Ai object management method and apparatus, source node, first node, and terminal

Abstract

An AI object management method and apparatus, a source node, a first node, and a terminal, relating to the technical field of communications. The AI object management method in embodiments of the present application comprises: during handover preparation, the source node sends first information to the first node, wherein the first node comprises a candidate node or a target node, and the first information comprises at least one of the following: information of an AI object supported by the terminal; information of an AI object currently activated by the terminal; information of a first AI object; first data which is collected by the source node and has not been used for AI object training or AI object monitoring; second data which is collected by the source node and has not been used for AI object inference; an application scope of AI object inference performed by the terminal; an RRM measurement result; a target prediction result; and a monitoring result of an AI object. The AI object comprises an AI model or an AI function, and the first AI object is an AI object which is stored by the source node and is suitable for the terminal.

Description

AI object management method, device, source node, first node and terminal

[0001] Cross-references

[0002] This disclosure claims priority to Chinese patent application number 202410104900.4 filed on January 24, 2024, entitled “AI object management method, device, source node, first node and terminal,” and the entire contents of the Chinese patent application are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to an AI object management method, device, source node, first node and terminal. Background Art

[0004] Currently, in mobile communication networks, artificial intelligence (AI) can be used to perform tasks or provide services. For example, AI models or AI functions can be used to perform mobility management-related processes such as radio resource management (RRM) measurement prediction and event prediction. However, the related art lacks a corresponding solution for how to manage AI models or AI functions during cell handovers. This can easily lead to difficulties for terminals to correctly and effectively perform reasoning based on AI models or AI functions after cell handovers. Summary of the Invention

[0005] Embodiments of the present application provide an AI object management method, apparatus, source node, first node, and terminal, which can exchange relevant information of AI objects between the source node and the first node in the event of a cell handover. In this way, after the cell handover, the target cell can more accurately manage AI objects related to the terminal based on the relevant information of the AI ​​objects.

[0006] In a first aspect, a method for managing an AI object is provided, the method comprising:

[0007] During the handover preparation process, the source node sends first information to the first node;

[0008] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0009] Information about AI objects supported by the terminal;

[0010] Information about the AI ​​object currently activated on the terminal;

[0011] Information about the first AI object;

[0012] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0013] Second data collected by the source node and not yet used for AI object reasoning;

[0014] The scope of application of AI object reasoning by terminals;

[0015] Radio Resource Management RRM measurement results;

[0016] Target prediction results;

[0017] Monitoring results of AI objects;

[0018] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0019] In a second aspect, an AI object management device is provided, the device comprising:

[0020] A sending module, configured to send first information to the first node during a handover preparation process;

[0021] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0022] Information about AI objects supported by the terminal;

[0023] Information about the AI ​​object currently activated on the terminal;

[0024] Information about the first AI object;

[0025] First data collected by the source node but not yet used for AI object training or AI object monitoring;

[0026] Second data collected by the source node but not yet used for AI object reasoning;

[0027] The scope of application of AI object reasoning by terminals;

[0028] Radio Resource Management RRM measurement results;

[0029] Target prediction results;

[0030] Monitoring results of AI objects;

[0031] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0032] In a third aspect, an AI object management method is provided, the method comprising:

[0033] During the handover preparation process, the first node receives first information sent by the source node;

[0034] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0035] Information about AI objects supported by the terminal;

[0036] Information about the AI ​​object currently activated on the terminal;

[0037] Information about the first AI object;

[0038] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0039] Second data collected by the source node and not yet used for AI object reasoning;

[0040] The scope of application of AI object reasoning by terminals;

[0041] Radio Resource Management RRM measurement results;

[0042] Target prediction results;

[0043] Monitoring results of AI objects;

[0044] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0045] In a fourth aspect, an AI object management device is provided, the device comprising:

[0046] A receiving module, configured to receive first information sent by a source node during a handover preparation process;

[0047] The first information includes at least one of the following:

[0048] Information about AI objects supported by the terminal;

[0049] Information about the AI ​​object currently activated on the terminal;

[0050] Information about the first AI object;

[0051] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0052] Second data collected by the source node and not yet used for AI object reasoning;

[0053] The scope of application of AI object reasoning by terminals;

[0054] Radio Resource Management RRM measurement results;

[0055] Target prediction results;

[0056] Monitoring results of AI objects;

[0057] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0058] In a fifth aspect, a method for managing an AI object is provided, the method comprising:

[0059] When the handover is executed or completed, the terminal performs a second operation;

[0060] The second operation includes at least one of the following:

[0061] Deactivate the AI ​​object activated in the source cell;

[0062] Release the AI ​​object from the source cell;

[0063] Release the AI ​​object that applies only to the source cell;

[0064] Release AI objects that are out of scope;

[0065] releasing collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0066] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0067] The AI ​​object includes an AI model or an AI function.

[0068] In a sixth aspect, an AI object management device is provided, the device comprising:

[0069] an execution module, configured to execute a second operation when the handover is executed or completed;

[0070] The second operation includes at least one of the following:

[0071] Deactivate the AI ​​object activated in the source cell;

[0072] Release the AI ​​object from the source cell;

[0073] Release the AI ​​object that applies only to the source cell;

[0074] Release AI objects that are out of scope;

[0075] Release collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0076] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0077] The AI ​​object includes an AI model or an AI function.

[0078] In a seventh aspect, a source node is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0079] In an eighth aspect, a source node is provided, comprising a processor and a communication interface, wherein the communication interface is configured to send first information to a first node during a handover preparation process; wherein the first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0080] Information about AI objects supported by the terminal;

[0081] Information about the AI ​​object currently activated on the terminal;

[0082] Information about the first AI object;

[0083] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0084] Second data collected by the source node and not yet used for AI object reasoning;

[0085] The scope of application of AI object reasoning by terminals;

[0086] Radio Resource Management RRM measurement results;

[0087] Target prediction results;

[0088] Monitoring results of AI objects;

[0089] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0090] In a ninth aspect, a first node is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the third aspect are implemented.

[0091] In a tenth aspect, a first node is provided, comprising a processor and a communication interface, wherein the communication interface is configured to receive first information sent by a source node during a handover preparation process; wherein the first node comprises a candidate node or a target node, and the first information comprises at least one of the following:

[0092] Information about AI objects supported by the terminal;

[0093] Information about the AI ​​object currently activated on the terminal;

[0094] Information about the first AI object;

[0095] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0096] Second data collected by the source node and not yet used for AI object reasoning;

[0097] The scope of application of AI object reasoning by terminals;

[0098] Radio Resource Management RRM measurement results;

[0099] Target prediction results;

[0100] Monitoring results of AI objects;

[0101] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0102] In the eleventh aspect, a terminal is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the fifth aspect are implemented.

[0103] According to a twelfth aspect, a terminal is provided, including a processor and a communication interface, wherein the processor is configured to perform a second operation when handover is performed or handover is completed; wherein the second operation includes at least one of the following:

[0104] Deactivate the AI ​​object activated in the source cell;

[0105] Release the AI ​​object from the source cell;

[0106] Release the AI ​​object that applies only to the source cell;

[0107] Release AI objects that are out of scope;

[0108] releasing collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0109] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0110] The AI ​​object includes an AI model or an AI function.

[0111] In a thirteenth aspect, an AI object management system is provided, comprising: a source node, a first node, and a terminal, wherein the source node can be used to execute the steps of the AI ​​object management method as described in the first aspect, the first node can be used to execute the steps of the AI ​​object management method as described in the third aspect, and the terminal can be used to execute the steps of the AI ​​object management method as described in the fifth aspect.

[0112] In the fourteenth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the third aspect are implemented, or the steps of the method described in the fifth aspect are implemented.

[0113] In the fifteenth aspect, a chip is provided, which includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the method described in the first aspect, or implement the steps of the method described in the third aspect, or implement the steps of the method described in the fifth aspect.

[0114] In the sixteenth aspect, a computer program / program product is provided, wherein the computer program / program product comprises a computer program or computer instructions, and the computer program or computer instructions are executed by at least one processor to implement the steps of the method described in the first aspect, or the steps of the method described in the third aspect, or the steps of the method described in the fifth aspect.

[0115] In an embodiment of the present application, during a handover preparation process, a source node sends first information to a first node; wherein the first node includes a candidate node or a target node, and the first information includes at least one of the following: information about AI objects supported by the terminal; information about the AI ​​object currently activated by the terminal; information about the first AI object; first data collected by the source node that has not been used for AI object training or AI object monitoring; second data collected by the source node that has not been used for AI object reasoning; an applicable scope for AI object reasoning by the terminal; RRM measurement results; target prediction results; and monitoring results of the AI ​​object; the AI ​​object includes an AI model or an AI function, and the first AI object is an AI object applicable to the terminal stored by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object. That is, in the embodiment of the present application, in the case of a cell handover, relevant information about the AI ​​object is exchanged between the source node and the first node. In this way, after the cell handover, the target cell can more accurately manage the AI ​​objects related to the terminal, thereby ensuring that the terminal can more accurately and effectively perform reasoning based on the AI ​​object. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0117] FIG2a is a schematic diagram of the structure of a neural network provided in an embodiment of the present application;

[0118] FIG2 b is a schematic diagram of the structure of a neuron provided in an embodiment of the present application;

[0119] FIG3 is a schematic diagram of an AI / ML functional architecture provided in an embodiment of the present application;

[0120] FIG4 is a flowchart of a cell switching provided by an embodiment of the present application;

[0121] FIG5 is a flowchart of an AI object management method provided in an embodiment of the present application;

[0122] FIG6 is a flowchart of another AI object management method provided in an embodiment of the present application;

[0123] FIG7 is a flowchart of another AI object management method provided in an embodiment of the present application;

[0124] FIG8 is a structural diagram of an AI object management device provided in an embodiment of the present application;

[0125] FIG9 is a structural diagram of another AI object management device provided in an embodiment of the present application;

[0126] FIG10 is a structural diagram of another AI object management device provided in an embodiment of the present application;

[0127] FIG11 is a structural diagram of a communication device provided in an embodiment of the present application;

[0128] FIG12 is a structural diagram of a source node provided in an embodiment of the present application.

[0129] FIG13 is a structural diagram of the first node provided in an embodiment of the present application.

[0130] FIG14 is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0131] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0132] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0133] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0134] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0135] FIG1 is a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (Flight Vehicle), a vehicle-mounted device (VUE), a ship-mounted device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, vehicle-mounted controller, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application.

[0136] The network-side device 12 may include an access network device or a core network device, wherein the access network device may also be referred to as a radio access network (RAN) device, a radio access network function, or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AP), or a wireless fidelity (WiFi) node. Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0137] The core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (MME), access mobility management function (AMF), session management function (SMF), user plane function (UPF), policy control function (PCF), policy and charging rules function unit (PCRF), edge application service discovery function (EASDF), unified data management (UDM), unified data repository (UDR), home user server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BSF), application function ( It should be noted that in the embodiments of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.

[0138] It should be noted that the source node provided in the embodiment of the present application may be the above-mentioned access network device, and the first node provided in the embodiment of the present application may also be the above-mentioned access network device.

[0139] For ease of understanding, some of the contents involved in the embodiments of this application are described below:

[0140] 1. Artificial Intelligence (AI)

[0141] Artificial intelligence (AI) is currently being widely applied in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for future wireless communication networks. AI modules can be implemented in a variety of ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI module.

[0142] For example, a neural network can be shown in Figure 2a. The neural network is composed of neurons, and each neuron can be shown in Figure 2b. Here, a1, a2, ... aK are inputs, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, tanh, and Rectified Linear Unit (ReLU).

[0143] Neural network parameters are optimized using a gradient optimization algorithm. Gradient optimization algorithms are a class of algorithms that minimize or maximize an objective function (also called a loss function), which is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we construct a neural network model f(.). With this model, we can obtain a predicted output f(x) based on the input x, and calculate the difference between the predicted value and the true value (f(x) - Y). This is the loss function. The goal is to find the appropriate W, b to minimize the value of this loss function. The smaller the loss value, the closer the model is to the true value.

[0144] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0145] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), adaptive gradient descent (Adagrad), Adadelta, root mean square error prop (RMSprop), adaptive momentum estimation (Adam), etc.

[0146] When these optimization algorithms backpropagate errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained by the loss function, add the influence of the learning rate, the previous gradient / derivative / partial derivative, etc., obtain the gradient, and pass the gradient to the previous layer.

[0147] 2. AI Unit / AI Model

[0148] The AI ​​unit / AI model of the embodiment of the present application may also be referred to as a machine learning (ML) model, ML unit, AI structure, AI function, AI feature, machine learning model, neural network, neural network function, neural network function, etc., or the above-mentioned AI unit / AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as GPU, NPU, TPU, ASIC, etc., which is not specifically limited in the embodiment of the present application. Optionally, the specific data set includes at least one of the input and output of the AI ​​unit / AI model.

[0149] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This embodiment of the present application does not specifically limit this.

[0150] AI functionality: an AI algorithm function that can include multiple AI models.

[0151] 3. AI / ML Framework

[0152] The Air Interface AI project, Release 18, studied the AI / ML framework, as shown in Figure 3. The main process is as follows:

[0153] Data Collection: Responsible for providing input data for Model Training, Management and Interence;

[0154] Model Training: Responsible for executing AI / ML model training, validation, and testing. Also responsible for data preparation, i.e., data preprocessing and conversion into specific formats;

[0155] Management: responsible for model selection / activation / deactivation / switching / fallback, etc.

[0156] Inference: Provides the output of applying an AI / ML model or AI / ML function.

[0157] Model Storage: responsible for saving trained / updated models

[0158] Model Transfer / Delivery: Responsible for delivering the AI / ML model to the inference function node.

[0159] 4. Layer 3 Handover Process

[0160] As shown in Figure 4, the main steps of Layer 3 handover include:

[0161] (1) The source base station sends a measurement configuration to the UE;

[0162] (2) The UE performs measurements according to the measurement configuration and reports a measurement report when conditions are met;

[0163] (3) The source base station interacts with the target base station to prepare for handover;

[0164] (4) The UE receives the handover command sent by the source base station, immediately disconnects from the source cell, and connects to the target cell.

[0165] 5. RRM measurement reporting

[0166] The measurement configuration mainly consists of the measurement object, reporting configuration and measurement identification (ID);

[0167] Measurement Object: The frequency point to be measured

[0168] Report Configuration (ReportConfig): includes reporting criteria (periodic / event-triggered); reference signal type (Synchronous Signal Block (SSB) / Channel State Information Reference Signal (CSI-RS)), measurement reporting amount (any combination of Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) / Signal to Interference Plus Noise Ratio (SINR)); whether to report beam measurement results, the maximum number of reportable beams, etc.

[0169] Measurement identifier (measId): used to associate a measurement object with a reporting configuration. A measurement object can be associated with multiple reporting configurations, and a reporting configuration can be associated with multiple measurement objects.

[0170] In NR, the three are linked together in the following ways:

[0171] The reporting configuration can include event-triggered reporting. The events defined in NR can be found in Table 1, which include the following events:

[0172] Table 1

[0173] Taking the A3 event as an example, the meanings of the parameters for the entry and exit conditions are as follows:

[0174] Mn: Neighboring cell measurement result, without considering any offset;

[0175] Ofn: Neighborhood measurement object specific offset;

[0176] Ocn: Neighboring cell-level specific offset;

[0177] Mp: SpCell (primary serving cell) measurement result, without considering any offset;

[0178] Ofp: SpCell measurement object specific offset;

[0179] Ocp: SpCell cell-level specific offset;

[0180] Hys: hysteresis parameter of the event;

[0181] Off: The offset parameter of the event.

[0182] If the reporting type is event-triggered, to avoid frequent reporting or ping-pong switching, the base station configures a trigger time (timeToTrigger) parameter for each event. If the L3 filtered signal quality of one or more candidate cells within the timeToTrigger time meets the event entry condition, the measurement report is triggered;

[0183] For conditional handover, the UE uses a cell that meets the conditions as a triggering cell and selects one of the triggering cells to perform conditional reconfiguration.

[0184] 6. Conditional Handover

[0185] Conditional handover means the network pre-configures multiple candidate cells for the UE. The UE evaluates the execution conditions of the candidate cells and switches to the corresponding cell when the conditions are met. The execution conditions may include one or two trigger conditions.

[0186] Take the parameters in NR as an example:

[0187] condReconfigId indicates the conditional reconfiguration ID; condExecutionCond is used to configure the execution conditions for the Primary Cell (PCell) change, and condRRCReconfig is used to configure the configuration parameters of the target Master Cell Group (MCG). The three parameters correspond to a set of candidate cell configurations and are used for conditional reconfiguration of a candidate PCell, as shown below:

[0188] The execution conditions above refer to the measurement events configured in the reporting configuration associated with MeasId. For conditional handover, the measurement events support conditional handover event A3 (condEventA3), component handover event A4 (condEventA4), or component handover event A5 (condEventA5). The judgment conditions are the same as those for A3, A4, and A5 above. If the L3 filtered signal quality of one or more candidate cells meets the event entry conditions within the timeToTrigger time, the UE selects the cells that meet the conditions as the triggering cells and selects one of the triggering cells to execute conditional reconfiguration.

[0189] The following describes in detail the AI ​​object management method provided in the embodiments of the present application through some embodiments and their application scenarios in conjunction with the accompanying drawings.

[0190] Please refer to FIG5 , which is a flowchart of an AI object management method provided in an embodiment of the present application. The method can be executed by a source node, as shown in FIG5 , and includes the following steps:

[0191] Step 501: During handover preparation, a source node sends first information to a first node.

[0192] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0193] Information about AI objects supported by the terminal;

[0194] Information about the AI ​​object currently activated on the terminal;

[0195] Information about the first AI object;

[0196] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0197] Second data collected by the source node and not yet used for AI object reasoning;

[0198] The scope of application of AI object reasoning by terminals;

[0199] Radio Resource Management RRM measurement results;

[0200] Target prediction results;

[0201] Monitoring results of AI objects;

[0202] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0203] In this embodiment, the above-mentioned handover preparation process refers to the process in which the source node requests the target node or candidate node to provide the target cell or candidate cell configuration for the terminal. Exemplarily, the above-mentioned first information can be carried in a handover request message (Handover Request message), a terminal context establishment request message (UE CONTEXT SETUP REQUEST message) or a terminal context modification request message (UE CONTEXT MODIFICATION REQUEST message), etc.

[0204] The AI ​​objects supported by the terminal can be understood as the terminal being able to perform reasoning based on the AI ​​objects or the terminal having the ability to perform reasoning using the AI ​​objects, etc. In some optional embodiments, the terminal may report the AI ​​objects supported by the terminal to the source cell through capability reporting information (UECapabilityInformation) or assistance information (UEAssistanceInformation).

[0205] For the AI ​​object currently activated by the terminal, the terminal uses the AI ​​object for reasoning. In some optional embodiments, the AI ​​object currently activated by the terminal may be activated by the source cell instructing the terminal to do so, or the terminal itself may determine activation and notify the source cell.

[0206] The first AI object is an AI object applicable to the terminal and stored by the source node. The AI ​​object applicable to the terminal can be understood as an AI object suitable for performing RRM measurement prediction or event prediction on the terminal. Specifically, the source node transmits information about the AI ​​object applicable to the current terminal to the candidate node or target node, so that the target node obtains the corresponding AI object.

[0207] It should be noted that the information of the above-mentioned AI object may include but is not limited to at least one of the following: the identification of the AI ​​object, the description information of the AI ​​object, the file information of the AI ​​object, the input of the AI ​​object, and the output of the AI ​​object. Specifically, with respect to the identification of the above-mentioned AI object, if the AI ​​object is an AI model, the identification of the above-mentioned AI object may include at least one of the identification of the AI ​​model and the identification of the AI ​​function corresponding to the AI ​​model; if the above-mentioned AI object is an AI function, the identification of the above-mentioned AI object may be the identification of the AI ​​function. The description information of the above-mentioned AI object may include but is not limited to the usage information of the AI ​​object (for example, the validity period and valid range of the AI ​​object), the size of the AI ​​object, and other descriptive information. The file information of the above-mentioned AI object may include but is not limited to the parameter information of the AI ​​object, the structure or architecture information of the AI ​​object, and the like.

[0208] The first data collected by the above-mentioned source node and not yet used for AI object training or AI object monitoring, for example, the source node collects at least one of the input and output of the AI ​​object. If it is determined that cell switching is required before using at least one of the collected input and output of the AI ​​object for AI object training or AI object monitoring, the source node may send at least one of the collected input and output of the AI ​​object to the candidate node or the target node, so that after the cell switching, the target node can perform AI object training or AI object monitoring based on at least one of the input and output of the AI ​​object collected by the source node.

[0209] The second data collected by the above-mentioned source node and not yet used for AI object reasoning, for example, the source node collects the input of the AI ​​object. If it is determined that cell switching is required before using the collected input of the AI ​​object for AI object reasoning, the source node can send the collected input of the AI ​​object to the candidate node or the target node, so that after the cell switching, the target node can perform AI object reasoning based on the input of the AI ​​object collected by the source node. Among them, the above-mentioned AI object reasoning based on the input of the AI ​​object collected by the source node can be understood as the AI ​​object reasoning based on the input of the AI ​​object collected by the source node.

[0210] The applicable scope of the terminal for AI object reasoning can be understood as the applicable scope of the terminal for reasoning based on the AI ​​object, for example, the frequency range, cell range, tracking area (TA) range, or slice range for the terminal for AI object reasoning. Taking the applicable scope as the cell range as an example, the AI ​​object activated by the terminal may be applicable to multiple cells, but the terminal only reasons about the AI ​​object for some cells. Therefore, the candidate node or target node can configure the terminal with a candidate cell or target cell within the applicable scope of reasoning based on the AI ​​object by obtaining the applicable scope of the terminal for AI object reasoning.

[0211] The above-mentioned target prediction result may include at least one of an RRM measurement prediction result based on an AI object and an event prediction result based on an AI object, wherein the above-mentioned RRM measurement prediction result based on an AI object can be understood as the result obtained by performing RRM measurement prediction based on an AI object, and the above-mentioned event prediction result based on an AI object can be understood as the result obtained by performing event prediction based on an AI object. Exemplarily, the above-mentioned event prediction result may include but is not limited to at least one of a prediction result of a measurement event, a prediction result of a radio link failure (RLF), and a prediction result of a handover failure (HOF).

[0212] The monitoring results of the above-mentioned AI objects can be understood as the monitoring results obtained by monitoring the AI ​​objects. For example, the AI ​​objects can be monitored based on the monitoring indicator parameters predefined by the network side device configuration or the protocol, wherein the above-mentioned monitoring indicator parameters include RRM measurement prediction accuracy, the prediction accuracy of the optimal cell, the prediction accuracy of the optimal beam, the prediction accuracy of HOF, the prediction accuracy of RLF, the prediction accuracy of the measurement event, the number or probability of abnormal events in the first time period, the switching delay or interruption duration in the second time period, the number of switches per unit time or the third time period, etc.

[0213] In this embodiment, the source node sends first information to the first node, and the first node can then configure a target cell or candidate cell for the terminal based on the first information, or the first node can perform AI object training, AI object monitoring, AI object reasoning, AI object management operations, etc. based on the first information. This embodiment is described below with examples.

[0214] The source node indicates the AI ​​object information supported by the terminal to the first node, so that the first node can provide indication information of AI object management operations in the candidate cell or target cell configuration based on the AI ​​object information supported by the terminal, such as the selection, activation, deactivation, switching or fallback of the AI ​​object, so that the terminal can quickly execute the AI ​​object management operation indicated by the first node based on the indication information after the switching is completed.

[0215] The source node indicates the AI ​​object information currently activated by the terminal to the first node, so that the first node can provide indication information of AI object management operations in the candidate cell or target cell configuration based on the AI ​​object information currently activated by the terminal, such as the selection, activation, deactivation, switching or fallback of the AI ​​object, so that the terminal can quickly execute the AI ​​object management operation indicated by the first node based on the indication information after the switching is completed.

[0216] The source node indicates the information of the first AI object to the first node, so that after the terminal switches to the target cell, the first node can use the first AI object provided by the source node to perform AI object reasoning on the terminal to continue to support measurement prediction or event prediction functions, etc.

[0217] The source node sends the collected first data that has not yet been used for AI object training or AI object monitoring to the first node, so that the first node can use the first data for AI object training or AI object monitoring, which is conducive to making full use of the data collected at the source node and avoiding repeated collection; in addition, the first node uses the first data that has not been used for AI object monitoring to monitor the AI ​​object, which is also conducive to ensuring the accuracy of the prediction results of the AI ​​object.

[0218] The source node sends the collected second data that has not yet been used for AI object reasoning to the first node, so that the first node can use the second data to perform AI object reasoning and obtain a prediction result. That is, the first node can use the data collected at the source node to obtain a prediction result, which is conducive to optimizing the performance of the terminal in the target cell.

[0219] The source node sends the applicable scope of the terminal for AI object reasoning to the first node, so that the first node can determine the applicable scope of the terminal's AI object reasoning, and thus adjust the applicable scope of the AI ​​object according to its own needs. For example, if the original applicable scope is large and the first node does not need to perform reasoning within such a large scope, the first node can reduce the applicable scope; or the first node instructs the terminal to delete / release the AI ​​object. For example, if the first node is no longer within the applicable scope, the first node can instruct the terminal to release the AI ​​object.

[0220] The source node sends the RRM measurement results to the first node, so that the first node can determine the target cell / beam quality based on the RRM measurement results to provide an appropriate configuration.

[0221] The source node sends the target prediction result to the first node, so that the first node can judge the target cell / beam quality based on the target prediction result to provide a suitable configuration.

[0222] The source node sends the monitoring results of the AI ​​object to the first node, so that the first node can judge the validity of the AI ​​object based on the target prediction results and make decisions on AI object management operations, such as selection, activation, deactivation, switching or rollback of the AI ​​object.

[0223] In an embodiment of the present application, during a handover preparation process, a source node sends first information to a first node; wherein the first node includes a candidate node or a target node, and the first information includes at least one of the following: information about AI objects supported by the terminal; information about an AI object currently activated by the terminal; information about a first AI object; first data collected by the source node that has not been used for AI object training or AI object monitoring; second data collected by the source node that has not been used for AI object reasoning; an applicable scope for AI object reasoning by the terminal; RRM measurement results; target prediction results; and monitoring results of an AI object; the AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal stored by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object. That is, in the embodiment of the present application, in the case of a cell handover, relevant information about the AI ​​object is exchanged between the source node and the first node. In this way, after the cell handover, the target cell can more accurately manage the AI ​​objects related to the terminal, thereby facilitating the terminal to more accurately and effectively perform reasoning based on the AI ​​object.

[0224] Optionally, the information of the AI ​​object supported by the terminal includes at least one of the following: an identifier of the AI ​​object supported by the terminal, file information of the AI ​​object supported by the terminal, input of the AI ​​object supported by the terminal, and output of the AI ​​object supported by the terminal; wherein the file information of the AI ​​object supported by the terminal includes at least one of parameter information and structure information of the AI ​​object supported by the terminal;

[0225] or,

[0226] The information of the AI ​​object currently activated by the terminal includes at least one of the following: an identifier of the AI ​​object currently activated by the terminal, file information of the AI ​​object currently activated by the terminal, an input of the AI ​​object currently activated by the terminal, and an output of the AI ​​object currently activated by the terminal; wherein the file information of the AI ​​object currently activated by the terminal includes at least one of parameter information and structure information of the AI ​​object currently activated by the terminal;

[0227] or,

[0228] The information of the first AI object includes at least one of the following: an identifier of the first AI object, file information of the first AI object, input of the first AI object, and output of the first AI object. The file information of the first AI object includes at least one of parameter information and structure information of the first AI object.

[0229] Regarding the identification of the AI ​​object supported by the above-mentioned terminal, if the AI ​​object supported by the above-mentioned terminal is an AI model, the identification of the AI ​​object supported by the above-mentioned terminal may include at least one of the identification of the AI ​​model supported by the terminal and the AI ​​function identification corresponding to the AI ​​model supported by the terminal; if the AI ​​object supported by the above-mentioned terminal is an AI function, the identification of the AI ​​object supported by the above-mentioned terminal may include the identification of the AI ​​function supported by the terminal.

[0230] The file information of the AI ​​object supported by the terminal may include at least one of parameter information of the AI ​​object supported by the terminal, structure or architecture information of the AI ​​object supported by the terminal, and the like.

[0231] The input of the AI ​​object supported by the above-mentioned terminal may include but is not limited to at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell.

[0232] The output of the AI ​​object supported by the above terminal may include but is not limited to at least one of the following: the identifier of the optimal cell, the identifier of the optimal beam, a flag indicating whether the measurement event is met, a flag indicating whether RLF occurs, and a flag indicating whether HOF occurs.

[0233] Regarding the identifier of the AI ​​object currently activated by the above-mentioned terminal, if the AI ​​object currently activated by the above-mentioned terminal is an AI model, the identifier of the AI ​​object currently activated by the above-mentioned terminal may include at least one of the identifier of the AI ​​model currently activated by the terminal and the AI ​​function identifier corresponding to the AI ​​model currently activated by the terminal; if the AI ​​object currently activated by the above-mentioned terminal is an AI function, the identifier of the AI ​​object currently activated by the above-mentioned terminal may include the identifier of the AI ​​function currently activated by the terminal.

[0234] The file information of the AI ​​object currently activated by the terminal may include at least one of parameter information of the AI ​​object currently activated by the terminal, structure or architecture information of the AI ​​object currently activated by the terminal, and the like.

[0235] The input of the AI ​​object currently activated by the above-mentioned terminal may include but is not limited to at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell.

[0236] The output of the AI ​​object currently activated by the above-mentioned terminal may include but is not limited to at least one of the following: the identifier of the optimal cell, the identifier of the optimal beam, the flag of whether the measurement event is met, the flag of whether RLF occurs, and the flag of whether HOF occurs.

[0237] Regarding the identifier of the first AI object, if the first AI object is an AI model, the identifier of the first AI object may include at least one of the identifier of the first AI model and the identifier of the AI ​​function corresponding to the first AI model; if the first AI object is an AI function, the identifier of the first AI object may include the identifier of the first AI function.

[0238] The file information of the first AI object may include at least one of parameter information of the first AI object, structure or architecture information of the first AI object, and the like.

[0239] The input of the above-mentioned first AI object may include but is not limited to at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell.

[0240] The output of the first AI object may include but is not limited to at least one of the following: an identifier of the optimal cell, an identifier of the optimal beam, a flag indicating whether the measurement event is met, a flag indicating whether RLF occurs, and a flag indicating whether HOF occurs.

[0241] Optionally, the applicable scope of the terminal performing AI object reasoning includes at least one of the following: a cell range in which the terminal performs AI object reasoning, and a frequency range in which the terminal performs AI object reasoning.

[0242] The cell range in which the terminal performs AI object reasoning can be understood as the terminal performing reasoning based on AI objects only for cells within the cell range. The frequency range in which the terminal performs AI object reasoning can be understood as the terminal performing reasoning based on AI objects only for frequencies within the frequency range.

[0243] Optionally, the first data includes at least one of an input of an AI object and an output of an AI object;

[0244] or,

[0245] The second data includes input of the AI ​​object.

[0246] Optionally, the input of the AI ​​object includes at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell;

[0247] or,

[0248] The output of the AI ​​object includes at least one of the following: an identifier of the optimal cell, an identifier of the optimal beam, a flag indicating whether a measurement event is met, a flag indicating whether a radio link failure RLF occurs, and a flag indicating whether a handover failure HOF occurs.

[0249] Optionally, the RRM measurement result includes at least one of the following:

[0250] a measurement of the cell signal quality of the serving cell;

[0251] A measurement of the beam signal quality of the serving cell;

[0252] a measurement of the cell signal quality of the candidate cell;

[0253] The measured value of the beam signal quality of the candidate cell;

[0254] a measurement of the cell signal quality of the target cell;

[0255] The measured value of the beam signal quality of the target cell;

[0256] The identifier of the best beam of the candidate cell obtained by measurement;

[0257] The identifier of the measured optimal beam of the target cell.

[0258] It can be understood that the above RRM measurement result can be understood as a measurement result obtained by the terminal performing RRM measurement.

[0259] Optionally, the RRM measurement prediction result includes at least one of the following:

[0260] A predicted value of the cell signal quality of the serving cell;

[0261] The predicted value of the beam signal quality of the serving cell;

[0262] The predicted value of the cell signal quality of the candidate cell;

[0263] The predicted value of the beam signal quality of the candidate cell;

[0264] The predicted value of the cell signal quality of the target cell;

[0265] The predicted value of the beam signal quality of the target cell;

[0266] The identifier of the predicted optimal beam of the candidate cell;

[0267] The identifier of the predicted optimal beam of the target cell.

[0268] It can be understood that the above RRM measurement prediction result can be understood as a prediction result obtained by the terminal performing RRM measurement prediction based on the AI ​​object.

[0269] Optionally, the monitoring result of the AI ​​object includes at least one of the following:

[0270] RRM measurement prediction accuracy;

[0271] The prediction accuracy of the optimal cell;

[0272] The prediction accuracy of the optimal beam;

[0273] HOF prediction accuracy;

[0274] RLF prediction accuracy;

[0275] Measuring the accuracy of event predictions;

[0276] The number or probability of abnormal events occurring in the first time period;

[0277] the switching delay or interruption duration in the second time period;

[0278] The number of switching times per unit time or within the third time period.

[0279] Exemplarily, the RRM measurement prediction accuracy may include the prediction error of the reference signal received power (RSRP) / reference signal received quality (RSRQ) / signal to interference plus noise ratio (SINR) at the cell level or beam level, etc., wherein the prediction error may include but is not limited to mean square error (MSE), root mean square error (RMSE), normalized mean square error (NMSE), etc.

[0280] The prediction of the above measurement event can be understood as predicting whether the measurement event will be satisfied.

[0281] Exemplarily, the switching delay or interruption duration may include an average switching delay or an average interruption duration in the second time period.

[0282] The first time period, the second time period, and the third time period may all be configured by a network-side device or predefined by a protocol.

[0283] The following examples illustrate how to determine each of the above monitoring results:

[0284] 1. RRM measurement prediction accuracy: determined based on the RRM measurement prediction result and the error of the RRM measurement result within the prediction time or time period corresponding to the RRM measurement prediction result; wherein the above-mentioned RRM measurement prediction result can be understood as the result obtained by the terminal performing RRM measurement prediction based on the AI ​​object, and the above-mentioned RRM measurement result can be understood as the result obtained by the terminal performing RRM measurement.

[0285] 2. The prediction accuracy of the optimal beam or optimal cell is determined according to the following steps:

[0286] Step S11: The terminal predicts at the first moment the optimal beam or optimal cell at the second moment;

[0287] Step S12: The terminal measures and obtains signal qualities of m beams or n cells at a second moment, where the m beams include a predicted optimal beam, or the n cells include a predicted optimal cell.

[0288] Step S13: The terminal sorts the signals from high to low according to the signal quality, and determines the actual optimal beam or optimal cell;

[0289] Step S14: The terminal compares the prediction result of step S11 with the actual result of step S13 to determine whether the prediction is accurate.

[0290] 3. The prediction accuracy of the measurement event is determined according to the following steps:

[0291] Step S21: The terminal predicts at the third moment that a measurement event associated with the first cell at the fourth moment will be satisfied;

[0292] Step S22: The terminal determines whether a measurement event at the fourth moment is satisfied based on the measurement result of the first cell between the third moment and the fourth moment.

[0293] Step S23: The terminal determines whether the prediction of the measurement event is accurate based on the prediction result and the actual result of the measurement event.

[0294] HOF prediction accuracy is determined according to the following steps:

[0295] Step S31: The terminal predicts at the fifth moment that HOF will occur in the second cell at the sixth moment;

[0296] Step S32: The terminal determines whether a HOF will occur at the sixth moment based on the measurement results of the second cell between the fifth moment and the sixth moment. How to determine the HOF based on the measurement results can be implemented based on the UE or pre-configured by the network. For example, the network can configure a second threshold. When the average signal quality of the second cell within a given time period is lower than the second threshold, the UE determines that a HOF will occur.

[0297] Step S33: The terminal determines whether the HOF prediction is accurate based on the HOF prediction result and the actual result.

[0298] 5. The RLF prediction accuracy is determined according to the following steps:

[0299] Step S41: The terminal predicts at the seventh moment that RLF will occur in the third cell at the eighth moment.

[0300] Step S42: The terminal determines whether an RLF will occur at the eighth time based on the measurement results of the third cell between the seventh time and the eighth time. The terminal may reuse the existing Radio Link Monitor (RLM) process to determine whether an RLF will occur based on signal quality.

[0301] Step S43: The terminal determines whether the RLF prediction is accurate based on the RLF prediction result and the actual result.

[0302] 6. Number of occurrences / probability of abnormal events within the first time period: The terminal counts the number of occurrences / probability of abnormal events within the first time period (predefined by network configuration or protocol).

[0303] 7. Handover delay / interruption duration in the second time period, or average handover delay / average interruption duration in the second time period: the terminal counts the handover delay / interruption duration in the second time period, or the terminal counts the average handover delay / average interruption duration in the second time period.

[0304] 8. Number of handovers per unit time or a third time period: The terminal counts the number of handovers per unit time or a third time period (predefined by network configuration or protocol).

[0305] Optionally, the method further includes:

[0306] The source node receives capability information of the terminal reported by the terminal, wherein the capability information is used to indicate AI objects supported by the terminal.

[0307] Exemplarily, the capability information of the terminal may be carried in capability reporting information (UECapabilityInformation) or auxiliary information (UEAssistanceInformation) to indicate the AI ​​objects supported by the terminal.

[0308] Please refer to FIG6 , which is a flowchart of an AI object management method provided in an embodiment of the present application. The method can be executed by the first node, as shown in FIG6 , and includes the following steps:

[0309] Step 601: During handover preparation, a first node receives first information sent by a source node.

[0310] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0311] Information about AI objects supported by the terminal;

[0312] Information about the AI ​​object currently activated on the terminal;

[0313] Information about the first AI object;

[0314] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0315] Second data collected by the source node and not yet used for AI object reasoning;

[0316] The scope of application of AI object reasoning by terminals;

[0317] Radio Resource Management RRM measurement results;

[0318] Target prediction results;

[0319] Monitoring results of AI objects;

[0320] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0321] Optionally, the method further comprises at least one of the following:

[0322] The first node sends configuration information of a first cell to the terminal through the source node; wherein the first cell includes a candidate cell or a target cell, and the configuration information of the first cell is determined according to the first information;

[0323] The first node performs a first operation based on the first information; wherein, the first operation includes at least one of the following: AI object training, AI object monitoring, AI object reasoning, and AI object management operation; the AI ​​object management operation includes activation, deactivation, switching, or falling back to non-AI operation.

[0324] In one embodiment, the first node may determine configuration information of the first cell based on the first information, and send the configuration information of the first cell to the source node, so that the source node can send the configuration information of the first cell to the terminal. For example, when the first information includes information about an AI object currently activated by the terminal, the first node may configure a first cell that supports the activated AI object for the terminal based on the information about the currently activated AI object; or, when the first information includes information about supported AI objects, the first node may configure a first cell that supports the AI ​​object supported by the terminal for the terminal based on the information about the AI ​​objects supported by the terminal.

[0325] In another embodiment, the first node may perform a first operation based on the first information. The above-mentioned fallback to the non-AI operation may be understood as falling back to an operation that does not use the AI ​​object for reasoning. For example, when the terminal performs RRM measurement prediction based on the AI ​​object, if the terminal receives instruction information to fall back to the non-AI operation, the terminal stops performing RRM measurement prediction based on the AI ​​object and reporting the RRM measurement prediction result, and performs RRM measurement to obtain and report the RRM measurement result.

[0326] Optionally, the configuration information of the first cell carries first indication information, and the first indication information is used to indicate the AI ​​object management operation.

[0327] In this embodiment, when the first node performs the AI ​​object management operation according to the first information, the AI ​​object management operation can be indicated to the terminal through the configuration information of the first cell, which can save signaling and facilitate the terminal to quickly perform the corresponding operation.

[0328] Optionally, the method further includes:

[0329] When the handover is completed, the first node sends second indication information to the terminal; wherein the second indication information is used to indicate the AI ​​object management operation.

[0330] In this embodiment, when the first node performs the AI ​​object management operation according to the first information, after the handover is completed, instruction information instructing the AI ​​object management operation is sent to the terminal so that the terminal performs the corresponding operation.

[0331] Optionally, the information of the AI ​​object supported by the terminal includes at least one of the following: an identifier of the AI ​​object supported by the terminal, file information of the AI ​​object supported by the terminal, input of the AI ​​object supported by the terminal, and output of the AI ​​object supported by the terminal; wherein the file information of the AI ​​object supported by the terminal includes at least one of parameter information and structure information of the AI ​​object supported by the terminal;

[0332] or,

[0333] The information of the AI ​​object currently activated by the terminal includes at least one of the following: an identifier of the AI ​​object currently activated by the terminal, file information of the AI ​​object currently activated by the terminal, an input of the AI ​​object currently activated by the terminal, and an output of the AI ​​object currently activated by the terminal; wherein the file information of the AI ​​object currently activated by the terminal includes at least one of parameter information and structure information of the AI ​​object currently activated by the terminal;

[0334] or,

[0335] The information of the first AI object includes at least one of the following: an identifier of the first AI object, file information of the first AI object, input of the first AI object, and output of the first AI object; wherein the file information of the first AI object includes at least one of parameter information and structure information of the first AI object.

[0336] Optionally, the applicable scope of the terminal performing AI object reasoning includes at least one of the following: a cell range in which the terminal performs AI object reasoning, and a frequency range in which the terminal performs AI object reasoning.

[0337] Optionally, the first data includes at least one of an input of an AI object and an output of an AI object;

[0338] or,

[0339] The second data includes input of the AI ​​object.

[0340] Optionally, the input of the AI ​​object includes at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell;

[0341] or,

[0342] The output of the AI ​​object includes at least one of the following: an identifier of the optimal cell, an identifier of the optimal beam, a flag indicating whether a measurement event is met, a flag indicating whether a radio link failure RLF occurs, and a flag indicating whether a handover failure HOF occurs.

[0343] Optionally, the RRM measurement result includes at least one of the following:

[0344] a measurement of the cell signal quality of the serving cell;

[0345] A measurement of the beam signal quality of the serving cell;

[0346] a measurement of the cell signal quality of the candidate cell;

[0347] The measured value of the beam signal quality of the candidate cell;

[0348] a measurement of the cell signal quality of the target cell;

[0349] The measured value of the beam signal quality of the target cell;

[0350] The identifier of the best beam of the candidate cell obtained by measurement;

[0351] The identifier of the measured optimal beam of the target cell.

[0352] Optionally, the RRM measurement prediction result includes at least one of the following:

[0353] A predicted value of the cell signal quality of the serving cell;

[0354] The predicted value of the beam signal quality of the serving cell;

[0355] The predicted value of the cell signal quality of the candidate cell;

[0356] The predicted value of the beam signal quality of the candidate cell;

[0357] The predicted value of the cell signal quality of the target cell;

[0358] The predicted value of the beam signal quality of the target cell;

[0359] The identifier of the predicted optimal beam of the candidate cell;

[0360] The identifier of the predicted optimal beam of the target cell.

[0361] Optionally, the monitoring result of the AI ​​object includes at least one of the following:

[0362] RRM measurement prediction accuracy;

[0363] The prediction accuracy of the optimal cell;

[0364] The prediction accuracy of the optimal beam;

[0365] HOF prediction accuracy;

[0366] RLF prediction accuracy;

[0367] Measuring the accuracy of event predictions;

[0368] The number or probability of abnormal events occurring in the first time period;

[0369] the switching delay or interruption duration in the second time period;

[0370] The number of switching times per unit time or within the third time period.

[0371] It should be noted that the implementation of this embodiment can refer to the relevant description of the embodiment shown in Figure 6, and will not be repeated here.

[0372] Please refer to FIG. 7 , which is a flowchart of an AI object management method provided in an embodiment of the present application. The method can be executed by a terminal, as shown in FIG. 7 , and includes the following steps:

[0373] Step 701: When handover is being performed or completed, the terminal performs a second operation.

[0374] The second operation includes at least one of the following:

[0375] Deactivate the AI ​​object activated in the source cell;

[0376] Release the AI ​​object from the source cell;

[0377] Release the AI ​​object that applies only to the source cell;

[0378] Release AI objects that are out of scope;

[0379] releasing collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0380] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0381] The AI ​​object includes an AI model or an AI function.

[0382] In this embodiment, the AI ​​object from the source cell may be understood as an AI object transmitted by the source cell to the terminal.

[0383] The aforementioned applicable scope may include, but is not limited to, a cell range, a frequency range, etc. The applicable scope of the aforementioned AI object can be understood as the range within which reasoning can be performed based on the AI ​​object. For example, an AI object that exceeds the applicable scope may include, for example, a target cell that is not within the cell range for which the AI ​​object is applicable, or a target cell frequency that is not within the frequency range for which the AI ​​object is applicable. Optionally, releasing an AI object that exceeds the applicable scope may also be referred to as releasing a fourth AI object, where the applicable scope of the fourth AI object does not include the target cell or the target cell frequency.

[0384] The following is an example to illustrate this embodiment:

[0385] When handover is performed or completed, the terminal deactivates the AI ​​object activated in the source cell to avoid the AI ​​object activated in the source cell being unapplicable in the target cell, resulting in system performance degradation.

[0386] When handover is performed or completed, the terminal releases the AI ​​object from the source cell to avoid the AI ​​object used in the source cell being unapplicable in the target cell, resulting in system performance degradation.

[0387] When performing handover or handover completion, the terminal releases the AI ​​object that is only applicable to the source cell. Since the AI ​​object that is only applicable to the source cell is not applicable in the target cell, it will cause system performance degradation. Therefore, releasing the AI ​​object that is only applicable to the source cell is beneficial to reducing system performance degradation.

[0388] When performing handover or handover completion, the terminal releases the AI ​​objects that are out of scope. Since the AI ​​objects that are out of scope are not applicable in the target cell, it will cause system performance to degrade. Therefore, releasing the AI ​​objects that are out of scope is beneficial to reducing system performance degradation.

[0389] When switching is executed or completed, the terminal releases the collected data corresponding to the AI ​​object that has been deactivated or released by the terminal. Since the collected data corresponding to the deactivated or released AI object is no longer useful, storage space can be saved after release.

[0390] When handover is executed or completed, if the actual result corresponding to the monitoring result of the third AI object has not been reported to the source cell, the terminal reports the actual result corresponding to the monitoring result of the third AI object to the target cell, so that the target cell can continue to monitor the AI ​​object to ensure the accuracy of the prediction result of the AI ​​object.

[0391] It should be noted that the node in the embodiment of the present application may include a distributed unit (DU), a centralized unit (CU) or a base station, etc. The switching in the embodiment of the present application may include L1 / L2-triggered mobility (LTM) or a primary / secondary cell change (PSCell change), etc.; the conditional switching in the embodiment of the present application may include conditional primary / secondary cell addition / modification (Conditional PSCell addition / change) or conditional LTM (i.e., Condition L1 / L2-triggered mobility).

[0392] It should also be noted that the AI ​​object management method provided in the embodiments of this application can be executed by an AI object management device, or by a control module within the AI ​​object management device that is configured to execute the AI ​​object management method. In the embodiments of this application, the AI ​​object management device provided in the embodiments of this application is described using the AI ​​object management device executing the AI ​​object management method as an example.

[0393] Please refer to FIG8 , which is a structural diagram of an AI object management device provided in an embodiment of the present application. As shown in FIG8 , the AI ​​object management device 800 includes:

[0394] The sending module 801 is configured to send first information to the first node during the handover preparation process;

[0395] The first node includes a candidate node or a target node, and the first information includes at least one of the following:

[0396] Information about AI objects supported by the terminal;

[0397] Information about the AI ​​object currently activated on the terminal;

[0398] Information about the first AI object;

[0399] First data collected by the source node but not yet used for AI object training or AI object monitoring;

[0400] Second data collected by the source node but not yet used for AI object reasoning;

[0401] The scope of application of AI object reasoning by terminals;

[0402] Radio Resource Management RRM measurement results;

[0403] Target prediction results;

[0404] Monitoring results of AI objects;

[0405] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0406] Optionally, the information of the AI ​​object supported by the terminal includes at least one of the following: an identifier of the AI ​​object supported by the terminal, file information of the AI ​​object supported by the terminal, input of the AI ​​object supported by the terminal, and output of the AI ​​object supported by the terminal; wherein the file information of the AI ​​object supported by the terminal includes at least one of parameter information and structure information of the AI ​​object supported by the terminal;

[0407] or,

[0408] The information of the AI ​​object currently activated by the terminal includes at least one of the following: an identifier of the AI ​​object currently activated by the terminal, file information of the AI ​​object currently activated by the terminal, an input of the AI ​​object currently activated by the terminal, and an output of the AI ​​object currently activated by the terminal; wherein the file information of the AI ​​object currently activated by the terminal includes at least one of parameter information and structure information of the AI ​​object currently activated by the terminal;

[0409] or,

[0410] The information of the first AI object includes at least one of the following: an identifier of the first AI object, file information of the first AI object, input of the first AI object, and output of the first AI object. The file information of the first AI object includes at least one of parameter information and structure information of the first AI object.

[0411] Optionally, the applicable scope of the terminal performing AI object reasoning includes at least one of the following: a cell range in which the terminal performs AI object reasoning, and a frequency range in which the terminal performs AI object reasoning.

[0412] Optionally, the first data includes at least one of an input of an AI object and an output of an AI object;

[0413] or,

[0414] The second data includes input of the AI ​​object.

[0415] Optionally, the input of the AI ​​object includes at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell;

[0416] or,

[0417] The output of the AI ​​object includes at least one of the following: an identifier of the optimal cell, an identifier of the optimal beam, a flag indicating whether a measurement event is met, a flag indicating whether a radio link failure RLF occurs, and a flag indicating whether a handover failure HOF occurs.

[0418] Optionally, the RRM measurement result includes at least one of the following:

[0419] a measurement of the cell signal quality of the serving cell;

[0420] A measurement of the beam signal quality of the serving cell;

[0421] a measurement of the cell signal quality of the candidate cell;

[0422] The measured value of the beam signal quality of the candidate cell;

[0423] a measurement of the cell signal quality of the target cell;

[0424] The measured value of the beam signal quality of the target cell;

[0425] The identifier of the best beam of the candidate cell obtained by measurement;

[0426] The identifier of the measured optimal beam of the target cell.

[0427] Optionally, the RRM measurement prediction result includes at least one of the following:

[0428] A predicted value of the cell signal quality of the serving cell;

[0429] The predicted value of the beam signal quality of the serving cell;

[0430] The predicted value of the cell signal quality of the candidate cell;

[0431] The predicted value of the beam signal quality of the candidate cell;

[0432] The predicted value of the cell signal quality of the target cell;

[0433] The predicted value of the beam signal quality of the target cell;

[0434] The identifier of the predicted optimal beam of the candidate cell;

[0435] The identifier of the predicted optimal beam of the target cell.

[0436] Optionally, the monitoring result of the AI ​​object includes at least one of the following:

[0437] RRM measurement prediction accuracy;

[0438] The prediction accuracy of the optimal cell;

[0439] The prediction accuracy of the optimal beam;

[0440] HOF prediction accuracy;

[0441] RLF prediction accuracy;

[0442] Measuring the accuracy of event predictions;

[0443] The number or probability of abnormal events occurring in the first time period;

[0444] the switching delay or interruption duration in the second time period;

[0445] The number of switching times per unit time or within the third time period.

[0446] Optionally, the device further comprises:

[0447] A receiving module is used to receive capability information of the terminal reported by the terminal, wherein the capability information is used to indicate AI objects supported by the terminal.

[0448] The AI ​​object management device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device can be a network-side device, or it can be a device other than a network-side device. For example, the network-side device can include but is not limited to the types of network-side devices 12 listed above. Other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0449] The AI ​​object management device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0450] Please refer to FIG9 , which is a structural diagram of an AI object management device provided in an embodiment of the present application. As shown in FIG9 , the AI ​​object management device 900 includes:

[0451] The receiving module 901 is configured to receive first information sent by a source node during a handover preparation process;

[0452] The first information includes at least one of the following:

[0453] Information about AI objects supported by the terminal;

[0454] Information about the AI ​​object currently activated on the terminal;

[0455] Information about the first AI object;

[0456] first data collected by the source node and not yet used for AI object training or AI object monitoring;

[0457] Second data collected by the source node and not yet used for AI object reasoning;

[0458] The scope of application of AI object reasoning by terminals;

[0459] Radio Resource Management RRM measurement results;

[0460] Target prediction results;

[0461] Monitoring results of AI objects;

[0462] The AI ​​object includes an AI model or an AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object.

[0463] Optionally, the device further comprises at least one of the following:

[0464] A first sending module, configured to send configuration information of a first cell to the terminal through the source node; wherein the first cell includes a candidate cell or a target cell, and the configuration information of the first cell is determined according to the first information;

[0465] An execution module is configured to execute a first operation based on the first information; wherein the first operation includes at least one of the following: AI object training, AI object monitoring, AI object reasoning, and AI object management operation; and the AI ​​object management operation includes activation, deactivation, switching, or falling back to a non-AI operation.

[0466] Optionally, the configuration information of the first cell carries first indication information, and the first indication information is used to indicate the AI ​​object management operation.

[0467] Optionally, the device further comprises:

[0468] The second sending module is used to send second indication information to the terminal when the switching is completed; wherein the second indication information is used to indicate the AI ​​object management operation.

[0469] Optionally, the information of the AI ​​object supported by the terminal includes at least one of the following: an identifier of the AI ​​object supported by the terminal, file information of the AI ​​object supported by the terminal, input of the AI ​​object supported by the terminal, and output of the AI ​​object supported by the terminal; wherein the file information of the AI ​​object supported by the terminal includes at least one of parameter information and structure information of the AI ​​object supported by the terminal;

[0470] or,

[0471] The information of the AI ​​object currently activated by the terminal includes at least one of the following: an identifier of the AI ​​object currently activated by the terminal, file information of the AI ​​object currently activated by the terminal, an input of the AI ​​object currently activated by the terminal, and an output of the AI ​​object currently activated by the terminal; wherein the file information of the AI ​​object currently activated by the terminal includes at least one of parameter information and structure information of the AI ​​object currently activated by the terminal;

[0472] or,

[0473] The information of the first AI object includes at least one of the following: an identifier of the first AI object, file information of the first AI object, input of the first AI object, and output of the first AI object. The file information of the first AI object includes at least one of parameter information and structure information of the first AI object.

[0474] Optionally, the applicable scope of the terminal performing AI object reasoning includes at least one of the following: a cell range in which the terminal performs AI object reasoning, and a frequency range in which the terminal performs AI object reasoning.

[0475] Optionally, the first data includes at least one of an input of an AI object and an output of an AI object;

[0476] or,

[0477] The second data includes input of the AI ​​object.

[0478] Optionally, the input of the AI ​​object includes at least one of the following: cell signal quality of the serving cell, beam signal quality of the serving cell, cell signal quality of the neighboring cell, and beam signal quality of the neighboring cell;

[0479] or,

[0480] The output of the AI ​​object includes at least one of the following: an identifier of the optimal cell, an identifier of the optimal beam, a flag indicating whether a measurement event is met, a flag indicating whether a radio link failure RLF occurs, and a flag indicating whether a handover failure HOF occurs.

[0481] Optionally, the RRM measurement result includes at least one of the following:

[0482] a measurement of the cell signal quality of the serving cell;

[0483] A measurement of the beam signal quality of the serving cell;

[0484] a measurement of the cell signal quality of the candidate cell;

[0485] The measured value of the beam signal quality of the candidate cell;

[0486] a measurement of the cell signal quality of the target cell;

[0487] The measured value of the beam signal quality of the target cell;

[0488] The identifier of the best beam of the candidate cell obtained by measurement;

[0489] The identifier of the measured optimal beam of the target cell.

[0490] Optionally, the RRM measurement prediction result includes at least one of the following:

[0491] A predicted value of the cell signal quality of the serving cell;

[0492] The predicted value of the beam signal quality of the serving cell;

[0493] The predicted value of the cell signal quality of the candidate cell;

[0494] The predicted value of the beam signal quality of the candidate cell;

[0495] The predicted value of the cell signal quality of the target cell;

[0496] The predicted value of the beam signal quality of the target cell;

[0497] The identifier of the predicted optimal beam of the candidate cell;

[0498] The identifier of the predicted optimal beam of the target cell.

[0499] Optionally, the monitoring result of the AI ​​object includes at least one of the following:

[0500] RRM measurement prediction accuracy;

[0501] The prediction accuracy of the optimal cell;

[0502] The prediction accuracy of the optimal beam;

[0503] HOF prediction accuracy;

[0504] RLF prediction accuracy;

[0505] Measuring the accuracy of event predictions;

[0506] The number or probability of abnormal events occurring in the first time period;

[0507] the switching delay or interruption duration in the second time period;

[0508] The number of switching times per unit time or within the third time period.

[0509] The AI ​​object management device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device can be a network-side device, or it can be a device other than a network-side device. For example, the network-side device can include but is not limited to the types of network-side devices 12 listed above. Other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0510] The AI ​​object management device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 6 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0511] Please refer to FIG10 , which is a structural diagram of an AI object management device provided in an embodiment of the present application. As shown in FIG10 , the AI ​​object management device 1000 includes:

[0512] An execution module 1001 is configured to execute a second operation when the handover is executed or completed;

[0513] The second operation includes at least one of the following:

[0514] Deactivate the AI ​​object activated in the source cell;

[0515] Release the AI ​​object from the source cell;

[0516] Release the AI ​​object that applies only to the source cell;

[0517] Release AI objects that are out of scope;

[0518] Release collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0519] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0520] The AI ​​object includes an AI model or an AI function.

[0521] The AI ​​object management device in the embodiments of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal or other device other than a terminal. For example, the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0522] The AI ​​object management device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 7 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0523] Optionally, as shown in Figure 11, an embodiment of the present application further provides a communication device 1100, including a processor 1101 and a memory 1102, wherein the memory 1102 stores a program or instruction that can be run on the processor 1101. For example, when the communication device 1100 is a source node, the program or instruction is executed by the processor 1101 to implement the various steps of the above-mentioned source node side AI object management method embodiment, and can achieve the same technical effect. When the communication device 1100 is a first node, the program or instruction is executed by the processor 1101 to implement the various steps of the above-mentioned first node side AI object management method embodiment, and can achieve the same technical effect. When the communication device 1100 is a terminal, the program or instruction is executed by the processor 1101 to implement the various steps of the above-mentioned terminal side AI object management method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0524] The embodiment of the present application also provides a source node, including a processor and a communication interface, wherein the communication interface is used to send first information to a first node during a switching preparation process; wherein the first node includes a candidate node or a target node, and the first information includes at least one of the following: information about AI objects supported by the terminal; information about the AI ​​object currently activated by the terminal; information about the first AI object; first data collected by the source node that has not been used for AI object training or AI object monitoring; second data collected by the source node that has not been used for AI object reasoning; the applicable scope of AI object reasoning by the terminal; radio resource management RRM measurement results; target prediction results; monitoring results of AI objects; the AI ​​object includes an AI model or AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object. This source node embodiment corresponds to the above-mentioned source node method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this source node embodiment and can achieve the same technical effect.

[0525] Specifically, embodiments of the present application also provide a source node. As shown in Figure 12, source node 1200 includes an antenna 1201, a radio frequency device 1202, a baseband device 1203, a processor 1204, and a memory 1205. Antenna 1201 is connected to radio frequency device 1202. In the uplink direction, radio frequency device 1202 receives information via antenna 1201 and sends the received information to baseband device 1203 for processing. In the downlink direction, baseband device 1203 processes the information to be transmitted and sends it to radio frequency device 1202. Radio frequency device 1202 processes the received information and then sends it through antenna 1201.

[0526] The method executed by the source node in the above embodiment may be implemented in the baseband device 1203 , which includes a baseband processor.

[0527] The baseband device 1203 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 12, one of which is a baseband processor, for example, which is connected to the memory 1205 through a bus interface to call the program in the memory 1205 and execute the network side device operations shown in the above method embodiment.

[0528] The source node may further include a network interface 1206, such as a Common Public Radio Interface (CPRI).

[0529] Specifically, the source node 1200 of the embodiment of the present application also includes: instructions or programs stored in the memory 1205 and executable on the processor 1204. The processor 1204 calls the instructions or programs in the memory 1205 to execute the method of execution of each module shown in Figure 8 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0530] The embodiment of the present application also provides a first node, including a processor and a communication interface, wherein the communication interface is used to receive first information sent by a source node during a handover preparation process; wherein the first node includes a candidate node or a target node, and the first information includes at least one of the following: information about AI objects supported by the terminal; information about the AI ​​object currently activated by the terminal; information about the first AI object; first data collected by the source node that has not been used for AI object training or AI object monitoring; second data collected by the source node that has not been used for AI object reasoning; the applicable scope of AI object reasoning by the terminal; radio resource management RRM measurement results; target prediction results; monitoring results of AI objects; the AI ​​object includes an AI model or AI function, the first AI object is an AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI ​​object and an event prediction result based on the AI ​​object. This first node embodiment corresponds to the above-mentioned first node method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this first node embodiment and can achieve the same technical effect.

[0531] Specifically, an embodiment of the present application further provides a first node. As shown in Figure 13, the first node 1300 includes: an antenna 1301, a radio frequency device 1302, a baseband device 1303, a processor 1304, and a memory 1305. Antenna 1301 is connected to radio frequency device 1302. In the uplink direction, radio frequency device 1302 receives information via antenna 1301 and sends the received information to baseband device 1303 for processing. In the downlink direction, baseband device 1303 processes the information to be transmitted and sends it to radio frequency device 1302. Radio frequency device 1302 processes the received information and then sends it through antenna 1301.

[0532] The method executed by the first node in the above embodiment may be implemented in the baseband device 1303 , which includes a baseband processor.

[0533] The baseband device 1303 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 13, one of which is a baseband processor, for example, which is connected to the memory 1305 through a bus interface to call the program in the memory 1305 and execute the network side device operations shown in the above method embodiment.

[0534] The first node may further include a network interface 1306 , which may be, for example, a Common Public Radio Interface (CPRI).

[0535] Specifically, the first node 1300 of the embodiment of the present application also includes: instructions or programs stored in the memory 1305 and executable on the processor 1304. The processor 1304 calls the instructions or programs in the memory 1305 to execute the methods executed by the modules shown in FIG9 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0536] The embodiment of the present application also provides a terminal, including a processor and a communication interface, wherein the processor is configured to perform a second operation when a handover is performed or completed; wherein the second operation includes at least one of the following: deactivating an AI object activated in a source cell; releasing an AI object from a source cell; releasing an AI object applicable only to a source cell; releasing an AI object beyond the applicable scope; releasing collected data corresponding to a second AI object, wherein the second AI object is an AI object that has been deactivated or released by the terminal; reporting the actual result corresponding to the monitoring result of the third AI object to a target cell when the actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, wherein the third AI object is an AI object that remains activated when the terminal is switched from the source cell to the target cell; and the AI ​​object includes an AI model or an AI function. This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this terminal embodiment and can achieve the same technical effect. Specifically, Figure 14 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0537] The terminal 1400 includes but is not limited to: a radio frequency unit 1401, a network module 1402, an audio output unit 1403, an input unit 1404, a sensor 1405, a display unit 1406, a user input unit 1407, an interface unit 1408, a memory 1409 and at least some of the components of the processor 1410.

[0538] Those skilled in the art will appreciate that the terminal 1400 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1410 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG14 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0539] It should be understood that in an embodiment of the present application, the input unit 1404 may include a graphics processing unit (GPU) 14041 and a microphone 14042, and the graphics processor 14041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1406 may include a display panel 14061, and the display panel 14061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1407 includes a touch panel 14071 and at least one of other input devices 14072. The touch panel 14071 is also called a touch screen. The touch panel 14071 may include two parts: a touch detection device and a touch controller. Other input devices 14072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and an operating stick, which will not be repeated here.

[0540] In the embodiment of the present application, after receiving downlink data from a network-side device, the radio frequency unit 1401 may transmit the data to the processor 1410 for processing. Furthermore, the radio frequency unit 1401 may send uplink data to the network-side device. Typically, the radio frequency unit 1401 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0541] The memory 1409 can be used to store software programs or instructions and various data. The memory 1409 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1409 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1409 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0542] Processor 1410 may include one or more processing units. Optionally, processor 1410 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 1410.

[0543] The processor 1410 is configured to perform a second operation when the handover is performed or the handover is completed; wherein the second operation includes at least one of the following:

[0544] Deactivate the AI ​​object activated in the source cell;

[0545] Release the AI ​​object from the source cell;

[0546] Release the AI ​​object that applies only to the source cell;

[0547] Release AI objects that are out of scope;

[0548] releasing collected data corresponding to a second AI object, where the second AI object is an AI object that has been deactivated or released by the terminal;

[0549] If an actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, reporting the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is an AI object that remains activated when the terminal is handed over from the source cell to the target cell;

[0550] The AI ​​object includes an AI model or an AI function.

[0551] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the aforementioned method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0552] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned AI object management method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0553] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0554] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned AI object management method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0555] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0556] An embodiment of the present application further provides a computer program / program product, which includes a computer program or computer instructions. The computer program or computer instructions are executed by at least one processor to implement the various processes of the above-mentioned AI object management method embodiment and can achieve the same technical effect. To avoid repetition, they are not described here.

[0557] An embodiment of the present application also provides an AI object management system, including: a source node, a first node, and a terminal. The source node is used to execute the various processes shown in Figure 5 and the above-mentioned method embodiments, the first node is used to execute the various processes shown in Figure 6 and the above-mentioned method embodiments, and the terminal is used to execute the various processes shown in Figure 7 and the above-mentioned method embodiments, and can achieve the same technical effects. To avoid repetition, they will not be repeated here.

[0558] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0559] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0560] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. An AI object management method, comprising: During the handover preparation process, a source node sends first information to a first node; Wherein, the first node includes a candidate node or a target node, and the first information includes at least one of the following: Information about AI objects supported by the terminal; Information about the currently active AI object of the terminal; Information about the first AI object; First data collected by the source node that has not been used for AI object training or AI object monitoring; Second data collected by the source node that has not been used for AI object inference; The applicable scope of the terminal for AI object inference; Radio Resource Management (RRM) measurement results; Target prediction results; Monitoring results of AI objects; The AI object includes an AI model or an AI function, the first AI object is an AI object suitable for the terminal saved by the source node, and the target prediction results include at least one of the RRM measurement prediction results based on the AI object and the event prediction results based on the AI object.

2. The method according to claim 1, wherein, The information about the AI objects supported by the terminal includes at least one of the following: the identifier of the AI objects supported by the terminal, the file information of the AI objects supported by the terminal, the input of the AI objects supported by the terminal, the output of the AI objects supported by the terminal; wherein, the file information of the AI objects supported by the terminal includes at least one of the parameter information and the structure information of the AI objects supported by the terminal; Or, The information about the currently active AI object of the terminal includes at least one of the following: the identifier of the currently active AI object of the terminal, the file information of the currently active AI object of the terminal, the input of the currently active AI object of the terminal, the output of the currently active AI object of the terminal; wherein, the file information of the currently active AI object of the terminal includes at least one of the parameter information and the structure information of the currently active AI object of the terminal; Or, The information about the first AI object includes at least one of the following: the identifier of the first AI object, the file information of the first AI object, the input of the first AI object, the output of the first AI object; wherein, the file information of the first AI object includes at least one of the parameter information and the structure information of the first AI object.

3. The method according to claim 1 or 2, wherein The applicable scope of the terminal for AI object inference includes at least one of the following: the cell range of the terminal for AI object inference, the frequency band range of the terminal for AI object inference.

4. The method according to any one of claims 1 to 3, wherein The first data includes at least one of the input of the AI object and the output of the AI object; Or, The second data includes the input of the AI object.

5. The method according to claim 4, wherein, The input of the AI object includes at least one of the following: the cell signal quality of the serving cell, the beam signal quality of the serving cell, the cell signal quality of the neighboring cell, the beam signal quality of the neighboring cell; Or, The output of the AI object includes at least one of the following: the identifier of the optimal cell, the identifier of the optimal beam, the flag indicating whether the measurement event is satisfied, the flag indicating whether a Radio Link Failure (RLF) occurs, the flag indicating whether a Handover Failure (HOF) occurs.

6. The method according to any one of claims 1 to 5, wherein The RRM measurement results include at least one of the following: The measured value of the cell signal quality of the serving cell; Measurement value of the beam signal quality of the serving cell; Measurement value of the cell signal quality of the candidate cell; Measurement value of the beam signal quality of the candidate cell; Measurement value of the cell signal quality of the target cell; Measurement value of the beam signal quality of the target cell; Identifier of the optimal beam of the measured candidate cell; Identifier of the optimal beam of the measured target cell.

7. The method according to any one of claims 1 to 6, wherein The RRM measurement prediction result includes at least one of the following: Predicted value of the cell signal quality of the serving cell; Predicted value of the beam signal quality of the serving cell; Predicted value of the cell signal quality of the candidate cell; Predicted value of the beam signal quality of the candidate cell; Predicted value of the cell signal quality of the target cell; Predicted value of the beam signal quality of the target cell; Identifier of the optimal beam of the predicted candidate cell; Identifier of the optimal beam of the predicted target cell.

8. The method according to any one of claims 1 to 7, wherein, The monitoring result of the AI object includes at least one of the following: RRM measurement prediction accuracy; Prediction accuracy of the optimal cell; Prediction accuracy of the optimal beam; Prediction accuracy of the HOF; Prediction accuracy of the RLF; Prediction accuracy of the measurement event; Number or probability of abnormal events occurring within the first time period; Handover delay or interruption duration within the second time period; Number of handovers per unit time or within the third time period.

9. The method according to any one of claims 1 to 8, wherein The method further includes: The source node receives the capability information of the terminal reported by the terminal, where the capability information is used to indicate the AI objects supported by the terminal.

10. An AI object management method, including: During the handover preparation process, the first node receives the first information sent by the source node; Wherein, the first node includes a candidate node or a target node, and the first information includes at least one of the following: Information of the AI objects supported by the terminal; Information of the currently active AI objects of the terminal; Information of the first AI object; The first data collected by the source node that has not been used for AI object training or AI object monitoring; The second data collected by the source node that has not been used for AI object inference; Applicable scope for the terminal to perform AI object inference; Radio resource management (RRM) measurement results; Target prediction results; Monitoring results of AI objects; The AI object includes an AI model or an AI function, the first AI object is the AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of the RRM measurement prediction result based on the AI object and the event prediction result based on the AI object.

11. The method according to claim 10, wherein, The method further includes at least one of the following: The first node sends the configuration information of the first cell to the terminal through the source node; wherein, the first cell includes a candidate cell or a target cell, and the configuration information of the first cell is determined according to the first information; The first node performs a first operation according to the first information; wherein, the first operation includes at least one of the following: AI object training, AI object monitoring, AI object inference, AI object management operation; the AI object management operation includes activation, deactivation, handover, or fallback to non-AI operation.

12. The method according to claim 11, wherein, The configuration information of the first cell carries first indication information for indicating the AI object management operation.

13. The method according to claim 11, wherein, The method further includes: When the handover is completed, the first node sends second indication information to the terminal; wherein, the second indication information is used to indicate the AI object management operation.

14. The method according to any one of claims 1 to 13, wherein The information of the AI objects supported by the terminal includes at least one of the following: the identifier of the AI objects supported by the terminal, the file information of the AI objects supported by the terminal, the input of the AI objects supported by the terminal, the output of the AI objects supported by the terminal; wherein, the file information of the AI objects supported by the terminal includes at least one of the parameter information and the structure information of the AI objects supported by the terminal; Or, The information of the currently activated AI objects of the terminal includes at least one of the following: the identifier of the currently activated AI objects of the terminal, the file information of the currently activated AI objects of the terminal, the input of the currently activated AI objects of the terminal, the output of the currently activated AI objects of the terminal; wherein, the file information of the currently activated AI objects of the terminal includes at least one of the parameter information and the structure information of the currently activated AI objects of the terminal; Or, The information of the first AI object includes at least one of the following: the identifier of the first AI object, the file information of the first AI object, the input of the first AI object, the output of the first AI object; wherein, the file information of the first AI object includes at least one of the parameter information and the structure information of the first AI object.

15. The method according to any one of claims 10 to 14, wherein The applicable scope of the terminal for performing AI object inference includes at least one of the following: the cell range for the terminal to perform AI object inference, the frequency band range for the terminal to perform AI object inference.

16. The method according to any one of claims 10 to 15, wherein The first data includes at least one of the input of the AI object and the output of the AI object; Or, The second data includes the input of the AI object.

17. The method according to claim 16, wherein, The input of the AI object includes at least one of the following: the cell signal quality of the serving cell, the beam signal quality of the serving cell, the cell signal quality of the neighboring cell, the beam signal quality of the neighboring cell; Or, The output of the AI object includes at least one of the following: the identifier of the optimal cell, the identifier of the optimal beam, the flag indicating whether the measurement event is satisfied, the flag indicating whether a radio link failure (RLF) occurs, the flag indicating whether a handover failure (HOF) occurs.

18. The method according to any one of claims 10 to 17, wherein The RRM measurement result includes at least one of the following: The measured value of the cell signal quality of the serving cell; The measured value of the beam signal quality of the serving cell; The measured value of the cell signal quality of the candidate cell; The measured value of the beam signal quality of the candidate cell; The measured value of the cell signal quality of the target cell; The measured value of the beam signal quality of the target cell; The identifier of the optimal beam of the measured candidate cell; The identifier of the optimal beam of the measured target cell.

19. The method according to any one of claims 10 to 18, wherein The RRM measurement prediction result includes at least one of the following: The predicted value of the cell signal quality of the serving cell; The predicted value of the beam signal quality of the serving cell; The predicted value of the cell signal quality of the candidate cell; The predicted value of the beam signal quality of the candidate cell; Predicted value of the cell signal quality of the target cell; Predicted value of the beam signal quality of the target cell; Identifier of the optimal beam of the candidate cell obtained by prediction; Identifier of the optimal beam of the target cell obtained by prediction.

20. The method according to any one of claims 10 to 19, wherein Monitoring result of the AI object, including at least one of the following: RRM measurement prediction accuracy; Prediction accuracy of the optimal cell; Prediction accuracy of the optimal beam; Prediction accuracy of HOF; Prediction accuracy of RLF; Prediction accuracy of measurement events; Number or probability of abnormal events occurring within the first time period; Handover delay or interruption duration within the second time period; Number of handovers per unit time or within the third time period.

21. An AI object management method, including: When a handover is executed or completed, the terminal executes a second operation; Wherein, the second operation includes at least one of the following: Deactivate the AI object activated in the source cell; Release the AI object from the source cell; Release the AI object that is only applicable to the source cell; Release the AI object that exceeds the applicable scope; Release the collected data corresponding to the second AI object, where the second AI object is the AI object that has been deactivated or released by the terminal; When the actual result corresponding to the monitoring result of the third AI object has not been reported to the source cell, report the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is the AI object that remains active when the terminal switches from the source cell to the target cell; The AI object includes an AI model or an AI function.

22. An AI object management device, including: A sending module, configured to send first information to a first node during the handover preparation process; Wherein, the first node includes a candidate node or a target node, and the first information includes at least one of the following: Information of the AI objects supported by the terminal; Information of the AI objects currently activated by the terminal; Information of the first AI object; First data collected by the source node that has not been used for AI object training or AI object monitoring; Second data collected by the source node that has not been used for AI object inference; Applicable scope for the terminal to perform AI object inference; Radio resource management (RRM) measurement results; Target prediction results; Monitoring results of AI objects; The AI object includes an AI model or an AI function, the first AI object is the AI object suitable for the terminal saved by the source node, and the target prediction results include at least one of the RRM measurement prediction results based on the AI object and the event prediction results based on the AI object.

23. An AI object management device, including: A receiving module, configured to receive the first information sent by the source node during the handover preparation process; Wherein, the first information includes at least one of the following: Information of the AI objects supported by the terminal; Information of the AI objects currently activated by the terminal; Information of the first AI object; First data collected by the source node that has not been used for AI object training or AI object monitoring; Second data collected by the source node that has not been used for AI object inference; Applicable scope for the terminal to perform AI object inference; Radio resource management (RRM) measurement results; Target prediction results; Monitoring results of the AI object; The AI object includes an AI model or an AI function. The first AI object is the AI object applicable to the terminal saved by the source node, and the target prediction result includes at least one of an RRM measurement prediction result based on the AI object and an event prediction result based on the AI object.

24. An AI object management device, comprising: An execution module, configured to perform a second operation when a handover is executed or the handover is completed; Wherein, the second operation includes at least one of the following: Deactivate the AI object activated in the source cell; Release the AI object from the source cell; Release the AI object only applicable to the source cell; Release the AI object beyond the applicable scope; Release the acquisition data corresponding to the second AI object, where the second AI object is the AI object deactivated or released by the terminal; When the actual result corresponding to the monitoring result of the third AI object is not reported to the source cell, report the actual result corresponding to the monitoring result of the third AI object to the target cell, where the third AI object is the AI object that remains activated when the terminal switches from the source cell to the target cell; The AI object includes an AI model or an AI function.

25. A source node, comprising a processor and a memory, where the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the AI object management method according to any one of claims 1 to 9 are implemented.

26. A first node, comprising a processor and a memory, where the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the AI object management method according to any one of claims 10 to 20 are implemented.

27. A terminal, comprising a processor and a memory, where the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the AI object management method according to claim 21 are implemented.

28. A readable storage medium, where a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the AI object management method according to any one of claims 1 to 9 are implemented, or the steps of the AI object management method according to any one of claims 10 to 20 are implemented, or the steps of the AI object management method according to claim 21 are implemented.

29. A computer program product, where the computer program product includes a computer program or a computer instruction, and when the computer program or the computer instruction is executed by at least one processor, the steps of the AI object management method according to any one of claims 1 to 9 are implemented, or the steps of the AI object management method according to any one of claims 10 to 20 are implemented, or the steps of the AI object management method according to claim 21 are implemented.

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