optional implementation of S301A can refer to the optional implementation of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0354] In some embodiments, the terminal receives the first indication information sent by the network device, but is not limited thereto, and may also receive the first indication information sent by other entities.

[0355] In some embodiments, the terminal obtains first indication information specified by a protocol.

[0356] In some embodiments, the terminal obtains the first indication information from an upper layer(s).

[0357] In some embodiments, the terminal performs processing to obtain the first indication information.

[0358] In some embodiments, S301A is omitted, and the terminal autonomously implements the function indicated by the first indication information, or the above function is default or acquiescent.

[0359] In some embodiments, the first indication information is used to instruct the terminal to monitor the AI ​​operation, where the AI ​​operation is whether to enable an inactive AI model.

[0360] S302A, sending a first message.

[0361] Among them, the optional implementation of S302A can refer to the optional implementation of S202 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0362] In some embodiments, the terminal sends the first message to the network device, but is not limited thereto, and the first message may also be sent to other entities.

[0363] In some embodiments, the first message is used by the network device to determine the target operation. Optional implementations thereof can be found in the optional implementations of S203 in FIG2 and other related parts in the embodiment involved in FIG2 , which will not be described in detail here.

[0364] S303A, obtain second indication information.

[0365] Among them, the optional implementation of S303A can refer to the optional implementation of S204 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0366] In some embodiments, the terminal receives the second indication information sent by the network device, but is not limited thereto, and may also receive the second indication information sent by other entities.

[0367] In some embodiments, the terminal obtains second indication information specified by the protocol.

[0368] In some embodiments, the terminal obtains the second indication information from an upper layer(s).

[0369] In some embodiments, the terminal performs processing to obtain the second indication information.

[0370] In some embodiments, S303A is omitted, and the terminal autonomously implements the function indicated by the second indication information, or the above function is default or acquiescent.

[0371] The communication method involved in the embodiments of the present disclosure may include at least one of S301A to S303A. For example, S301A may be implemented as an independent embodiment, S302A may be implemented as an independent embodiment, S303A may be implemented as an independent embodiment, S301A+S302A may be implemented as an independent embodiment, and S301A+S303A may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0372] In some embodiments, S302A and S303A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0373] In some embodiments, S302A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0374] In some embodiments, S303A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0375] By implementing the embodiments of the present disclosure, the terminal can monitor whether the inactivated AI model is enabled based on the first indication information of the network device, thereby facilitating the management of the AI ​​model and improving the efficiency of processing using the AI ​​model.

[0376] FIG3B is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0377] S301B, obtain first indication information.

[0378] S302B: Monitor the AI ​​operation according to the first instruction information and determine the monitoring result.

[0379] S303B: Determine whether to enable the inactive AI model based on the monitoring results.

[0380] S304B: Determine to enable an inactivated AI model, and determine a processing result based on the monitoring result and the local AI model of the terminal, wherein the inactivated AI model includes the local AI model of the terminal.

[0381] Among them, the optional implementation methods of S301A to S304A can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0382] The communication method involved in the embodiments of the present disclosure may include at least one of S301B to S304B. For example, S301B may be implemented as an independent embodiment, S302B may be implemented as an independent embodiment, S303B may be implemented as an independent embodiment, S304B may be implemented as an independent embodiment, S301B+S302B may be implemented as an independent embodiment, S301B+S302B+S303B may be implemented as an independent embodiment, and S301B+S302B+S304B may be implemented as an independent embodiment, but are not limited thereto.

[0383] In some embodiments, S301B, S302B, S303B, and S304B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0384] In some embodiments, S302B, S303B, and S304B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0385] In some embodiments, S303B and S304B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0386] In some embodiments, S303B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0387] In some embodiments, S304B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0388] FIG3C is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0389] S301C, obtain first indication information.

[0390] S302C: Monitor the AI ​​operation according to the first instruction information and determine the monitoring result.

[0391] S303C: Determine statistical results that meet the first condition based on the monitoring results.

[0392] Among them, the optional implementation methods of S301C to S303C can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0393] The communication method involved in the embodiments of the present disclosure may include at least one of S301C to S303C. For example, S301C may be implemented as an independent embodiment, S302C may be implemented as an independent embodiment, S303C may be implemented as an independent embodiment, and S301C+S302C may be implemented as independent embodiments, but the present invention is not limited thereto.

[0394] In some embodiments, S302C and S303C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0395] In some embodiments, S303C is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0396] FIG3D is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0397] S301D: Obtain first indication information, where the first indication information includes first signaling, and the first signaling is used to instruct the terminal on measurement resources used to monitor the AI ​​operation.

[0398] S302D: Monitor the AI ​​operation on the measurement resource according to the first signaling.

[0399] Among them, the optional implementation methods of S301D to S302D can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0400] The communication method involved in the embodiments of the present disclosure may include at least one of S301D to S302D. For example, S301D may be implemented as an independent embodiment, and S302D may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0401] In some embodiments, S302D is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0402] FIG3E is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0403] S301E: Obtain first indication information, where the first indication information includes second signaling, and the second signaling is used to instruct the terminal to send a reporting resource used for monitoring a monitoring result of an AI operation.

[0404] S302E: Send monitoring results to the network device in the reporting resource.

[0405] Among them, the optional implementation methods of S301E to S302E can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0406] The communication method involved in the embodiments of the present disclosure may include at least one of S301E to S302E. For example, S301E may be implemented as an independent embodiment, and S302E may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0407] In some embodiments, S302E is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0408] FIG3F is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG3F , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a terminal and includes:

[0409] S301F: Obtain first indication information, where the first indication information includes third signaling, and the third signaling is used to indicate a trigger condition for the terminal to send a monitoring result of monitoring the AI ​​operation.

[0410] S302F: Determine that the trigger condition is met and send the monitoring result to the network device.

[0411] Among them, the optional implementation methods of S301F to S302F can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0412] The communication method involved in the embodiments of the present disclosure may include at least one of S301F to S302F. For example, S301F may be implemented as an independent embodiment, and S302F may be implemented as an independent embodiment, but the present disclosure is not limited thereto.

[0413] In some embodiments, S302F is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0414] FIG4A is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a network device and includes:

[0415] S401A, sending first indication information.

[0416] Among them, the optional implementation of S401A can refer to the optional implementation of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0417] In some embodiments, the network device sends the first indication information to the terminal, but is not limited thereto, and the first indication information may also be sent to other entities.

[0418] In some embodiments, the first indication information is used to instruct the terminal to monitor the AI ​​operation, where the AI ​​operation is whether to enable an inactive AI model.

[0419] S402A, obtain the first message.

[0420] Among them, the optional implementation of S402A can refer to the optional implementation of S202 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0421] In some embodiments, the network device receives the first message sent by the terminal, but is not limited thereto and may also receive the first message sent by other entities.

[0422] In some embodiments, the network device obtains a first message specified by a protocol.

[0423] In some embodiments, the network device obtains the first message from an upper layer(s).

[0424] In some embodiments, the network device performs processing to obtain the first message.

[0425] In some embodiments, S402A is omitted, and the network device autonomously implements the function indicated by the first message, or the above function is default or by default.

[0426] In some embodiments, the first message is used by the network device to determine the target operation. Optional implementations thereof can be found in the optional implementations of S203 in FIG2 and other related parts in the embodiment involved in FIG2 , which will not be described in detail here.

[0427] S403A: Determine a target operation according to the first message.

[0428] Among them, the optional implementation of S403A can refer to the optional implementation of S203 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0429] S404A, sending second indication information.

[0430] Among them, the optional implementation of S404A can refer to the optional implementation of S204 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0431] In some embodiments, the network device sends the second indication information to the terminal, but is not limited thereto, and the second indication information may also be sent to other entities.

[0432] The communication method involved in the embodiments of the present disclosure may include at least one of S401A to S404A. For example, S401A may be implemented as an independent embodiment, S402A may be implemented as an independent embodiment, S403 may be implemented as an independent embodiment, S404A may be implemented as an independent embodiment, S401A+S402A may be implemented as an independent embodiment, S401A+S402A+S403A may be implemented as an independent embodiment, and S401A+S404A may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0433] In some embodiments, S402A, S403A, and S404A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0434] In some embodiments, S402A and S403A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0435] In some embodiments, S403A and S404A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0436] In some embodiments, S404A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0437] FIG4B is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a network device and includes:

[0438] S401B: Send first indication information, where the first indication information includes second signaling, and the second signaling is used to instruct the terminal to send a reporting resource used for monitoring the AI ​​operation.

[0439] S402B: receiving the monitoring result sent by the terminal on the reporting resource.

[0440] Among them, the optional implementation methods of S401B to S402B can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0441] The communication method involved in the embodiments of the present disclosure may include at least one of S401B to S402B. For example, S401B may be implemented as an independent embodiment, and S402B may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0442] In some embodiments, S402B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0443] FIG4C is a flow chart of a monitoring configuration method according to an embodiment of the present disclosure. As shown in FIG4C , the embodiment of the present disclosure relates to a monitoring configuration method, which is executed by a network device and includes:

[0444] S401C: Send first indication information, where the first indication information includes third signaling, and the third signaling is used to indicate a trigger condition for the terminal to report a monitoring result of monitoring the AI ​​operation.

[0445] S402C: receiving the monitoring result sent by the terminal when determining that the trigger condition is met.

[0446] Among them, the optional implementation methods of S401C to S402C can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0447] The communication method involved in the embodiment of the present disclosure may include at least one of S401C to S402C. For example, S401C may be implemented as an independent embodiment, and S402C may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0448] In some embodiments, S402C is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0449] To facilitate understanding of the embodiments of the present disclosure, the following exemplary embodiments are provided.

[0450] In the exemplary embodiment, description is given by taking the case where the first indication information is the first signaling and the second indication information is the second signaling as an example.

[0451] In some embodiments, the network device sends a first signaling, where the first signaling includes at least one of the following information:

[0452] -Notify the terminal whether to start monitoring based on AI operations, that is, to monitor inactive AI models or AI functions;

[0453] -Monitoring configuration signaling, such as the measurement resources used by the terminal for monitoring, or the resources for the terminal to report monitoring results (such as whether to start AI-based processing), the triggering conditions for the terminal to report, etc.

[0454] In some embodiments, the terminal performs performance monitoring based on the monitoring configuration signaling sent by the network device, and the monitoring action includes one of the following:

[0455] - Based on the measurement resource, one or more measurements are performed, which can be periodic or aperiodic;

[0456] -If the monitoring results are obtained, use the local AI function or AI model to perform AI processing based on the monitoring results to obtain the corresponding processing results

[0457] -If monitoring results are obtained, the current usage environment is determined locally based on the monitoring results, such as whether it is an indoor environment or an outdoor environment, and whether the inactivated AI model is applicable to the current usage environment.

[0458] In some embodiments, the terminal reports at least one of the following:

[0459] -The terminal sends the monitoring results to the network device;

[0460] -The terminal sends the processing results of the AI ​​function or AI model to the network device;

[0461] - The terminal processes the monitoring results, determines whether to enable AI-based processing based on the monitoring results, and reports the judgment results to the network device, such as whether to enable the AI ​​function or AI model, and which AI function or AI model to enable;

[0462] -The terminal sends statistical results that meet preset conditions to the network device, such as the number of measurements with RSRP greater than X within the measurement period, or the accuracy of AI functions or AI model reasoning.

[0463] In some embodiments, the network device performs at least one of the following processing based on the information reported by the terminal:

[0464] - Based on the monitoring results reported by AI or the processing results based on AI, the network device uses the corresponding AI model on the network side for processing;

[0465] -Based on the monitoring results of the terminal or the judgment results of the terminal, determine whether to enable the AI ​​model or AI function or whether to continue monitoring or turn off monitoring.

[0466] In some embodiments, the network device sends a second signaling to the terminal, where the second signaling is used to indicate at least one of the following:

[0467] - Activate AI-based processing;

[0468] -The terminal continues to perform performance monitoring;

[0469] -Terminal turns off performance monitoring.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] FIG5A is a schematic diagram of the structure of a terminal according to an embodiment of the present disclosure. As shown in FIG5A , the terminal 10 may include at least one of a transceiver module 11 and a processing module 12 .

[0474] In some embodiments, the transceiver module 11 is configured to receive first indication information sent by a network device, wherein the first indication information is configured to instruct the terminal to monitor an AI operation, where the AI ​​operation is whether to enable an inactive AI model.

[0475] The transceiver module 11 is configured to execute at least one of the communication steps (e.g., S201 to S204, but not limited thereto) such as sending and / or receiving performed by the terminal 10 in any of the above methods, and will not be described in detail here. Optionally, the processing module 22 is configured to execute at least one of the other steps (e.g., S201 to S204, but not limited thereto) performed by the terminal 10 in any of the above methods, and will not be described in detail here.

[0476] 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.

[0477] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0478] FIG5B is a schematic diagram of the structure of a network device according to an embodiment of the present disclosure. As shown in FIG5B , the network device 20 may include at least one of a transceiver module 21 and a processing module 22 .

[0479] In some embodiments, the transceiver module 21 is configured to send first indication information to the terminal, wherein the first indication information is configured to instruct the terminal to monitor an AI operation, where the AI ​​operation is whether to enable an inactive AI model.

[0480] The transceiver module 21 is configured to execute at least one of the communication steps (e.g., S201 to S204, but not limited thereto) such as sending and / or receiving performed by the network device 20 in any of the above methods, and will not be described in detail here. Optionally, the processing module 22 is configured to execute at least one of the other steps (e.g., S201 to S204, but not limited thereto) performed by the network device 20 in any of the above methods, and will not be described in detail here.

[0481] 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.

[0482] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0483] Figure 6A is a schematic diagram of the structure of a communication device 8100 proposed in an embodiment of the present disclosure. Communication device 8100 can be a network device, a terminal (e.g., user equipment), a chip, a chip system, or a processor that supports a network device in implementing any of the above methods, or a chip, a chip system, or a processor that supports a terminal in implementing any of the above methods. Communication device 8100 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.

[0484] As shown in Figure 6A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as 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 8100 is used to perform any of the above methods.

[0485] In some embodiments, the communication device 8100 further includes one or more memories 8102 for storing instructions. Optionally, all or part of the memories 8102 may be located outside the communication device 8100.

[0486] In some embodiments, the communication device 8100 further includes one or more transceivers 8103. When the communication device 8100 includes one or more transceivers 8103, the transceiver 8103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, S201 to S204, but not limited thereto), and the processor 8101 performs at least one of the other steps (for example, S201 to S204, but not limited thereto).

[0487] 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.

[0488] In some embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuit 8104 is connected to the memory 8102. The interface circuit 8104 may be configured to receive signals from the memory 8102 or other devices, and may be configured to send signals to the memory 8102 or other devices. For example, the interface circuit 8104 may read instructions stored in the memory 8102 and send the instructions to the processor 8101.

[0489] The communication device 8100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 8100 described in the present disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 6A. 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.

[0490] 6B is a schematic diagram of the structure of the chip 8200 proposed in an embodiment of the present disclosure. If the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 8200 shown in FIG6B, but the present disclosure is not limited thereto.

[0491] The chip 8200 includes one or more processors 8201 , and the chip 8200 is configured to execute any of the above methods.

[0492] In some embodiments, the chip 8200 further includes one or more interface circuits 8202. Optionally, the interface circuit 8202 is connected to the memory 8203. The interface circuit 8202 can be used to receive signals from the memory 8203 or other devices, and can be used to send signals to the memory 8203 or other devices. For example, the interface circuit 8202 can read instructions stored in the memory 8203 and send the instructions to the processor 8201.

[0493] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, S201 to S204, but not limited to these), and the processor 8201 performs at least one of the other steps (for example, S201 to S204, but not limited to these).

[0494] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0495] In some embodiments, the chip 8200 further includes one or more memories 8203 for storing instructions. Alternatively, all or part of the memories 8203 may be outside the chip 8200.

[0496] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 8100, causes the communication device 8100 to execute 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.

[0497] The present disclosure also provides a program product, which, when executed by the communication device 8100, enables the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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 monitoring configuration method, characterized in that: The method is executed by a terminal, and includes: Receive first indication information sent by a network device, wherein the first indication information is used to instruct the terminal to monitor an artificial intelligence (AI) operation, and the AI ​​operation is whether to enable an inactivated AI model.

2. The method according to claim 1, characterized in that The method further comprises: The AI ​​operation is monitored according to the first indication information to determine a monitoring result.

3. The method according to claim 2, characterized in that The method further comprises: According to the monitoring result, determine whether to enable the inactivated AI model.

4. The method according to claim 3, characterized in that The determining, according to the monitoring result, whether to enable the inactivated AI model includes: Determine the current usage environment according to the monitoring results; Whether to enable the inactivated AI model is determined according to whether the inactivated AI model is applicable to the current usage environment.

5. The method according to claim 3 or 4, characterized in that The inactivated AI model includes a local AI model of the terminal, and the method further includes: Determine to enable the inactivated AI model, and determine a processing result according to the monitoring result and the local AI model of the terminal.

6. The method according to any one of claims 2 to 5, characterized in that The method further comprises: According to the monitoring results, a statistical result satisfying a first condition is determined.

7. The method according to claim 6, characterized in that The first condition is that the reference signal received power RSRP is greater than a first value within the measurement time period.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: Sending a first message to the network device; The first message includes at least one of the following: A monitoring result, wherein the monitoring result is determined by the terminal by monitoring the AI ​​operation according to the first indication information; First information, wherein the first information is used to indicate whether to enable the inactivated AI model; A processing result, wherein the processing result is that the terminal determines to enable the inactivated AI model, which is determined according to the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal; Statistical results, wherein the statistical results are statistical results that the terminal determines to meet the first condition based on the monitoring results.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: Receive second indication information sent by the network device, wherein the second indication information is used to indicate at least one of the following: Activate the inactive AI model; Continue to monitor AI operations; Stop monitoring AI operations.

10. The method according to any one of claims 1 to 9, characterized in that The first indication information includes at least one of the following: first signaling, wherein the first signaling is used to indicate a measurement resource used by the terminal to monitor the AI ​​operation; second signaling, wherein the second signaling is used to instruct the terminal to send a reporting resource used for monitoring a monitoring result of the AI ​​operation; The third signaling is used to instruct the terminal to send a trigger condition for monitoring the monitoring result of the AI ​​operation.

11. The method according to claim 10, characterized in that The monitoring results include at least one of the following: RSRP measurement results; Channel state information reference signal CSI-RS measurement result; Measurement results of the positioning reference signal PRS.

12. The method according to claim 10 or 11, characterized in that The trigger condition includes at least one of the following: The monitoring result is lower than a first threshold; The moving speed of the terminal in the monitoring result is greater than a second threshold.

13. The method according to any one of claims 10 to 12, characterized in that The measurement resource is a periodic resource or a non-periodic resource.

14. The method according to any one of claims 10 to 13, characterized in that The first indication information includes the first signaling, and the method further includes: The AI ​​operation is monitored on the measurement resource according to the first signaling.

15. The method according to any one of claims 10 to 14, characterized in that The first indication information includes the second signaling, and the method further includes: The monitoring result is sent to the network device on the reporting resource.

16. The method according to any one of claims 10 to 15, characterized in that The first indication information includes the third signaling, and the method further includes: Determine that the trigger condition is met, and send the monitoring result to the network device.

17. A monitoring configuration method, characterized in that: The method is performed by a network device and includes: Sending first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor an AI operation, where the AI ​​operation is whether to enable an inactivated AI model.

18. The method according to claim 17, characterized in that The method further comprises: receiving a first message sent by the terminal; Determining a target operation according to the first message; The first message includes at least one of the following: A monitoring result, wherein the monitoring result is determined by the terminal by monitoring the AI ​​operation according to the first indication information; First information, wherein the first information is used to indicate whether to enable the inactivated AI model; A processing result, wherein the processing result is that the terminal determines to enable the inactivated AI model, which is determined according to the monitoring result and the local AI model of the terminal, and the inactivated AI model includes the local AI model of the terminal; Statistical results, wherein the statistical results are statistical results that the terminal determines to meet the first condition based on the monitoring results.

19. The method according to claim 18, characterized in that The first condition is that RSRP is greater than a first value during a measurement period.

20. The method according to claim 18 or 19, characterized in that The target operation includes at least one of the following: Whether to enable the inactive AI model; Whether AI operations need to continue to be monitored; Whether it is necessary to stop monitoring AI operations.

21. The method of claim 20, wherein: The inactivated AI model includes the AI ​​model of the network device, and the method further includes: Determine to enable the inactivated AI model, and determine to use the AI ​​model of the network device for processing.

22. The method according to any one of claims 17 to 21, characterized in that The method further comprises: Sending second indication information to the terminal, wherein the second indication information is used to indicate at least one of the following: Activate the inactive AI model; Continue to monitor AI operations; Stop monitoring AI operations.

23. The method according to any one of claims 17 to 22, characterized in that The first indication information includes at least one of the following: first signaling, wherein the first signaling is used to indicate a measurement resource used by the terminal to monitor the AI ​​operation; second signaling, wherein the second signaling is used to instruct the terminal to report a reporting resource used by the monitoring result of monitoring the AI ​​operation; The third signaling is used to instruct the terminal to report a trigger condition for monitoring a monitoring result of the AI ​​operation.

24. The method of claim 23, wherein: The monitoring results include at least one of the following: RSRP measurement results; CSI-RS measurement results; Measurement results of PRS.

25. The method according to claim 23 or 24, characterized in that The trigger condition includes at least one of the following: The monitoring result is lower than a first threshold; The moving speed of the terminal in the monitoring result is greater than a second threshold.

26. The method according to any one of claims 23 to 25, characterized in that The measurement resource is a periodic resource or a non-periodic resource.

27. The method according to any one of claims 23 to 26, characterized in that The first indication information includes the second signaling, and the method further includes: Receive the monitoring result sent by the terminal on the reporting resource.

28. The method according to any one of claims 23 to 27, characterized in that The first indication information includes the third signaling, and the method further includes: The monitoring result sent by the terminal is received when it is determined that the trigger condition is met.

29. A monitoring configuration method, characterized in that: include: The network device sends first indication information to the terminal, wherein the first indication information is used to instruct the terminal to monitor the AI ​​operation, and the AI ​​operation is whether to enable an inactivated AI model; The terminal receives the first indication information sent by the network device.

30. A terminal, characterized in that: include: A transceiver module is used to receive first indication information sent by a network device, wherein the first indication information is used to instruct the terminal to monitor the AI ​​operation, and the AI ​​operation is whether to enable an inactivated AI model.

31. A network device, characterized in that: include: A transceiver module is used to send first indication information to a terminal, wherein the first indication information is used to instruct the terminal to monitor an AI operation, and the AI ​​operation is whether to enable an inactivated AI model.

32. A terminal, characterized in that: include: one or more processors; The terminal is used to execute the method according to any one of claims 1 to 16.

33. A network device, characterized in that: include: one or more processors; The network device is used to execute the method according to any one of claims 17 to 28.

34. A communication system, characterized in that: The method comprises a terminal and a network device, wherein the terminal is configured to implement the method according to any one of claims 1 to 16, and the network device is configured to implement the method according to any one of claims 17 to 28.

35. 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 method according to any one of claims 1 to 16 and 17 to 28.

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