Ai object monitoring method and apparatus, terminal and network side device

Abstract

The present application belongs to the technical field of communications. Disclosed are an AI object monitoring method and apparatus, a terminal and a network side device. The AI object monitoring method in the embodiments of the present application comprises: a terminal performs a first operation on the basis of a first parameter, or the terminal sends a first measurement report to a network side device, wherein the first operation comprises at least one of AI object monitoring and AI object monitoring result reporting, the first parameter comprises at least one of the following: an AI object monitoring index parameter and an AI object monitoring result reporting parameter, the first measurement report is used for the network side device to monitor an AI object, the first measurement report comprises an AI object-based first prediction result, the first prediction result comprises at least one of a radio resource management (RRM) measurement prediction result and an event prediction result, the AI object comprises an AI model or an AI function, and the AI object is an AI object for mobility management.

Description

AI object monitoring method, device, terminal and network-side equipment

[0001] Cross-references

[0002] This disclosure claims priority to Chinese patent application number 202410104942.8 filed on January 24, 2024, entitled “AI object monitoring method, device, terminal and network-side equipment”, the entire contents of which 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 monitoring method, apparatus, terminal, and network-side equipment. Background Art

[0004] Currently, in mobile communication networks, tasks can be performed or services can be provided based on artificial intelligence (AI). For example, mobility management-related processing such as radio resource management (RRM) measurement prediction and event prediction can be performed based on AI models or AI functions. However, the related art lacks a corresponding solution for how to monitor AI models or AI functions in mobility management to ensure the reliability of prediction results, which affects the reliability of the prediction results of AI models or AI functions. Summary of the Invention

[0005] The embodiments of the present application provide an AI object monitoring method, apparatus, terminal, and network-side equipment, which can monitor AI models or AI functions during mobility management to improve the reliability of the prediction results of the AI ​​models or AI functions.

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

[0007] The terminal performs a first operation according to the first parameter, or the terminal sends a first measurement report to the network side device;

[0008] The first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the AI ​​object monitoring result;

[0009] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0010] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0011] In a second aspect, an AI object monitoring device is provided, the device comprising:

[0012] A first execution module, configured to perform a first operation according to a first parameter, or send a first measurement report to a network-side device;

[0013] The first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the AI ​​object monitoring result;

[0014] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0015] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0016] In a third aspect, an AI object monitoring method is provided, the method comprising:

[0017] The network side device sends the first parameter to the terminal, or the network side device receives a first measurement report sent by the terminal;

[0018] The first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and reporting of monitoring results of the AI ​​object, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object;

[0019] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0020] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0021] In a fourth aspect, an AI object monitoring device is provided, the device comprising:

[0022] A transmission module, configured to send a first parameter to a terminal, or the network-side device to receive a first measurement report sent by the terminal;

[0023] The first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and reporting of monitoring results of the AI ​​object, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object;

[0024] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0025] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0026] In a fifth aspect, a terminal is provided, comprising 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.

[0027] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the processor is configured to perform a first operation according to a first parameter, or the communication interface is configured to send a first measurement report to a network-side device;

[0028] The first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the AI ​​object monitoring result;

[0029] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0030] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0031] In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the third aspect are implemented.

[0032] In an eighth aspect, a network-side device is provided, including a processor and a communication interface, wherein the communication interface is configured to send a first parameter to a terminal, or receive a first measurement report sent by the terminal;

[0033] The first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and reporting of monitoring results of the AI ​​object, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object;

[0034] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0035] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0036] In the ninth aspect, an AI object monitoring system is provided, comprising: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the AI ​​object monitoring method as described in the first aspect, and the network-side device can be used to execute the steps of the AI ​​object monitoring method as described in the third aspect.

[0037] In the tenth 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.

[0038] In the eleventh aspect, a chip is provided, 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 steps of the method described in the first aspect, or to implement the steps of the method described in the third aspect.

[0039] In the twelfth aspect, a computer program / program product is provided, which includes 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 to implement the steps of the method described in the third aspect.

[0040] In an embodiment of the present application, the terminal performs a first operation according to a first parameter, or the terminal sends a first measurement report to a network side device; wherein the first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object, a reporting parameter of the monitoring result of the AI ​​object; the first measurement report is used by the network side device to monitor the AI ​​object, and the first measurement report includes a first prediction result based on the AI ​​object, and the first prediction result includes at least one of a radio resource management RRM measurement prediction result and an event prediction result; the AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management, that is, in an embodiment of the present application, the terminal can perform at least one of AI object monitoring and AI object monitoring result reporting according to the first parameter, or the terminal can report the prediction result based on the AI ​​object to the network side device, so that the network side device monitors the AI ​​object based on the prediction result of the AI ​​object, thereby realizing monitoring of the AI ​​model or AI function during the mobility management process, which is conducive to improving the reliability of the prediction result of the AI ​​model or AI function. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0042] FIG2a is a schematic diagram of the structure of a neural network provided in an embodiment of the present application;

[0043] FIG2 b is a schematic diagram of the structure of a neuron provided in an embodiment of the present application;

[0044] FIG3 is a schematic diagram of an AI / ML functional architecture provided in an embodiment of the present application;

[0045] FIG4 is a flow chart of an AI object monitoring method provided in an embodiment of the present application;

[0046] FIG5 is a flowchart of another AI object monitoring method provided in an embodiment of the present application;

[0047] FIG6 is a structural diagram of an AI object monitoring device provided in an embodiment of the present application;

[0048] FIG7 is a structural diagram of another AI object monitoring device provided in an embodiment of the present application;

[0049] FIG8 is a structural diagram of a communication device provided in an embodiment of the present application;

[0050] FIG9 is a structural diagram of a terminal provided in an embodiment of the present application;

[0051] FIG10 is a structural diagram of a network-side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AP) or a wireless fidelity (WiFi) node, etc.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.

[0057] 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.

[0058] For ease of understanding, some of the contents involved in the embodiments of this application are described below:

[0059] 1. Artificial Intelligence (AI)

[0060] 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.

[0061] 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).

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 2. AI Unit / AI Model

[0067] 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.

[0068] 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.

[0069] AI functionality: an AI algorithm function that can include multiple AI models.

[0070] 3. AI / ML Framework

[0071] The Air Interface AI project, Release 18, studied the AI / ML framework, as shown in Figure 3. The main process is as follows:

[0072] Data Collection: Responsible for providing input data for Model Training, Management and Interence;

[0073] 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;

[0074] Management: responsible for model selection / activation / deactivation / switching / fallback, etc.

[0075] Inference: Provides the output of applying an AI / ML model or AI / ML function.

[0076] Model Storage: responsible for saving trained / updated models

[0077] Model Transfer / Delivery: Responsible for delivering the AI / ML model to the inference function node.

[0078] 4. RRM measurement reporting

[0079] The measurement configuration mainly consists of the measurement object, reporting configuration and measurement identification (ID);

[0080] Measurement Object: The frequency point to be measured

[0081] 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.

[0082] 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.

[0083] Optionally, the above three are linked together in the following way:

[0084] The reporting configuration can include event-triggered reporting. The events defined in NR can be found in Table 1, which include the following events:

[0085] Table 1

[0086] Taking the A3 event as an example, the meanings of the parameters for the entry and exit conditions are as follows:

[0087] Mn: Neighboring cell measurement result, without considering any offset;

[0088] Ofn: Neighborhood measurement object specific offset;

[0089] Ocn: Neighboring cell-level specific offset;

[0090] Mp: SpCell (primary serving cell) measurement result, without considering any offset;

[0091] Ofp: SpCell measurement object specific offset;

[0092] Ocp: SpCell cell-level specific offset;

[0093] Hys: hysteresis parameter of the event;

[0094] Off: The offset parameter of the event.

[0095] 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;

[0096] 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.

[0097] 5. Conditional Handover

[0098] 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.

[0099] Take the parameters in NR as an example:

[0100] 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:

[0101] 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 events A3 (condEventA3), A4 (condEventA4), or 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 within the timeToTrigger timeframe meets the event entry conditions, the UE selects the cells that meet the conditions as triggering cells and selects one of the triggering cells to execute conditional reconfiguration.

[0102] The following describes in detail the AI ​​object monitoring method provided in the embodiments of the present application through some embodiments and their application scenarios in combination with the accompanying drawings.

[0103] Please refer to FIG4 , which is a flowchart of an AI object monitoring method provided in an embodiment of the present application. The method can be executed by a terminal, as shown in FIG4 , and includes the following steps:

[0104] Step 401: The terminal performs a first operation according to a first parameter, or the terminal sends a first measurement report to a network-side device;

[0105] The first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the AI ​​object monitoring result;

[0106] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0107] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0108] In this embodiment, the first parameter may be configured by a network-side device or predefined by a protocol. The first parameter includes at least one of a monitoring indicator parameter of an AI object and a reporting parameter of a monitoring result of an AI object.

[0109] Exemplarily, the monitoring indicator parameters of the above-mentioned AI object may include but are not limited to RRM measurement prediction accuracy, prediction accuracy of optimal cell / beam, prediction accuracy of measurement events, etc.

[0110] The monitoring result of the AI ​​object can be understood as the result obtained by monitoring the AI ​​object based on the monitoring indicator parameters of the AI ​​object. For example, if the monitoring indicator parameters include RRM measurement prediction accuracy, the monitoring result of the AI ​​object can include the value of the RRM measurement prediction accuracy. If the monitoring indicator parameters include the prediction accuracy of the measurement event, the monitoring result of the AI ​​object can include the value of the prediction accuracy of the measurement event.

[0111] Exemplarily, the reporting parameters of the monitoring results of the above-mentioned AI object may include but are not limited to at least one of the reporting period, reporting event, and reporting threshold of the monitoring results of the above-mentioned AI object.

[0112] Exemplarily, when the first parameter includes a monitoring indicator parameter of an AI object, the terminal may monitor the AI ​​object based on the monitoring indicator parameter of the AI ​​object; when the first parameter includes a reporting parameter of a monitoring result of the AI ​​object, the terminal may report the monitoring result of the AI ​​object based on the reporting parameter of the monitoring result of the AI ​​object; when the first parameter includes a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object, the terminal may monitor the AI ​​object based on the monitoring indicator parameter of the AI ​​object, and report the monitoring result of the AI ​​object based on the reporting parameter of the monitoring result of the AI ​​object.

[0113] The above-mentioned first measurement report may include a prediction result based on the AI ​​object, that is, a first prediction result, and the first prediction result includes at least one of an RRM measurement prediction result and an event prediction result. Exemplarily, the above-mentioned RRM measurement prediction result can be understood as the result obtained by predicting the RRM measurement quantity based on the AI ​​object, for example, the cell signal quality prediction result of at least one of the serving cell, the neighboring cell signal, the candidate cell, and the target cell; the above-mentioned event prediction result can be understood as the result obtained by predicting the event based on the AI ​​object, for example, at least one of the prediction result of the measurement event, the prediction result of the handover failure (HOF), and the radio link failure (RLF).

[0114] It is understandable that the terminal sends a first measurement report to the network side device, so that when the network side device receives the first measurement report, it can monitor the AI ​​object based on the first prediction result to obtain the monitoring result of the AI ​​object. For example, if the terminal sends the HOF prediction result to the network side device, the network side device can monitor the AI ​​object used for HOF prediction based on the HOF prediction result and the actual result of HOF (i.e., whether HOF actually occurs) to obtain the prediction accuracy of the AI ​​object for HOF; if the terminal sends the RLF prediction result to the network side device, the network side device can monitor the AI ​​object used for RLF prediction based on the RLF prediction result and the actual result of RLF (i.e., whether RLF actually occurs) to obtain the prediction accuracy of the AI ​​object for RLF; if the terminal sends the RRM measurement prediction result to the network side device, the network side device can monitor the AI ​​object used for RRM measurement prediction based on the RRM measurement prediction result and the actual result of RRM measurement (i.e., the RRM measurement result obtained by the terminal performing RRM measurement) to obtain the RRM measurement prediction accuracy of the AI ​​object.

[0115] It should be noted that the above-mentioned AI object for mobility management may include at least one AI object. For example, the above-mentioned AI object for mobility management may include at least one of an AI object for RRM measurement prediction, an AI object for measurement event prediction, an AI object for HOF prediction, an AI object for RLF prediction, and the like.

[0116] In an embodiment of the present application, the terminal performs a first operation according to a first parameter, or the terminal sends a first measurement report to a network side device; wherein the first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object, a reporting parameter of the monitoring result of the AI ​​object; the first measurement report is used by the network side device to monitor the AI ​​object, and the first measurement report includes a first prediction result based on the AI ​​object, and the first prediction result includes at least one of a radio resource management RRM measurement prediction result and an event prediction result; the AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management, that is, in an embodiment of the present application, the terminal can perform at least one of AI object monitoring and AI object monitoring result reporting according to the first parameter, or the terminal can report the prediction result based on the AI ​​object to the network side device, so that the network side device monitors the AI ​​object based on the prediction result of the AI ​​object, thereby realizing monitoring of the AI ​​model or AI function during the mobility management process, which is conducive to improving the reliability of the prediction result of the AI ​​model or AI function.

[0117] Optionally, the monitoring indicator parameters of the AI ​​object include at least one of the following:

[0118] RRM measurement prediction accuracy;

[0119] The prediction accuracy of the best N cells, where N is a positive integer;

[0120] The prediction accuracy of the optimal M beams, where M is a positive integer;

[0121] The prediction accuracy of HOF switching failure;

[0122] The prediction accuracy of radio link failure (RLF);

[0123] Measuring the accuracy of event predictions;

[0124] The number or probability of abnormal events occurring in the first time period;

[0125] the switching delay or interruption duration in the second time period;

[0126] The number of switching times per unit time or within the third time period.

[0127] Exemplarily, the above-mentioned RRM measurement prediction accuracy may include the prediction error of RSRP / RSRQ / SINR at the cell level or beam level, etc., wherein the above-mentioned prediction error may include but is not limited to mean square error (MSE), root mean square error (RMSE), normalized mean square error (NMSE), etc.

[0128] The prediction of the above measurement event can be understood as predicting whether the measurement event will be satisfied.

[0129] Exemplarily, the switching delay or interruption duration may include an average switching delay or an average interruption duration in the second time period.

[0130] The above N and M may be values ​​predefined by the protocol, or values ​​configured by the network side device.

[0131] 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.

[0132] The following examples illustrate how to determine the parameters of each of the above monitoring indicators:

[0133] 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.

[0134] 2. The prediction accuracy of the optimal M beams or the optimal N cells is determined according to the following steps:

[0135] Step S11: The terminal predicts the optimal M beams or the optimal N cells at the second moment at the first moment;

[0136] Step S12: The terminal measures and obtains signal qualities of m beams or n cells at the second moment, where m>=M, n>=N, and the m beams include the aforementioned M beams, or the n cells include the aforementioned N cells.

[0137] Step S13: The terminal sorts the signals from high to low according to signal quality and determines the optimal M beams or N cells.

[0138] 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.

[0139] 3. The prediction accuracy of the measurement event is determined according to the following steps:

[0140] 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;

[0141] 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.

[0142] 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.

[0143] HOF prediction accuracy is determined according to the following steps:

[0144] Step S31: The terminal predicts at the fifth moment that HOF will occur in the second cell at the sixth moment;

[0145] 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.

[0146] Step S33: The terminal determines whether the HOF prediction is accurate based on the HOF prediction result and the actual result.

[0147] 5. The RLF prediction accuracy is determined according to the following steps:

[0148] Step S41: The terminal predicts at the seventh moment that RLF will occur in the third cell at the eighth moment.

[0149] 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.

[0150] Step S43: The terminal determines whether the RLF prediction is accurate based on the RLF prediction result and the actual result.

[0151] 6. Number of occurrences / probability of abnormal events in the first time period: The terminal counts the number of occurrences / probability of abnormal events in the first time period (predefined by network configuration or protocol).

[0152] 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.

[0153] 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).

[0154] This embodiment is conducive to ensuring the accuracy of the prediction results of the AI ​​object when the AI ​​object is monitored based on at least one of the RRM measurement prediction accuracy, the prediction accuracy of the optimal N cells, the prediction accuracy of the optimal M beams, the HOF prediction accuracy, the RLF prediction accuracy, and the measurement event prediction accuracy; when the AI ​​object is monitored based on at least one of the number or probability of abnormal events in the first time period, the switching delay or interruption duration in the second time period, and the number of switching per unit time or in the third time period, it is conducive to ensuring that the prediction results based on the AI ​​object can meet the system performance requirements.

[0155] Optionally, the abnormal event includes at least one of the following: ping-pong handover, premature handover, late handover, handover to an incorrect cell, handover failure, and radio link failure.

[0156] In this embodiment, the above-mentioned premature switching can be understood as the terminal switching to the target cell too early, resulting in a radio link failure in the target cell; the above-mentioned late switching can be understood as the terminal switching to the target cell too late, resulting in a radio link failure in the source cell.

[0157] Optionally, the reporting parameters of the monitoring results include at least one of the following: a reporting period, a reporting trigger event, and a reporting threshold.

[0158] The above-mentioned reporting period can be predefined by the protocol or configured by the network-side device. For example, the terminal can receive the reporting period configured by the network-side device and periodically report the monitoring results of the AI ​​object according to the configured reporting period.

[0159] The aforementioned reporting trigger event can be understood as the time for triggering the reporting of the monitoring results of the AI ​​object, which can be predefined by the protocol or configured by the network-side device. For example, the terminal can receive the reporting trigger event configured by the network-side device and trigger the reporting of the monitoring results of the AI ​​object when the aforementioned reporting trigger event is met.

[0160] The reporting threshold can be compared with the value of the monitoring indicator parameter to determine whether to trigger reporting of the AI ​​object's monitoring results. It can be predefined by the protocol or configured by the network-side device. For example, the terminal can receive the reporting threshold configured by the network-side device and trigger reporting of the AI ​​object's monitoring results when the value of the monitoring indicator parameter meets the reporting threshold.

[0161] Optionally, the reporting parameter of the monitoring result includes the reporting threshold, and the reporting threshold includes a threshold corresponding to each monitoring indicator parameter of the AI ​​object; and the terminal performs a first operation according to the first parameter, including at least one of the following:

[0162] When the RRM measurement prediction accuracy is less than or equal to the threshold corresponding to the RRM measurement prediction accuracy, the terminal reports the monitoring result of the AI ​​object;

[0163] When the prediction accuracy of the optimal N cells is less than or equal to the threshold corresponding to the optimal N cells, the terminal reports the monitoring result of the AI ​​object;

[0164] When the prediction accuracy of the optimal M beams is less than or equal to the threshold corresponding to the optimal M beams, the terminal reports the monitoring result of the AI ​​object;

[0165] When the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, the terminal reports the monitoring result of the AI ​​object;

[0166] When the number of occurrences or probability of an abnormal event within a first time period is greater than or equal to a threshold corresponding to the abnormal event, the terminal reports the monitoring result of the AI ​​object;

[0167] When the switching delay or the interruption duration in the second time period is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, the terminal reports the monitoring result of the AI ​​object;

[0168] When the number of switching times within the unit time or the third time period is greater than or equal to the threshold corresponding to the number of switching times, the terminal reports the monitoring result of the AI ​​object.

[0169] In this embodiment, the terminal determines whether to report the monitoring results of the AI ​​object based on the comparison results of the values ​​of each monitoring indicator parameter and the corresponding threshold. This is beneficial for reducing some unnecessary monitoring result reports while ensuring the accuracy of the prediction results of the AI ​​object, thereby saving system resources.

[0170] Optionally, the reporting triggering event includes at least one of the following:

[0171] A first event occurs K1 times consecutively, or a first event occurs K1 times within a fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to a threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer;

[0172] A second event occurs K2 times consecutively, or K2 times within a fifth time period, where the second event is that the prediction of the best N cells is incorrect, or the prediction accuracy of the best N cells is less than or equal to a threshold corresponding to the best N cells, and K2 is a positive integer.

[0173] A third event occurs K3 times consecutively, or occurs K3 times within a sixth time period, where the third event is a prediction error of the optimal M beams, or the prediction accuracy of the optimal M beams is less than or equal to a threshold corresponding to the optimal M beams, and K3 is a positive integer.

[0174] The fourth event occurs K4 times consecutively, or the fourth event occurs K4 times within the seventh time period, the fourth event is a measurement event prediction error, or the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer;

[0175] Abnormal events occur K5 times consecutively, or K5 abnormal events occur within the eighth time period, where K5 is a positive integer;

[0176] A fifth event occurs K6 times consecutively, or a fifth event occurs K6 times within a ninth time period, where the fifth event is a switching delay or an interruption duration that is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, and K6 is a positive integer;

[0177] Switching occurs K7 times continuously, or K7 times in the ninth time period, where K7 is a positive integer.

[0178] The above K1 to K7 may be predefined by the protocol or configured by the network side device or determined by the terminal.

[0179] The fifth time period to the ninth time period may be predefined by a protocol or configured by a network-side device or determined by a terminal.

[0180] Optionally, the reporting parameter is associated with a first object, wherein the first object includes one of the following: terminal, cell, frequency, AI object, measurement configuration, measurement identifier, measurement object, and reporting configuration.

[0181] Exemplarily, when the above-mentioned reporting parameters are associated with a terminal, the above-mentioned reporting parameters can be used to report the monitoring results of each AI object of the terminal. For example, the terminal can periodically report the RRM measurement prediction accuracy of the AI ​​object currently activated by the terminal for RRM measurement prediction based on the above-mentioned reporting period, or the terminal can trigger the reporting of the RRM measurement prediction accuracy of the AI ​​object currently activated by the terminal for RRM measurement prediction based on the above-mentioned reporting trigger event.

[0182] Exemplarily, in the case where the above-mentioned reporting parameters are associated with a cell, for example, a special cell (SpCell), the above-mentioned reporting parameters can be used to report the monitoring results corresponding to the cell. For example, the terminal can periodically report the RRM measurement prediction accuracy of the SpCell based on the above-mentioned reporting period, or the terminal can trigger the reporting of the RRM measurement prediction accuracy of the SpCell based on the above-mentioned reporting trigger event.

[0183] Exemplarily, when the above-mentioned reporting parameters are associated with a frequency point, the above-mentioned reporting parameters can be used to report the monitoring results corresponding to the cell on the frequency point. For example, the terminal can periodically report the RRM measurement prediction accuracy of the cell on the frequency point based on the above-mentioned reporting period, or the terminal can trigger the reporting of the RRM measurement prediction accuracy of the cell on the frequency point based on the above-mentioned reporting trigger event.

[0184] Exemplarily, when the above-mentioned reporting parameters are associated with an AI object, the above-mentioned reporting parameters can be used to report the monitoring results of the AI ​​object. For example, the terminal can periodically report the RRM measurement prediction accuracy of the AI ​​object based on the above-mentioned reporting period, or the terminal can trigger the reporting of the RRM measurement prediction accuracy of the AI ​​object based on the above-mentioned reporting trigger event.

[0185] Exemplarily, when the above-mentioned reporting parameters are associated with measurement configuration / measurement ID / measurement object / reporting configuration, the above-mentioned reporting parameters can be used to report the monitoring results of the AI ​​object associated with the measurement configuration / measurement ID / measurement object / reporting configuration. For example, the terminal can periodically report the RRM measurement prediction accuracy of the AI ​​object associated with the measurement configuration / measurement ID / measurement object / reporting configuration based on the above-mentioned reporting period, or the terminal can trigger the reporting of the RRM measurement prediction accuracy of the AI ​​object associated with the measurement configuration / measurement ID / measurement object / reporting configuration based on the above-mentioned reporting trigger event.

[0186] Optionally, the method further includes:

[0187] The terminal sends a second measurement report to the network side device;

[0188] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0189] The predicted moment or predicted time period corresponding to the above-mentioned first prediction result, for example, the terminal predicts the cell signal quality within at least one future moment or at least one time period based on the AI ​​object, then the above-mentioned at least one moment or at least one time period is the predicted moment or predicted time period corresponding to the above-mentioned predicted cell signal quality.

[0190] The above-mentioned actual results may include but are not limited to RRM measurement results, whether the measurement event actually occurs, whether RLF / HOF actually occurs, etc.

[0191] In this embodiment, the terminal sends the actual result of the prediction moment or prediction time period corresponding to the first prediction result to the network side device, so that the network side device can more conveniently monitor the AI ​​object based on the first prediction result and its corresponding actual result of the prediction moment or prediction time period.

[0192] Optionally, the method further includes:

[0193] The terminal receives second information sent by the network side device;

[0194] The second information is used to determine the association between the reporting time of the first measurement report and the reporting time of the second measurement report.

[0195] Exemplarily, the second information may include a time interval between a reporting time of the first measurement report and a reporting time of the second measurement report.

[0196] In this embodiment, the second information is used to determine the correlation between the reporting time of the first measurement report and the reporting time of the second measurement report, and the terminal can control the reporting time of the first measurement report and the reporting time of the second measurement report based on the second information.

[0197] Optionally, the second information includes configuration information of the first timer; and the terminal sending the second measurement report to the network side device includes:

[0198] When the terminal sends the first measurement report to the network-side device, starting the first timer;

[0199] When the first timer times out, the terminal sends a second measurement report to the network side device.

[0200] The configuration information of the first timer may include the duration of the first timer.

[0201] Taking RRM measurement prediction as an example, assume that the duration of the first timer is 1s, and the first measurement report carries the RRM measurement prediction result of cell 1 after 1s. After reporting the first measurement report, the first timer is started. After the first timer times out, the terminal reports the second measurement report, which includes the RRM measurement result of cell 1 at the current moment, that is, the actual result. That is, by setting the first timer, it can be more accurately ensured that the second measurement report carries the actual result of the predicted moment corresponding to the RRM measurement prediction result.

[0202] Taking event prediction as an example, assume that the duration of the first timer is 1s, and the first measurement report carries the prediction result that event A3 will be satisfied after 1s. After reporting the first measurement report, the first timer is started. After the first timer times out, the terminal reports the second measurement report, which includes the actual result of whether event A3 is satisfied at the current moment. That is, by setting the first timer, it can be more accurately ensured that the second measurement report carries the actual result of the predicted moment corresponding to the prediction result that event A3 will be satisfied.

[0203] In this embodiment, the terminal starts the first timer configured according to the configuration information of the above-mentioned first timer when sending the first measurement report to the network side device, and sends a second measurement report when the first timer times out. This is conducive to ensuring that the second measurement report carries the actual result of the predicted time or predicted time period corresponding to the predicted result carried by the first measurement report.

[0204] Optionally, the first measurement report includes first prediction results within T predicted moments or T predicted time periods, and the first timer includes T timers, the T timers respectively corresponding to the T predicted moments or T predicted time periods, and the T timers have different durations, and T is an integer greater than 1;

[0205] The terminal starting the first timer when sending the first measurement report to the network side device includes:

[0206] When the terminal sends the first measurement report to the network side device, starting the T timers;

[0207] When the first timer times out, the terminal sends a second measurement report to the network side device, including:

[0208] When each of the T timers times out, the terminal sends a second measurement report corresponding to each timer to the network side device, wherein the second measurement report corresponding to each timer includes the actual result within the predicted time or predicted time period corresponding to each timer.

[0209] In this embodiment, if the terminal carries prediction results of at least two predicted moments or predicted time periods in the first measurement report, the terminal can simultaneously start at least two timers of different lengths after triggering the reporting of the first measurement report. After each timer times out, it triggers the reporting of a measurement report carrying the actual results within the corresponding predicted moment or predicted time period.

[0210] Optionally, the first measurement report includes a first identifier, and the second measurement report includes the first identifier.

[0211] The first identifier may be any identifier, for example, any number, letter, or combination of numbers and letters.

[0212] In this embodiment, the first measurement report and the second measurement report carry the same identifier, that is, the first identifier, so that the network side device can identify the second measurement report corresponding to the first measurement report based on the first identifier.

[0213] Optionally, the method further includes:

[0214] When the first condition is met, the terminal does not report the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first prediction result, and the second measurement report includes the actual result within the predicted time or predicted time period corresponding to the first prediction result;

[0215] The first condition includes any one of the following:

[0216] A switch, rebuild, or redirect occurs;

[0217] The first prediction result is the same as the actual result at the prediction moment or prediction time period corresponding to the first prediction result, or the difference between the first prediction result and the actual result at the prediction moment or prediction time period corresponding to the first prediction result is less than or equal to a first threshold;

[0218] The measurement object associated with the first measurement report is deleted;

[0219] RLF, HOF or Radio Resource Control (RRC) state transition occurs.

[0220] Exemplarily, after the terminal reports the first measurement report, if the first condition is met, the terminal does not report the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first prediction result; if the first condition is not met, the terminal may report the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first prediction result.

[0221] In some optional embodiments, when the first condition includes switching, reconstruction or redirection, the terminal does not report the second measurement report or the actual result within the predicted moment or predicted time period corresponding to the first prediction result to the source cell.

[0222] In this embodiment, when the first condition is met, the terminal does not report the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first prediction result, which can save resources.

[0223] Optionally, the method further includes:

[0224] In the event of handover, reestablishment, or redirection, the terminal performs a second operation;

[0225] The second operation includes any one of the following:

[0226] When the AI ​​object associated with the first measurement report is in an activated state, reporting a second measurement report to the target cell;

[0227] If the AI ​​object associated with the first measurement report is in an inactive state or has been released, the second measurement report is not reported to the target cell;

[0228] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0229] The AI ​​object associated with the above-mentioned first measurement report can be understood as the prediction result carried by the first measurement report including the prediction result based on the AI ​​object.

[0230] In this embodiment, in the event of handover, reconstruction or redirection, if the AI ​​object associated with the first measurement report is in an activated state, the terminal can report a second measurement report to the target cell when the handover is completed, so that the target cell can monitor the AI ​​object; if the AI ​​object associated with the first measurement report is in an inactivated state or has been released, the terminal does not need to monitor the AI ​​object associated with the first measurement report. In this case, the terminal does not report the second measurement report to the target cell to save resources.

[0231] Optionally, the event prediction result includes at least one of a prediction result of a measurement event, a prediction result of an RLF, and a prediction result of a HOF.

[0232] Optionally, when the first prediction result includes a prediction result of the HOF, the method further includes:

[0233] The terminal starts a second timer after sending the first measurement report to the network side device;

[0234] When the second timer times out, the terminal reports an actual result of the first predicted cell during the running period of the second timer;

[0235] or,

[0236] In the case where a HOF or an RRC state transition occurs during the running of the second timer, the terminal does not report an actual result of the first predicted cell during the running of the second timer.

[0237] In this embodiment, the first predicted cell may be understood as the cell targeted by the HOF prediction result, that is, the HOF prediction result is a prediction result obtained by performing HOF prediction on the first predicted cell.

[0238] The duration of the second timer may be predefined by a protocol, configured by a network-side device, or determined by the terminal.

[0239] In some optional embodiments, when HOF or RRC state transition occurs during the operation interval of the second timer, the terminal may stop the operation of the second timer and does not report the actual result of the first predicted cell during the operation of the second timer.

[0240] Optionally, when the first prediction result includes the RLF prediction result, the method further includes:

[0241] The terminal starts a third timer after sending the first measurement report to the network side device;

[0242] When the third timer times out, the terminal reports an actual result of the second predicted cell during the running period of the third timer;

[0243] or,

[0244] In a case where an RLF or an RRC state transition occurs during the running of the third timer, the terminal does not report an actual result of the second predicted cell during the running of the third timer.

[0245] In this embodiment, the second predicted cell may be understood as the cell targeted by the RLF prediction result, that is, the RLF prediction result is a prediction result obtained by performing RLF prediction on the second predicted cell.

[0246] The duration of the third timer may be predefined by a protocol, configured by a network-side device, or determined by the terminal.

[0247] In some optional embodiments, when RLF or RRC state transition occurs during the operation interval of the third timer, the terminal may stop the operation of the third timer and does not report the actual result of the second predicted cell during the operation of the third timer.

[0248] The following examples illustrate AI object monitoring for RLF or HOF prediction on network-side devices:

[0249] 1. For HOF prediction, the terminal reports the measurement results of the target cell to the network device so that the network device can monitor the AI ​​object used for HOF prediction. The specific steps include:

[0250] At the ninth moment, the terminal predicts that a HOF will occur in the fourth cell (eg, the target cell), and reports the HOF prediction result to the network-side device.

[0251] The terminal starts the first timer, and after the second timer times out, the terminal reports the measurement result or average measurement result of the fourth cell during the running period of the second timer.

[0252] Optionally, the terminal reports the prediction result of the HOF at the tenth moment, and reports the measurement result of the fourth cell in the first time period (i.e., during the operation of the second timer) at the eleventh moment;

[0253] Among them, if HOF occurs in the terminal before reporting the measurement result of the fourth cell within the first time period, the measurement result of the fourth cell within the first time period is not reported; if an RRC state transition occurs before reporting the measurement result of the fourth cell within the first time period, the measurement result of the fourth cell within the first time period is not reported.

[0254] 2. For RLF prediction, the terminal reports the measurement results of the source cell to the network so that the network can monitor the AI ​​object used for RLF prediction. The specific steps include:

[0255] At the twelfth moment, the terminal predicts that an RLF will occur in the fifth cell (for example, the source cell), and the terminal reports the RLF prediction result to the network-side device;

[0256] The terminal starts a third timer. After the third timer times out, the terminal reports the measurement result or average measurement result of the fifth cell within the third timer.

[0257] Optionally, the terminal reports the prediction result of the HOF at the thirteenth moment, and reports the measurement result of the fifth cell in the second time period (that is, during the operation of the third timer) at the fourteenth moment;

[0258] Among them, if RLF occurs in the terminal before reporting the measurement result of the fifth cell within the second time period, the measurement result of the fifth cell within the second time period will not be reported; if an RRC state transition occurs before reporting the measurement result of the fifth cell within the second time period, the measurement result of the fifth cell within the second time period will not be reported.

[0259] Please refer to FIG5 , which is a flowchart of an AI object monitoring method provided in an embodiment of the present application. The method can be executed by a network-side device, as shown in FIG5 , including the following steps:

[0260] Step 501: A network-side device sends a first parameter to a terminal, or the network-side device receives a first measurement report sent by the terminal;

[0261] The first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and reporting of monitoring results of the AI ​​object, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object;

[0262] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0263] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0264] Optionally, the monitoring indicator parameters of the AI ​​object include at least one of the following:

[0265] RRM measurement prediction accuracy;

[0266] The prediction accuracy of the best N cells, where N is a positive integer;

[0267] The prediction accuracy of the optimal M beams, where M is a positive integer;

[0268] The prediction accuracy of HOF switching failure;

[0269] The prediction accuracy of radio link failure (RLF);

[0270] Measuring the accuracy of event predictions;

[0271] The number or probability of abnormal events occurring in the first time period;

[0272] the switching delay or interruption duration in the second time period;

[0273] The number of switching times per unit time or within the third time period.

[0274] Optionally, the abnormal event includes at least one of the following: ping-pong handover, premature handover, late handover, handover to an incorrect cell, handover failure, and radio link failure.

[0275] Optionally, the reporting parameters of the monitoring results include at least one of the following: a reporting period, a reporting trigger event, and a reporting threshold.

[0276] Optionally, the reporting threshold includes a threshold corresponding to each monitoring indicator parameter of the AI ​​object.

[0277] Optionally, the reporting triggering event includes at least one of the following:

[0278] A first event occurs K1 times consecutively, or a first event occurs K1 times within a fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to a threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer;

[0279] A second event occurs K2 times consecutively, or K2 times within a fifth time period, where the second event is that the prediction of the best N cells is incorrect, or the prediction accuracy of the best N cells is less than or equal to a threshold corresponding to the best N cells, and K2 is a positive integer.

[0280] A third event occurs K3 times consecutively, or occurs K3 times within a sixth time period, where the third event is a prediction error of the optimal M beams, or the prediction accuracy of the optimal M beams is less than or equal to a threshold corresponding to the optimal M beams, and K3 is a positive integer.

[0281] The fourth event occurs K4 times consecutively, or the fourth event occurs K4 times within the seventh time period, the fourth event is a measurement event prediction error, or the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer;

[0282] Abnormal events occur K5 times consecutively, or K5 abnormal events occur within the eighth time period, where K5 is a positive integer;

[0283] A fifth event occurs K6 times consecutively, or a fifth event occurs K6 times within a ninth time period, where the fifth event is a switching delay or an interruption duration that is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, and K6 is a positive integer;

[0284] Switching occurs K7 times continuously, or K7 times in the ninth time period, where K7 is a positive integer.

[0285] Optionally, the reporting parameter is associated with a first object, wherein the first object includes one of the following: terminal, cell, frequency, AI object, measurement configuration, measurement identifier, measurement object, and reporting configuration.

[0286] Optionally, the method further includes:

[0287] The network side device receives a second measurement report sent by the terminal;

[0288] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0289] Optionally, the method further includes:

[0290] The network side device sends second information to the terminal;

[0291] The second information is used to determine the association between the reporting time of the first measurement report and the reporting time of the second measurement report.

[0292] Optionally, the second information includes configuration information of a first timer, where the first timer is used to control a reporting time of the second measurement report.

[0293] Optionally, the first measurement report includes the first prediction results within T predicted moments or T predicted time periods, and the first timer includes T timers, the T timers respectively corresponding to the T predicted moments or T predicted time periods, and the durations of the T timers are different, and T is an integer greater than 1.

[0294] Optionally, the first measurement report includes a first identifier, and the second measurement report includes the first identifier.

[0295] Optionally, the event prediction result includes at least one of a prediction result of a measurement event, a prediction result of an RLF, and a prediction result of a HOF.

[0296] It should be noted that the implementation of this embodiment can refer to the relevant description of the embodiment shown in Figure 4, and will not be repeated here.

[0297] It should also be noted that the conditional handover in the embodiment of the present application may include conditional primary / secondary cell addition / modification (Conditional PSCell addition / change) or conditional LTM (ie, Condition L1 / L2-triggered mobility).

[0298] It should also be noted that the AI ​​object monitoring method provided in the embodiments of this application can be executed by an AI object monitoring device, or by a control module within the AI ​​object monitoring device that is configured to execute the AI ​​object monitoring method. In the embodiments of this application, the AI ​​object monitoring device provided in the embodiments of this application is described using the AI ​​object monitoring device executing the AI ​​object monitoring method as an example.

[0299] Please refer to FIG6 , which is a structural diagram of an AI object monitoring device provided in an embodiment of the present application. As shown in FIG6 , the AI ​​object monitoring device 600 includes:

[0300] A first execution module 601 is configured to execute a first operation according to a first parameter, or send a first measurement report to a network-side device;

[0301] The first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the AI ​​object monitoring result;

[0302] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0303] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0304] Optionally, the monitoring indicator parameters of the AI ​​object include at least one of the following:

[0305] RRM measurement prediction accuracy;

[0306] The prediction accuracy of the best N cells, where N is a positive integer;

[0307] The prediction accuracy of the optimal M beams, where M is a positive integer;

[0308] The prediction accuracy of HOF switching failure;

[0309] The prediction accuracy of radio link failure (RLF);

[0310] Measuring the accuracy of event predictions;

[0311] The number or probability of abnormal events occurring in the first time period;

[0312] the switching delay or interruption duration in the second time period;

[0313] The number of switching times per unit time or within the third time period.

[0314] Optionally, the abnormal event includes at least one of the following: ping-pong handover, premature handover, late handover, handover to an incorrect cell, handover failure, and radio link failure.

[0315] Optionally, the reporting parameters of the monitoring results include at least one of the following: a reporting period, a reporting trigger event, and a reporting threshold.

[0316] Optionally, the reporting parameter of the monitoring result includes the reporting threshold, and the reporting threshold includes a threshold corresponding to each monitoring indicator parameter of the AI ​​object; and the terminal performs a first operation according to the first parameter, including at least one of the following:

[0317] When the RRM measurement prediction accuracy is less than or equal to the threshold corresponding to the RRM measurement prediction accuracy, the terminal reports the monitoring result of the AI ​​object;

[0318] When the prediction accuracy of the optimal N cells is less than or equal to the threshold corresponding to the optimal N cells, the terminal reports the monitoring result of the AI ​​object;

[0319] When the prediction accuracy of the optimal M beams is less than or equal to the threshold corresponding to the optimal M beams, the terminal reports the monitoring result of the AI ​​object;

[0320] When the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, the terminal reports the monitoring result of the AI ​​object;

[0321] When the number of occurrences or probability of an abnormal event within a first time period is greater than or equal to a threshold corresponding to the abnormal event, the terminal reports the monitoring result of the AI ​​object;

[0322] When the switching delay or the interruption duration in the second time period is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, the terminal reports the monitoring result of the AI ​​object;

[0323] When the number of switching times within the unit time or the third time period is greater than or equal to the threshold corresponding to the number of switching times, the terminal reports the monitoring result of the AI ​​object.

[0324] Optionally, the reporting triggering event includes at least one of the following:

[0325] A first event occurs K1 times consecutively, or a first event occurs K1 times within a fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to a threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer;

[0326] A second event occurs K2 times consecutively, or K2 times within a fifth time period, where the second event is that the prediction of the best N cells is incorrect, or the prediction accuracy of the best N cells is less than or equal to a threshold corresponding to the best N cells, and K2 is a positive integer.

[0327] A third event occurs K3 times consecutively, or occurs K3 times within a sixth time period, where the third event is a prediction error of the optimal M beams, or the prediction accuracy of the optimal M beams is less than or equal to a threshold corresponding to the optimal M beams, and K3 is a positive integer.

[0328] The fourth event occurs K4 times consecutively, or the fourth event occurs K4 times within the seventh time period, the fourth event is a measurement event prediction error, or the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer;

[0329] Abnormal events occur K5 times consecutively, or K5 abnormal events occur within the eighth time period, where K5 is a positive integer;

[0330] A fifth event occurs K6 times consecutively, or a fifth event occurs K6 times within a ninth time period, where the fifth event is a switching delay or an interruption duration that is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, and K6 is a positive integer;

[0331] Switching occurs K7 times continuously, or K7 times in the ninth time period, where K7 is a positive integer.

[0332] Optionally, the reporting parameter is associated with a first object, wherein the first object includes one of the following: terminal, cell, frequency, AI object, measurement configuration, measurement identifier, measurement object, and reporting configuration.

[0333] Optionally, the device further comprises:

[0334] A first sending module, configured to send a second measurement report to the network side device;

[0335] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0336] Optionally, the device further comprises:

[0337] A first receiving module, configured to receive second information sent by a network-side device;

[0338] The second information is used to determine the association between the reporting time of the first measurement report and the reporting time of the second measurement report.

[0339] Optionally, the second information includes configuration information of a first timer; and the first sending module includes:

[0340] a starting unit, configured to start the first timer when sending the first measurement report to the network side device;

[0341] A sending unit is configured to send a second measurement report to the network side device when the first timer times out.

[0342] Optionally, the first measurement report includes first prediction results within T predicted moments or T predicted time periods, and the first timer includes T timers, the T timers respectively corresponding to the T predicted moments or T predicted time periods, and the T timers have different durations, and T is an integer greater than 1;

[0343] The startup unit is specifically used for:

[0344] When sending the first measurement report to the network side device, start the T timers;

[0345] The sending unit is specifically configured to:

[0346] When each of the T timers times out, a second measurement report corresponding to each timer is sent to the network side device respectively, wherein the second measurement report corresponding to each timer includes the actual result within the predicted moment or predicted time period corresponding to each timer.

[0347] Optionally, the first measurement report includes a first identifier, and the second measurement report includes the first identifier.

[0348] Optionally, if the first condition is met, the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first prediction result is not reported, and the second measurement report includes the actual result within the predicted time or predicted time period corresponding to the first prediction result;

[0349] The first condition includes any one of the following:

[0350] A switch, rebuild, or redirect occurs;

[0351] The first prediction result is the same as the actual result at the prediction moment or prediction time period corresponding to the first prediction result, or the difference between the first prediction result and the actual result at the prediction moment or prediction time period corresponding to the first prediction result is less than or equal to a first threshold;

[0352] The measurement object associated with the first measurement report is deleted;

[0353] RLF, HOF or Radio Resource Control (RRC) state transition occurs.

[0354] Optionally, the device further comprises:

[0355] A second execution module, configured to execute a second operation in case of switching, reconstruction or redirection;

[0356] The second operation includes any one of the following:

[0357] When the AI ​​object associated with the first measurement report is in an activated state, reporting a second measurement report to the target cell;

[0358] If the AI ​​object associated with the first measurement report is in an inactive state or has been released, the second measurement report is not reported to the target cell;

[0359] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0360] Optionally, the event prediction result includes at least one of a prediction result of a measurement event, a prediction result of an RLF, and a prediction result of a HOF.

[0361] Optionally, when the first prediction result includes a prediction result of the HOF, the apparatus further includes:

[0362] A first starting module, configured to start a second timer after sending a first measurement report to the network side device;

[0363] The first reporting module is used to report the actual result of the first predicted cell during the operation period of the second timer when the second timer times out; or, if a HOF or RRC state transition occurs during the operation of the second timer, not report the actual result of the first predicted cell during the operation period of the second timer.

[0364] Optionally, when the first prediction result includes the RLF prediction result, the apparatus further includes:

[0365] A second starting module, configured to start a third timer after sending the first measurement report to the network side device;

[0366] The second reporting module is used to report the actual result of the second predicted cell during the operation period of the third timer when the third timer times out; or, when an RLF or RRC state transition occurs during the operation of the third timer, not report the actual result of the second predicted cell during the operation period of the third timer.

[0367] The AI ​​object monitoring 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 it can be other devices 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.

[0368] The AI ​​object monitoring device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0369] Please refer to FIG. 7 , which is a structural diagram of an AI object monitoring device provided in an embodiment of the present application. As shown in FIG. 7 , the AI ​​object monitoring device 700 includes:

[0370] The transmission module 701 is configured to send a first parameter to a terminal, or receive a first measurement report sent by the terminal;

[0371] The first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and reporting of monitoring results of the AI ​​object, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object and a reporting parameter of the monitoring result of the AI ​​object;

[0372] The first measurement report is used by the network side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result;

[0373] The AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0374] Optionally, the monitoring indicator parameters of the AI ​​object include at least one of the following:

[0375] RRM measurement prediction accuracy;

[0376] The prediction accuracy of the best N cells, where N is a positive integer;

[0377] The prediction accuracy of the optimal M beams, where M is a positive integer;

[0378] The prediction accuracy of HOF switching failure;

[0379] The prediction accuracy of radio link failure (RLF);

[0380] Measuring the accuracy of event predictions;

[0381] The number or probability of abnormal events occurring in the first time period;

[0382] the switching delay or interruption duration in the second time period;

[0383] The number of switching times per unit time or within the third time period.

[0384] Optionally, the abnormal event includes at least one of the following: ping-pong handover, premature handover, late handover, handover to an incorrect cell, handover failure, and radio link failure.

[0385] Optionally, the reporting parameters of the monitoring results include at least one of the following: a reporting period, a reporting trigger event, and a reporting threshold.

[0386] Optionally, the reporting threshold includes a threshold corresponding to each monitoring indicator parameter of the AI ​​object.

[0387] Optionally, the reporting triggering event includes at least one of the following:

[0388] A first event occurs K1 times consecutively, or a first event occurs K1 times within a fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to a threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer;

[0389] A second event occurs K2 times consecutively, or K2 times within a fifth time period, where the second event is that the prediction of the best N cells is incorrect, or the prediction accuracy of the best N cells is less than or equal to a threshold corresponding to the best N cells, and K2 is a positive integer.

[0390] A third event occurs K3 times consecutively, or occurs K3 times within a sixth time period, where the third event is a prediction error of the optimal M beams, or the prediction accuracy of the optimal M beams is less than or equal to a threshold corresponding to the optimal M beams, and K3 is a positive integer.

[0391] The fourth event occurs K4 times consecutively, or the fourth event occurs K4 times within the seventh time period, the fourth event is a measurement event prediction error, or the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer;

[0392] Abnormal events occur K5 times consecutively, or K5 abnormal events occur within the eighth time period, where K5 is a positive integer;

[0393] A fifth event occurs K6 times consecutively, or a fifth event occurs K6 times within a ninth time period, where the fifth event is a switching delay or an interruption duration that is greater than or equal to a threshold corresponding to the switching delay or the interruption duration, and K6 is a positive integer;

[0394] Switching occurs K7 times continuously, or K7 times in the ninth time period, where K7 is a positive integer.

[0395] Optionally, the reporting parameter is associated with a first object, wherein the first object includes one of the following: terminal, cell, frequency, AI object, measurement configuration, measurement identifier, measurement object, and reporting configuration.

[0396] Optionally, the method further includes:

[0397] A receiving module, configured to receive a second measurement report sent by the terminal;

[0398] The second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

[0399] Optionally, the method further includes:

[0400] A sending module, configured to send second information to the terminal;

[0401] The second information is used to determine the association between the reporting time of the first measurement report and the reporting time of the second measurement report.

[0402] Optionally, the second information includes configuration information of a first timer, where the first timer is used to control a reporting time of the second measurement report.

[0403] Optionally, the first measurement report includes the first prediction results within T predicted moments or T predicted time periods, and the first timer includes T timers, the T timers respectively corresponding to the T predicted moments or T predicted time periods, and the durations of the T timers are different, and T is an integer greater than 1.

[0404] Optionally, the first measurement report includes a first identifier, and the second measurement report includes the first identifier.

[0405] Optionally, the event prediction result includes at least one of a prediction result of a measurement event, a prediction result of an RLF, and a prediction result of a HOF.

[0406] The AI ​​object monitoring 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, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0407] The AI ​​object monitoring device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0408] Optionally, as shown in FIG8 , an embodiment of the present application further provides a communication device 800, comprising a processor 801 and a memory 802, wherein the memory 802 stores a program or instruction that can be run on the processor 801. For example, when the communication device 800 is a terminal, the program or instruction, when executed by the processor 801, implements the various steps of the above-mentioned terminal-side AI object monitoring method embodiment and can achieve the same technical effect. When the communication device 800 is a network-side device, the program or instruction, when executed by the processor 801, implements the various steps of the above-mentioned network-side device-side AI object monitoring method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described here.

[0409] An embodiment of the present application also provides a terminal, including a processor and a communication interface, wherein the processor is used to perform a first operation according to a first parameter, or the communication interface is used to send a first measurement report to a network side device; wherein the first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: a monitoring indicator parameter of the AI ​​object, a reporting parameter of the monitoring result of the AI ​​object; the first measurement report is used by the network side device to monitor the AI ​​object, and the first measurement report includes a first prediction result based on the AI ​​object, and the first prediction result includes at least one of a radio resource management RRM measurement prediction result and an event prediction result; the AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management. This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and the various implementation processes and implementation methods of the above-mentioned method embodiment are applicable to this terminal embodiment and can achieve the same technical effect. Specifically, Figure 9 is a schematic diagram of the hardware structure of a terminal that implements an embodiment of the present application.

[0410] The terminal 900 includes but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909 and at least some of the components of the processor 910.

[0411] Those skilled in the art will appreciate that the terminal 900 may further include a power source (such as a battery) for powering various components. The power source may be logically connected to the processor 910 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG9 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.

[0412] It should be understood that in an embodiment of the present application, the input unit 904 may include a graphics processing unit (GPU) 9041 and a microphone 9042, and the graphics processor 9041 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 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 907 includes a touch panel 9071 and at least one of other input devices 9072. The touch panel 9071 is also called a touch screen. The touch panel 9071 may include two parts: a touch detection device and a touch controller. Other input devices 9072 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.

[0413] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 901 may transmit the data to the processor 910 for processing. Furthermore, the RF unit 901 may send uplink data to the network-side device. Typically, the RF unit 901 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0414] The memory 909 can be used to store software programs or instructions and various data. The memory 909 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 909 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 909 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0415] Processor 910 may include one or more processing units. Optionally, processor 910 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 910.

[0416] Among them, the processor 910 is used to perform a first operation according to the first parameter, or the radio frequency unit 901 is used to send a first measurement report to the network side device; wherein, the first operation includes at least one of AI object monitoring and AI object monitoring result reporting, and the first parameter includes at least one of the following: the monitoring indicator parameter of the AI ​​object, the reporting parameter of the monitoring result of the AI ​​object; the first measurement report is used by the network side device to monitor the AI ​​object, and the first measurement report includes a first prediction result based on the AI ​​object, and the first prediction result includes at least one of a wireless resource management RRM measurement prediction result and an event prediction result; the AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management.

[0417] 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.

[0418] An embodiment of the present application also provides a network-side device, including a processor and a communication interface, the communication interface being used to send a first parameter to a terminal, or to receive a first measurement report sent by the terminal; wherein the first parameter is used by the terminal to perform a first operation, the first operation including at least one of AI object monitoring and AI object monitoring result reporting, the first parameter including at least one of the following: a monitoring indicator parameter of the AI ​​object, a reporting parameter of the monitoring result of the AI ​​object; the first measurement report is used by the network-side device to monitor the AI ​​object, the first measurement report including a first prediction result based on the AI ​​object, the first prediction result including at least one of a radio resource management RRM measurement prediction result and an event prediction result; the AI ​​object includes an AI model or an AI function, and the AI ​​object is an AI object for mobility management. This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this network-side device embodiment and can achieve the same technical effect.

[0419] Specifically, an embodiment of the present application also provides a network-side device. As shown in Figure 10, the network-side device 1000 includes: an antenna 1001, a radio frequency device 1002, a baseband device 1003, a processor 1004, and a memory 1005. Antenna 1001 is connected to radio frequency device 1002. In the uplink direction, radio frequency device 1002 receives information via antenna 1001 and sends the received information to baseband device 1003 for processing. In the downlink direction, baseband device 1003 processes the information to be transmitted and sends it to radio frequency device 1002. Radio frequency device 1002 processes the received information and sends it through antenna 1001.

[0420] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 1003 , which includes a baseband processor.

[0421] The baseband device 1003 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 10, one of which is, for example, a baseband processor, which is connected to the memory 1005 through a bus interface to call the program in the memory 1005 and execute the network side device operations shown in the above method embodiment.

[0422] The network side device may further include a network interface 1006, which is, for example, a Common Public Radio Interface (CPRI).

[0423] Specifically, the network side device 1000 of the embodiment of the present application also includes: instructions or programs stored in the memory 1005 and executable on the processor 1004. The processor 1004 calls the instructions or programs in the memory 1005 to execute the method of execution of each module shown in Figure 7 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0424] 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 monitoring method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0425] 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.

[0426] 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 monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0427] 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.

[0428] 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 monitoring method embodiment and can achieve the same technical effect. To avoid repetition, they are not repeated here.

[0429] An embodiment of the present application also provides an AI object monitoring system, including: a terminal and a network-side device, wherein the terminal is used to execute the various processes shown in Figure 4 and the various method embodiments described above, and the network-side device is used to execute the various processes shown in Figure 5 and the various method embodiments described above, and can achieve the same technical effects. To avoid repetition, they will not be repeated here.

[0430] 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.

[0431] 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.

[0432] 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 monitoring method, comprising: The terminal performs a first operation according to a first parameter, or the terminal sends a first measurement report to a network-side device; Wherein, the first operation includes at least one of AI object monitoring and reporting of the monitoring result of the AI object, and the first parameter includes at least one of the following: the monitoring metric parameter of the AI object, the reporting parameter of the monitoring result of the AI object; The first measurement report is used to monitor the AI object, and the first measurement report includes a first prediction result based on the AI object, and the first prediction result includes at least one of a radio resource management (RRM) measurement prediction result and an event prediction result; The AI object includes an AI model or an AI function, and the AI object is an AI object for mobility management.

2. The method according to claim 1, wherein The monitoring metric parameter of the AI object includes at least one of the following: RRM measurement prediction accuracy; Prediction accuracy of the optimal N cells, where N is a positive integer; Prediction accuracy of the optimal M beams, where M is a positive integer; Prediction accuracy of handover failure (HOF); Prediction accuracy of radio link failure (RLF); Prediction accuracy of measurement events; Number or probability of abnormal events occurring within a first time period; Handover delay or interruption duration within a second time period; Number of handovers per unit time or within a third time period.

3. The method according to claim 2, wherein The abnormal events include at least one of the following: ping-pong handover, premature handover, late handover, handover to the wrong cell, handover failure, radio link failure.

4. The method according to any one of claims 1 to 3, wherein The reporting parameter of the monitoring result includes at least one of the following: reporting period, reporting trigger event, reporting threshold.

5. The method according to claim 4, wherein The reporting parameter of the monitoring result includes the reporting threshold, and the reporting threshold includes the thresholds corresponding to the respective monitoring metric parameters of the AI object; the terminal performing the first operation according to the first parameter includes at least one of the following: When the RRM measurement prediction accuracy is less than or equal to the threshold corresponding to the RRM measurement prediction accuracy, the terminal reports the monitoring result of the AI object; When the prediction accuracy of the optimal N cells is less than or equal to the threshold corresponding to the optimal N cells, the terminal reports the monitoring result of the AI object; When the prediction accuracy of the optimal M beams is less than or equal to the threshold corresponding to the optimal M beams, the terminal reports the monitoring result of the AI object; When the prediction accuracy of measurement events is less than or equal to the threshold corresponding to the measurement events, the terminal reports the monitoring result of the AI object; When the number or probability of abnormal events occurring within the first time period is greater than or equal to the threshold corresponding to the abnormal events, the terminal reports the monitoring result of the AI object; When the handover delay or interruption duration within the second time period is greater than or equal to the threshold corresponding to the handover delay or interruption duration, the terminal reports the monitoring result of the AI object; When the number of handovers per unit time or within the third time period is greater than or equal to the threshold corresponding to the number of handovers, the terminal reports the monitoring result of the AI object.

6. The method according to claim 4, wherein The reporting trigger event includes at least one of the following: The first event occurs continuously for K1 times, or the first event occurs K1 times within the fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to the threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer; The second event occurs continuously for K2 times, or the second event occurs K2 times within the fifth time period, where the second event is that the prediction of the optimal N cells is incorrect, or the prediction accuracy of the optimal N cells is less than or equal to the threshold corresponding to the optimal N cells, and K2 is a positive integer; The third event occurs continuously for K3 times, or the third event occurs K3 times within the sixth time period, where the third event is that the prediction of the optimal M beams is incorrect, or the prediction accuracy of the optimal M beams is less than or equal to the threshold corresponding to the optimal M beams, and K3 is a positive integer; The fourth event occurs continuously for K4 times, or the fourth event occurs K4 times within the seventh time period, where the fourth event is that the prediction of the measurement event is incorrect, or the prediction accuracy of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer; The abnormal event occurs continuously for K5 times, or the abnormal event occurs K5 times within the eighth time period, and K5 is a positive integer; The fifth event occurs continuously for K6 times, or the fifth event occurs K6 times within the ninth time period, where the fifth event is that the handover delay or interruption duration is greater than or equal to the threshold corresponding to the handover delay or interruption duration, and K6 is a positive integer; The handover occurs continuously for K7 times, or the handover occurs K7 times within the ninth time period, and K7 is a positive integer.

7. The method according to any one of claims 1 to 6, wherein The reported parameter is associated with the first object, where the first object includes one of the following: terminal, cell, frequency point, AI object, measurement configuration, measurement identifier, measurement object, reporting configuration.

8. The method according to any one of claims 1 to 7, wherein The method further includes: The terminal sends a second measurement report to the network-side device; Wherein, the second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

9. The method according to claim 8, wherein The method further includes: The terminal receives the second information sent by the network-side device; Wherein, the second information is used to determine the association relationship between the reporting time of the first measurement report and the reporting time of the second measurement report.

10. The method according to claim 9, wherein The second information includes the configuration information of the first timer; The terminal sends a second measurement report to the network-side device, including: The terminal starts the first timer when sending the first measurement report to the network-side device; The terminal sends the second measurement report to the network-side device when the first timer expires.

11. The method according to claim 10, wherein, The first measurement report includes the first prediction results within T prediction times or T prediction time periods, the first timer includes T timers, the T timers respectively correspond to the T prediction times or T prediction time periods, and the durations of the T timers are different, and T is an integer greater than 1; The terminal starts the first timer when sending the first measurement report to the network-side device, including: When the terminal sends the first measurement report to the network-side device, it starts the T timers; When the first timer expires, the terminal sends a second measurement report to the network-side device, including: When each of the T timers expires, the terminal sends a second measurement report corresponding to each timer to the network-side device respectively, where the second measurement report corresponding to each timer includes the actual result within the predicted time or predicted time period corresponding to each timer.

12. The method according to any one of claims 8 to 11, wherein, The first measurement report includes a first identifier, and the second measurement report includes the first identifier.

13. The method according to any one of claims 1 to 12, wherein: When a first condition is met, the terminal does not report the second measurement report or the actual result within the predicted time or predicted time period corresponding to the first predicted result, and the second measurement report includes the actual result within the predicted time or predicted time period corresponding to the first predicted result; Wherein, the first condition includes any one of the following: A handover, reconstruction or redirection occurs; The first predicted result is the same as the actual result within the predicted time or predicted time period corresponding to the first predicted result, or the difference between the first predicted result and the actual result within the predicted time or predicted time period corresponding to the first predicted result is less than or equal to a first threshold; The measurement object associated with the first measurement report is deleted; An RLF, HOF or radio resource control (RRC) state transition occurs.

14. The method according to any one of claims 1 to 13, wherein The method further includes: When a handover, reconstruction or redirection occurs, the terminal performs a second operation; Wherein, the second operation includes any one of the following: When the AI object associated with the first measurement report is in an active state, report the second measurement report to the target cell; When the AI object associated with the first measurement report is in an inactive state or has been released, do not report the second measurement report to the target cell; Wherein, the second measurement report includes the actual result within the predicted time or predicted time period corresponding to the first predicted result.

15. The method according to any one of claims 1 to 14, wherein The event prediction result includes at least one of the prediction result of the measurement event, the prediction result of the RLF, and the prediction result of the HOF.

16. The method according to claim 15, wherein, When the first predicted result includes the prediction result of the HOF, the method further includes: After the terminal sends the first measurement report to the network-side device, it starts a second timer; When the second timer expires, the terminal reports the actual result of the first predicted cell during the running of the second timer; Or, When a HOF or RRC state transition occurs during the running of the second timer, the terminal does not report the actual result of the first predicted cell during the running of the second timer.

17. The method according to claim 15, wherein, When the first predicted result includes the prediction result of the RLF, the method further includes: After the terminal sends the first measurement report to the network-side device, it starts a third timer; In the case where the third timer expires, the terminal reports the actual result of the second predicted cell during the operation of the third timer; Or, In the case where RLF or RRC state transition occurs during the operation of the third timer, the terminal does not report the actual result of the second predicted cell during the operation of the third timer.

18. An AI object monitoring method, comprising: The network side device sends a first parameter to the terminal, or the network side device receives a first measurement report sent by the terminal; Wherein, the first parameter includes at least one of the following: the monitoring metric parameter of the AI object, the reporting parameter of the monitoring result of the AI object; The first measurement report is used for the network side device to monitor the AI object, and the first measurement report includes a first prediction result based on the AI object, and the first prediction result includes at least one of a radio resource management (RRM) measurement prediction result and an event prediction result; The AI object includes an AI model or an AI function, and the AI object is an AI object for mobility management.

19. The method according to claim 18, wherein, The monitoring metric parameter of the AI object includes at least one of the following: RRM measurement prediction accuracy; Prediction accuracy of the optimal N cells, where N is a positive integer; Prediction accuracy of the optimal M beams, where M is a positive integer; Prediction accuracy of handover failure (HOF); Prediction accuracy of radio link failure (RLF); Prediction accuracy of measurement events; The number or probability of abnormal events occurring within the first time period; The handover delay or interruption duration within the second time period; The number of handovers per unit time or within the third time period.

20. The method according to claim 19, wherein, The abnormal events include at least one of the following: ping-pong handover, premature handover, late handover, handover to the wrong cell, handover failure, radio link failure.

21. The method according to any one of claims 18 to 20, wherein, The reporting parameter of the monitoring result includes at least one of the following: reporting period, reporting trigger event, reporting threshold.

22. The method according to claim 21, wherein The reporting threshold includes the thresholds corresponding to the respective monitoring metric parameters of the AI object.

23. The method according to claim 21, wherein, The reporting trigger event includes at least one of the following: The first event occurs continuously K1 times, or the first event occurs K1 times within the fourth time period, where the first event is that the RRM measurement prediction accuracy is less than or equal to the threshold corresponding to the RRM measurement prediction accuracy, and K1 is a positive integer; The second event occurs continuously K2 times, or the second event occurs K2 times within the fifth time period, where the second event is that the prediction of the optimal N cells is incorrect, or the prediction accuracy of the optimal N cells is less than or equal to the threshold corresponding to the optimal N cells, and K2 is a positive integer; The third event occurs continuously K3 times, or the third event occurs K3 times within the sixth time period, where the third event is that the prediction of the optimal M beams is incorrect, or the prediction accuracy of the optimal M beams is less than or equal to the threshold corresponding to the optimal M beams, and K3 is a positive integer; The fourth event occurs continuously K4 times, or the fourth event occurs K4 times within the seventh time period, where the fourth event is an incorrect measurement event prediction, or the prediction accuracy rate of the measurement event is less than or equal to the threshold corresponding to the measurement event, and K4 is a positive integer; The abnormal event occurs continuously K5 times, or the abnormal event occurs K5 times within the eighth time period, and K5 is a positive integer; The fifth event occurs continuously K6 times, or the fifth event occurs K6 times within the ninth time period, where the fifth event is that the handover delay or interruption duration is greater than or equal to the threshold corresponding to the handover delay or interruption duration, and K6 is a positive integer; The handover occurs continuously K7 times, or the handover occurs K7 times within the ninth time period, and K7 is a positive integer.

24. The method according to any one of claims 18 to 23, wherein The reporting parameter is associated with the first object, where the first object includes one of the following: terminal, cell, frequency point, AI object, measurement configuration, measurement identifier, measurement object, reporting configuration.

25. The method according to any one of claims 18 to 24, wherein The method further includes: The network-side device receives the second measurement report sent by the terminal; Wherein, the second measurement report includes the actual result within the prediction time or prediction time period corresponding to the first prediction result.

26. The method according to claim 25, wherein The method further includes: The network-side device sends second information to the terminal; Wherein, the second information is used to determine the association relationship between the reporting time of the first measurement report and the reporting time of the second measurement report.

27. The method according to claim 26, wherein The second information includes the configuration information of a first timer, and the first timer is used to control the reporting time of the second measurement report.

28. The method according to claim 27, wherein, The first measurement report includes the first prediction results within T prediction times or T prediction time periods, the first timer includes T timers, the T timers respectively correspond to the T prediction times or T prediction time periods, and the durations of the T timers are different, and T is an integer greater than 1.

29. The method according to any one of claims 25 to 28, wherein The first measurement report includes a first identifier, and the second measurement report includes the first identifier.

30. The method according to any one of claims 18 to 29, wherein The event prediction result includes at least one of the prediction result of the measurement event, the prediction result of RLF, and the prediction result of HOF.

31. An AI object monitoring device, comprising: A first execution module, configured to perform a first operation according to a first parameter, or send a first measurement report to a network-side device; Wherein, the first operation includes at least one of AI object monitoring and reporting of the monitoring result of the AI object, and the first parameter includes at least one of the following: the monitoring metric parameter of the AI object, the reporting parameter of the monitoring result of the AI object; The first measurement report is used to monitor the AI object, and the first measurement report includes a first prediction result based on the AI object, and the first prediction result includes at least one of a radio resource management (RRM) measurement prediction result and an event prediction result; The AI object includes an AI model or an AI function, and the AI object is an AI object for mobility management.

32. An AI object monitoring device, comprising: A transmission module, configured to send a first parameter to a terminal, or receive the first measurement report sent by the terminal; Wherein, the first parameter includes at least one of the following: the monitoring metric parameter of the AI object, the reporting parameter of the monitoring result of the AI object; The first measurement report is used for the network-side device to monitor the AI object, and the first measurement report includes a first prediction result based on the AI object, and the first prediction result includes at least one of a radio resource management (RRM) measurement prediction result and an event prediction result; The AI object includes an AI model or an AI function, and the AI object is an AI object for mobility management.

33. A terminal, comprising a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the AI object monitoring method according to any one of claims 1 to 17 are implemented.

34. A network-side device, comprising a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the AI object monitoring method according to any one of claims 18 to 30 are implemented.

35. 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 monitoring method according to any one of claims 1 to 17 are implemented, or the steps of the AI object monitoring method according to any one of claims 18 to 30 are implemented.

36. A computer program product, where the computer program product includes a computer program or computer instruction, and the computer program or computer instruction is executed by at least one processor to implement the steps of the AI object monitoring method according to any one of claims 1 to 17, or to implement the steps of the AI object monitoring method according to any one of claims 18 to 30.

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