Data processing method and device, terminal and network side equipment

Abstract

The invention discloses a data processing method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the data processing method comprises the steps that the terminal executes a first operation, and the first operation comprises at least one of the following items: receiving first configuration information from the network side equipment, under the condition that at least one target condition in data collection conditions is met, first data are collected, and the first configuration information is used for configuring the data collection conditions; receiving a reporting condition from the network side equipment, and reporting the first data collected by the terminal to the network side equipment according to the reporting condition; under the condition that the model reasoning condition is met, performing model reasoning based on the target model; wherein the first data is used for training an artificial intelligence (AI) model to obtain a target model.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and specifically relates to a data processing method, device, terminal and network side equipment. Background Art

[0002] Artificial Intelligence (AI) has been widely used in various fields. Integrating AI into wireless communication networks and significantly improving technical indicators such as throughput, latency, and user capacity are important tasks for future wireless communication networks. In order to apply AI models to wireless communication networks, it is necessary to collect and report the training data of the AI ​​models, and to clarify the reasoning timing of the AI ​​models. Therefore, how to apply AI models to wireless communication networks has become an urgent problem to be solved. Summary of the invention

[0003] The embodiments of the present application provide a data processing method, apparatus, terminal, and network-side equipment, which can solve the problem of how to apply AI models to wireless communication networks.

[0004] In a first aspect, a data processing method is provided, comprising:

[0005] The terminal performs a first operation, where the first operation includes at least one of the following:

[0006] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0007] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0008] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0009] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0010] In a second aspect, a data processing method is provided, comprising:

[0011] The network-side device performs a second operation, where the second operation includes at least one of the following:

[0012] Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection;

[0013] Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data;

[0014] Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model;

[0015] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0016] In a third aspect, a data processing device is provided, comprising:

[0017] The first execution module is configured to execute a first operation, wherein the first operation includes at least one of the following:

[0018] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0019] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0020] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0021] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0022] In a fourth aspect, a data processing device is provided, comprising:

[0023] The second execution module is configured to execute a second operation, where the second operation includes at least one of the following:

[0024] Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection;

[0025] Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data;

[0026] Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model;

[0027] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0028] 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.

[0029] In a sixth aspect, a terminal is provided, including a processor and a communication interface, wherein the communication interface is used to perform a first operation, and the first operation includes at least one of the following:

[0030] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0031] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0032] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0033] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0034] 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 second aspect are implemented.

[0035] In an eighth aspect, a network side device is provided, including a processor and a communication interface, wherein the communication interface is used to perform a second operation, and the second operation includes at least one of the following:

[0036] Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection;

[0037] Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data;

[0038] Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model;

[0039] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0040] In a ninth 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 second aspect are implemented.

[0041] In the tenth aspect, a wireless communication system is provided, including: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.

[0042] In the eleventh aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0043] In the twelfth aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a storage medium, and the program / program product is executed by at least one processor to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0044] In an embodiment of the present application, a first operation is performed through a terminal, and the first operation includes at least one of the following: receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is met, and the first configuration information is used to configure the data collection condition; receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition; performing model reasoning based on the target model when the model reasoning condition is met; wherein the first data is used to train the artificial intelligence AI model to obtain the target model. In this way, since it is clear that the first data is collected based on the data collection condition, the first data is reported based on the reporting condition, and the model reasoning is performed based on the model reasoning condition, the AI ​​model can be applied to the wireless communication network to improve the communication performance of the wireless communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a block diagram of a wireless communication system to which the embodiments of the present application can be applied;

[0046] Figure 2 It is a schematic diagram of the structure of neurons in a neural network;

[0047] Figure 3 It is a flowchart of a data processing method provided in an embodiment of the present application;

[0048] Figure 4 It is a flowchart of another data processing method provided in an embodiment of the present application;

[0049] Figure 5 is a structural schematic diagram of a data processing device provided in an embodiment of the present application;

[0050] Figure 6 is a structural schematic diagram of another data processing device provided in an embodiment of the present application;

[0051] Figure 7 It is a structural diagram of a communication device provided in an embodiment of the present application;

[0052] Figure 8 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0053] Fig. 9 It is a structural diagram of a network side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The terms "first", "second", etc. of the present 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 one type, and the number of objects is not limited, for example, the first object can be one or more. In addition, "or" in the present application represents at least one of the connected objects. For example, "A or B" covers three schemes, namely, Scheme 1: including A but not including B; Scheme 2: including B but not including A; Scheme 3: including both A and B. The character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0055] The term "indication" in this application can be 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, operations to be performed, or request results in the sent indication; an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.

[0056] 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 described technology 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 descriptions, but these technologies can also be applied to systems other than NR systems, such as the 6th generation (6 th Generation, 6G) communication system.

[0057] Figure 1A block diagram of a wireless communication system applicable to an embodiment of the present application is shown. The wireless communication system includes a terminal 11 and a network side device 12. Among them, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (Augmented Reality, AR), a virtual reality (Virtual Reality, VR) device, a robot, a wearable device (Wearable Device), an aircraft (flight vehicle), a vehicle-mounted device (Vehicle User Equipment, VUE), a ship-mounted device, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (Personal Computer, PC), a teller machine or a self-service machine and 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 referred to as 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 (Wireless Local Area Network, WLAN) access point (Access Point, AP) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.Among them, the base station may be referred to as a Node B (NB), an evolved Node B (eNB), a next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a radio base station, a radio transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a Home Node B (HNB), a Home Evolved Node B (home evolved Node B), a 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.

[0058] 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 (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home user server (Home Subscriber Server, HSS), centralized network configuration (CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), binding support function (Binding Support Function, BSF), application function (Application Function, AF), etc. 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.

[0059] For ease of understanding, some contents involved in the embodiments of the present application are described below:

[0060] 1. Artificial Intelligence

[0061] Artificial intelligence has been widely used in various fields. There are many ways to implement AI modules (i.e., AI models), such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example for illustration, but does not limit the specific type of AI modules.

[0062] Optionally, a neural network is composed of neurons, a schematic diagram of which is shown in FIG. Figure 2As shown, a1, a2, ... aK are inputs, w is the weight, i.e. the multiplicative coefficient, b is the bias, i.e. the additive coefficient, and σ(.) is the activation function. Common activation functions include Sigmoid, tanh, and Rectified Linear Unit (ReLU). Among them, z = a1w1+···+a k w k +···+a K w K +b.

[0063] The parameters of the neural network are optimized through optimization algorithms. Optimization algorithms are a type of algorithm that can help us minimize or maximize the objective function, which can also be called the loss function. The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we build a neural network model f(.). With the model, we can get the predicted output f(x) based on the input x, and we can calculate the difference between the predicted value and the true value (f(x)-Y), which is the loss function. Our goal is to find a suitable W,b to minimize the value of the above loss function. The smaller the loss value, the closer our model is to the actual situation.

[0064] At present, the common optimization algorithms are basically based on the error back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two processes: the forward propagation of the signal and the back propagation of the error. During the forward propagation, the input sample is transmitted from the input layer, processed by each hidden layer layer by layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error back propagation stage. Error back propagation is to propagate the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all units in each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer of the signal forward propagation and error back propagation is repeated. The process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the pre-set number of learning times is reached.

[0065] Common optimization algorithms include: Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov, Adaptive Gradient Descent (Adagrad), Adadelta, root mean square prop (RMSprop) and Adaptive Moment Estimation (Adam).

[0066] When these optimization algorithms backpropagate errors, they all calculate the derivative or partial derivative of the current neuron based on the error or loss obtained by the loss function, add the influence of the learning rate, previous gradient, derivative or partial derivative, obtain the gradient, and pass the gradient to the previous layer.

[0067] 2. AI model.

[0068] In this application, the AI ​​model may be referred to as an AI unit, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI characteristic, a neural network, a neural network function, a neural network function, etc., or the 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 ​​model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​model may be a processing method, algorithm, function, module or unit running on AI or ML related hardware such as a graphics processing unit (GPU), a neural processing unit (NPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), etc., and no further limitation is made here.

[0069] Optionally, the specific data set includes at least one of the input and output of the AI ​​model.

[0070] Optionally, the identifier of the 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 ​​model, or an identifier of a specific scenario, environment, channel feature, device related to the AI ​​or ML, or an identifier of a function, feature, capability or module related to the AI ​​or ML, and no further limitation is given herein.

[0071] 3. AI-based mobility management.

[0072] The main research directions of AI-based mobility management include:

[0073] Target cell or target beam prediction: that is, based on the historical measurement results of the serving cell or neighboring cell and other auxiliary information, the optimal target cell or target beam at the future moment is predicted to switch to the target cell and target beam in advance.

[0074] Target cell or target beam signal quality prediction; that is, based on the historical measurement results of the serving cell or neighboring cell and other auxiliary information, the signal quality of the target cell or target beam at the future moment is predicted, and the optimal target cell or target beam is determined according to the predicted signal quality, so as to switch to the target cell or target beam in advance.

[0075] Abnormal handover event prediction: Based on the historical measurement results of the serving cell or neighboring cell and other auxiliary information, abnormal handover events that may occur in the future are predicted to adjust the handover strategy to avoid abnormal handover events.

[0076] Through the above AI mobility prediction, handover problems such as premature handover, late handover, ping-pong handover, handover to the wrong cell, and long handover delay can be avoided.

[0077] In order to reduce the power consumption of AI model operation, the AI ​​mobility function should be used in scenarios where there are or may be switching problems. The embodiment of the present application proposes a data processing method for the AI ​​mobility management function to determine the circumstances for data collection, data reporting and model reasoning.

[0078] The data processing method provided in the embodiment of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

[0079] Reference Figure 3 , the present application embodiment provides a data processing method, such as Figure 3 As shown, the data processing method includes:

[0080] Step 301: The terminal performs a first operation, where the first operation includes at least one of the following:

[0081] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0082] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0083] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0084] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0085] In an embodiment of the present application, the above-mentioned first configuration information is used to configure at least one data collection condition, and the above-mentioned target condition is part or all of the conditions in the configured at least one data collection condition. Since the network-side device configures the data collection conditions for the terminal, the terminal can collect the first data after the corresponding data collection conditions are met. This can avoid excessive data collection, improve the pertinence of data collection, and thus improve the accuracy of the trained AI model prediction. At the same time, the power consumption of terminal data collection and the signaling overhead when the terminal reports the collected data are reduced.

[0086] It should be understood that when at least one target condition is met, collecting the first data can be understood as: collecting the first data when some or all of the M target conditions are met, where M is a positive integer. For example, when M is greater than 1, it can be understood that when any one of the M target conditions is met, the collection of the first data is triggered; it can also be understood that when N target conditions are met at the same time, the collection of the first data is triggered, where N is an integer less than or equal to M, and N is greater than 1.

[0087] Optionally, when the first operation performed by the terminal includes collecting first data, after collecting the first data, the first data can be used as sample data to train an AI model on the terminal or a network side device. After completing the training of the AI ​​model, the terminal can use the trained AI model to perform model reasoning to obtain reasoning results, and finally perform AI-based mobility management or other non-mobility management functions (for example, beam management and heterofrequency prediction) based on the reasoning results.

[0088] It should be noted that in the embodiment of the present application, the AI ​​model can be trained by the network side device, or by the terminal. Optionally, when the AI ​​model is trained by the network side device, the terminal needs to report the collected first data to the network side device, and configure the reporting conditions for the terminal through the network side device, so that the terminal can report the first data according to the reporting conditions when the corresponding reporting conditions are met. Optionally, the above-mentioned reporting conditions can represent the conditions for triggering the reporting, so that when the reporting conditions are met, the terminal is triggered to report the first data. The network side device can perform model training based on the reported first data, and after completing the model training, the trained target model can be sent to the terminal, so that the terminal can perform model reasoning based on the target model to obtain reasoning results, and finally perform other functions of AI mobility management or non-mobility management based on the reasoning results.

[0089] Optionally, the terminal may obtain model input data for mobility management, and then input the model data into the target model for model reasoning, thereby obtaining a reasoning result, which may include information such as the cell identifier of the handover and an indication of whether to handover, etc. The terminal executes a related process based on the reasoning result.

[0090] Optionally, in some embodiments, the target model may be an AI model for mobility management, in which case the handover performance may be optimized. In addition, the target model may also be an AI model for non-mobility management.

[0091] For example, in the process of mobility management, switching is currently usually performed based on the signal quality of the cell. However, in some scenarios, due to the unstable signal quality of the cell, switching problems such as premature switching, late switching, ping-pong switching, switching to the wrong cell, and long switching delays may occur. When using the AI ​​model for mobility management, it can be determined whether to perform a cell switch and determine the target cell for switching based on the inference results of the above model to execute relevant processes of mobility management, such as initiating a switching process. This can avoid switching problems such as premature switching, late switching, ping-pong switching, switching to the wrong cell, and long switching delays. Therefore, the embodiments of the present application can apply the AI ​​model to mobility management to improve mobility management performance.

[0092] For example, in the traditional beam management process, the terminal needs to scan and measure all beams to determine the optimal beam. The delay in determining the optimal beam is too long, and too many measurements will also cause the terminal to consume more power. In AI-based beam management, based on the measurement results of some beams in the serving cell, the optimal beam or the signal quality of the optimal beam in the serving cell is predicted to reduce the power consumption and delay caused by beam measurement.

[0093] For example, in the traditional measurement process, the terminal needs to measure the signal quality of the inter-frequency cell in the measurement interval configured by the network. During the measurement interval, the terminal cannot send or receive data, and the inter-frequency measurement will cause terminal power consumption and data interruption. In AI-based inter-frequency prediction, the terminal predicts the signal quality of the inter-frequency cell based on the measurement results of the same-frequency cell to reduce the power consumption and data interruption caused by the terminal performing inter-frequency measurement.

[0094] It should be understood that in the embodiment of the present application, the first data reported by the terminal may be the first data collected based on the above-mentioned target conditions, or may be the first data collected not based on the above-mentioned target conditions, and no further limitation is made herein.

[0095] Optionally, the above-mentioned model reasoning conditions can be configured by the network side device or agreed upon by the protocol. When the terminal determines that the conditions for model reasoning are met, the terminal can perform model reasoning. Since the model reasoning conditions are used to trigger the terminal to perform model reasoning, the timing for the terminal to perform model reasoning can be clarified. At the same time, the power consumption of the terminal caused by too frequent model reasoning can be reduced.

[0096] It should be understood that the above-mentioned first data can be used for initial or retraining of the above-mentioned target model.

[0097] In an embodiment of the present application, a first operation is performed through a terminal, and the first operation includes at least one of the following: receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is met, and the first configuration information is used to configure the data collection condition; receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition; performing model reasoning based on the target model when the model reasoning condition is met; wherein the first data is used to train the artificial intelligence AI model to obtain the target model. In this way, since it is clear that the first data is collected based on the data collection condition, the first data is reported based on the reporting condition, and the model reasoning is performed based on the model reasoning condition, the AI ​​model can be applied to the wireless communication network to improve the communication performance of the wireless communication network.

[0098] Optionally, when the AI ​​model is applied to mobility management, in order to reduce the overhead of first data reporting, the AI ​​mobility function should be used in scenarios where there is a handover problem or a handover problem may exist, and the data processing method provided in the embodiment of the present application can also determine under what circumstances to report. For example, in some embodiments, the reporting condition includes at least one of the following:

[0099] The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data.

[0100] The amount of the first data collected is greater than a preset threshold;

[0101] The network side device indicates that reporting is implemented based on the terminal;

[0102] Cell selection occurs;

[0103] Cell reselection occurs;

[0104] A cell handover occurs;

[0105] An RRC connection establishment occurs;

[0106] RRC reestablishment occurs;

[0107] RRC connection recovery occurs;

[0108] N switching failures or abnormal switching events occur, where N is a positive integer;

[0109] Leave the cell list configured by the network side device.

[0110] In the embodiment of the present application, the collected first data may be reported after the reporting condition is met, or the collected first data may be reported in the uplink resources subsequently allocated by the network side device after the reporting condition is met. It should be understood that in other embodiments, the above-mentioned reporting condition may also be agreed upon by a protocol.

[0111] It should be understood that the above-mentioned target timer can be started or restarted when a certain target condition is met. For example, if a certain target condition is met, the target timer can be started; during the operation of the target timer, if another target condition is met, the target timer can be restarted. The target timer can also be started when all target conditions are met. Since the target timer is used to determine the timing of reporting, the timing duration of the target timer can be set so that the reported first data can meet the data volume required for model training, and at the same time, the timeliness of the first data used for model training can be guaranteed, thereby improving the accuracy of model prediction after training. At the same time, the problems of terminal power consumption and large memory usage caused by the long collection time of the first data can be avoided.

[0112] Optionally, when the reporting condition includes that the amount of the first data collected is greater than a preset threshold, the terminal can report the first data when the amount of the first data collected is greater than the preset threshold, so that the reported first data can meet the data volume required for model training, and at the same time, the timeliness of the first data used for model training can be guaranteed, thereby improving the accuracy of model prediction after training. At the same time, it can avoid the collection of the first data from occupying more memory.

[0113] Optionally, for the network side device indicating that the reporting is based on the terminal, when receiving the network side device indicating that the reporting is based on the terminal, the terminal can report autonomously according to the actual situation, thereby improving the flexibility of reporting the first data. It should be understood that when reporting is based on the terminal, the terminal can report at any time based on the implementation, that is, the terminal reports autonomously, and at this time the terminal can instruct the network side device to report.

[0114] Optionally, when the reporting conditions include cell selection, cell reselection, cell switching, RRC connection establishment, RRC reconstruction or RRC connection recovery, the reporting can be performed after the cell selection, cell reselection, cell switching, RRC connection establishment, RRC reconstruction or RRC connection recovery occurs, so that the first data can be reported in time, so that the target model can be trained or retrained to improve the accuracy of AI model prediction.

[0115] Optionally, when the reporting conditions include N switching failures or abnormal switching events, the terminal can record the number of switching failures or abnormal switching events, and then report the first data when the number reaches N times, so that the reported first data can meet the data volume required for model training, and at the same time can ensure the timeliness of the first data used for model training, thereby improving the accuracy of model prediction after training.

[0116] Optionally, since the reported first data is used for training the model, it is used for predicting the cells in the cell list, rather than the cells outside the cell list. In this way, when the reporting condition includes leaving the cell list configured by the network side device, the terminal can report the collected first data when the terminal switches to or reselects to other cells outside the cell list, avoiding reporting invalid first data collected after switching to other cells. Therefore, the validity of the reported first data is improved.

[0117] It should be understood that the above-mentioned reporting condition may include one or more of the above-mentioned conditions. For example, the reporting condition includes L conditions, where L is a positive integer. When L is greater than 1, the terminal may be triggered to report the first data to the network side device when any one of the conditions is met. The terminal may also be triggered to report the first data to the network side device when at least two conditions are met at the same time.

[0118] It should be noted that, in other embodiments, the terminal may also periodically report the first data, for example, periodically report according to a reporting period configured by the network side device. In addition, the terminal may also report according to a request of the network side device, for example, the network side device may send a first data reporting request to the terminal, and the terminal reports the collected first data based on the request.

[0119] Optionally, when applying the AI ​​model to mobility management, in order to reduce the power consumption of the AI ​​model, the embodiment of the present application defines the data collection conditions and the model reasoning conditions when using the AI ​​mobility function. For example, in some embodiments, at least one of the data collection conditions and the model reasoning conditions includes at least one of the following:

[0120] Condition 1: the location of the terminal is within a preset range;

[0121] Condition 2: within a configured time window, the average speed of the terminal exceeds a first threshold or is within a first range;

[0122] Condition 3: within the configured time window, the speed of the terminal continuously exceeds the second threshold value or is within the second range;

[0123] Condition 4: the serving cell signal quality is lower than or equal to the third threshold or is within the third range;

[0124] Condition 5: the signal quality of the neighboring cell is higher than or equal to the fourth threshold or is within the fourth range;

[0125] Condition 6: the signal quality of the neighboring cell is higher than the signal quality of the serving cell, and the difference between the signal quality of the neighboring cell and the signal quality of the serving cell is greater than or equal to the fifth threshold value or is within the fifth range;

[0126] Condition 7: within the configured time window, the average signal quality of the serving cell is lower than or equal to the sixth threshold or is within the sixth range;

[0127] Condition 8: within the configured time window, the average signal quality of the neighboring cells is higher than or equal to the seventh threshold or is within the seventh range;

[0128] Condition 9: within the configured time window, the average signal quality of the neighboring cell is higher than the average signal quality of the serving cell, and the difference between the average signal quality of the neighboring cell and the average signal quality of the serving cell is greater than or equal to the eighth threshold or is within the eighth range;

[0129] Condition 10: within the configured time window, the serving cell signal quality continues to be lower than the ninth threshold or within the ninth range;

[0130] Condition 11: within the configured time window, the signal quality of the neighboring cell is continuously lower than the tenth threshold or within the tenth range;

[0131] Condition 12: within the configured time window, the number of abnormal switching events occurring at the terminal is greater than or equal to the eleventh threshold;

[0132] Condition 13: within the configured time window, the ratio of the number of abnormal switching events occurring at the terminal to the total number of switching events is greater than or equal to the twelfth threshold;

[0133] Condition 14: within the configured time window, the signal quality of the terminal in the serving cell decreases by X dB;

[0134] Condition 15: within the configured time window, the signal quality of the terminal in the neighboring area is reduced by Y dB.

[0135] In the embodiment of the present application, since the data collection conditions are clarified, invalid first data collection can be avoided, thereby improving the accuracy of AI model training. In addition, since the model reasoning conditions are clarified, the power consumption caused by invalid model reasoning can be reduced.

[0136] Optionally, for condition 1, the terminal can use the AI ​​model for mobility management within a specified range to improve the reliability of mobility management within the specified range. The network side device can flexibly specify the range in which the AI ​​model needs to be used for mobility management, thereby improving the flexibility of using the AI ​​model for mobility management and reducing power consumption caused by invalid model reasoning.

[0137] For the above conditions 2 and 3, the terminal can use the AI ​​model to perform mobility management for high-speed moving terminals to avoid the high-speed moving terminals from switching too early, too late, ping-pong switching or switching to the wrong cell.

[0138] For the above conditions 4 to 11 and conditions 14 and 15, the terminal can determine whether to use the AI ​​model for mobility management based on the signal quality of the serving cell and the neighboring cell, thereby avoiding terminal ping-pong switching or switching to the wrong cell when the signal quality of the serving cell is poor and the signal quality of the neighboring cell is good.

[0139] For the above conditions 12 and 13, the terminal can determine whether to use the AI ​​model for mobility management based on the number and proportion of abnormal switching events, thereby determining the target cell for switching based on the model reasoning results, reducing the number of abnormal switching events, and avoiding excessive switching delays.

[0140] It should be understood that the conditions included in the above-mentioned data collection conditions and model reasoning conditions may be the same or different. When the conditions included in the data collection conditions and model reasoning conditions are the same, model reasoning can be performed based on the current target model when the target conditions are met, and the first data collection can be performed at the same time. The first data collection is used for subsequent re-training of the target model to update the current target model.

[0141] Optionally, the preset range may be a longitude and latitude range configured by the network-side device, and X and Y are constants.

[0142] It should be noted that, in the embodiment of the present application, the above-mentioned continuous corresponding time length can be understood as the entire duration of the configured time window, or can be understood as a partial duration of the configured time window. For example, the above-mentioned speed of the terminal continuously exceeds the second threshold within the configured time window can be understood as that within the configured time window, the average speed of the terminal exceeds the second threshold, or within the configured time window, the duration of the average speed of the terminal exceeding the second threshold is greater than a preset duration (such as half of the duration of the configured time window).

[0143] Optionally, the abnormal handover event may include ping-pong handover, premature handover, late handover, handover to a wrong cell, handover failure, radio link failure, and the like.

[0144] Optionally, the proportion of abnormal switching events occurring in the terminal may be understood as a ratio obtained by dividing the number of abnormal switching times by the total number of switching times.

[0145] Optionally, the method further comprises:

[0146] When a stop collection condition is met, the terminal stops collecting the first data; wherein the stop collection condition includes at least one of the following:

[0147] The target condition is not met;

[0148] The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the duration of collecting the first data.

[0149] In the embodiment of the present application, the stop collection condition matches the above data collection condition. Specifically, when the stop collection condition is met, stopping the collection of the first data can be understood as at least one of the following:

[0150] When any one of the M target conditions is met, the collection of the first data is triggered, and when all the M target conditions are not met, the collection of the first data is stopped.

[0151] When N target conditions out of M target conditions are met, the collection of the first data is triggered. When the number of target conditions met is less than N (ie, the number of target conditions not met is greater than MN), the collection of the first data is stopped.

[0152] In the embodiment of the present application, since the condition for stopping the collection of the first data is defined, the collection of invalid first data can be avoided, thereby reducing the power consumption of the terminal.

[0153] Optionally, in some embodiments, the first data includes at least one of the following:

[0154] A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0155] The target information of the terminal and a timestamp corresponding to the target information, the target information including at least one of speed, position, direction and beam information;

[0156] An identifier of a second cell and a timestamp corresponding to the identifier of the second cell, where the second cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0157] The condition type of the target condition;

[0158] The number of abnormal switching events that occurred;

[0159] The ratio of the number of abnormal switching events to the total number of switching events.

[0160] In an embodiment of the present application, the identifier of the second cell may include a physical cell identifier (Physical Cell Identifier, PCI), a frequency, and an NR Cell Global Identifier (NR Cell Global Identifier, NCGI), etc.

[0161] Optionally, the timestamp corresponding to the signal quality can be understood as the time information when the signal quality is obtained, the timestamp corresponding to the above-mentioned target information can be understood as the time information when the target information is obtained, and the timestamp corresponding to the identifier of the second cell can be understood as the time information when the signal quality of the second cell is obtained or the time information when the second cell is selected. Since the timestamp corresponding to the signal quality, the timestamp corresponding to the target information and the timestamp corresponding to the identifier of the second cell are included in the reported first data, the network side device can select the corresponding signal quality, target information and the identifier of the second cell as sample data for model training according to the timestamp, for example, select relevant data within a preset time period from the current time for model training, thereby improving the accuracy of the trained model prediction.

[0162] It should be noted that the above-mentioned signal quality may include the reference signal receiving power (RSRP) of the cell or the beam within the cell, the reference signal receiving quality (RSRP) and the signal to interference plus noise ratio (SINR).

[0163] Optionally, the timestamp may be represented by units such as year, month, day, hour, minute, second, millisecond, frame, subframe, time slot, symbol, etc. Specifically, the time granularity of the timestamp may be millisecond, frame, subframe, time slot, or symbol.

[0164] Optionally, in some embodiments, when the model reasoning condition is met, before performing model reasoning based on the target model, the method further includes:

[0165] The terminal receives second configuration information from the network side device, where the second configuration information is used to determine the applicable scope of the target model reasoning;

[0166] The second configuration information satisfies any of the following:

[0167] The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model;

[0168] The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells;

[0169] The second configuration information includes fourth information, fifth information and sixth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate the cell list in the activated cell list that is suitable for the target model;

[0170] The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model;

[0171] The second configuration information is used to configure the intra-frequency cell to be applicable to the target model;

[0172] The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

[0173] In an embodiment of the present application, when the second configuration information is used to configure a cell list to which the target model is applicable, the network side device may indicate that the cells included in the inferred cell list may be cells within a notification area (RAN-based Notification Area, RNA) based on a radio access network (Radio Access Network, RAN), cells within a tracking area (Tracking Area, TA), conditional handover (Conditional Handover, CHO) candidate cells, conditional primary and secondary cell addition or change (Conditional Pscell Addition or Change, CPAC) candidate cells, and layer 1 or layer 2 triggered mobility (L1 / L2-Triggered Mobility, LTM) candidate cells.

[0174] Optionally, in some embodiments, the network side device may configure multiple cells or cell lists through RRC signaling, and activate some cells or cell lists through a medium access control control element (MAC CE) for model reasoning.

[0175] Optionally, in some embodiments, the network side device may configure multiple cells or cell lists through RRC signaling, activate some cells or cell lists through MAC CE, and perform model inference on some cells or cell lists in some cells or cell lists activated by MAC CE through downlink control information (Downlink Control Information, DCI).

[0176] Optionally, in some embodiments, the cell list includes at least one of the following: a cell list within the access network notification area RNA; a cell list within the tracking area TA; a conditional switching CHO candidate cell; a conditional primary and secondary cell addition or modification CPAC candidate cell list; a layer one or layer two triggered mobility LTM candidate cell list.

[0177] Optionally, in some embodiments, the method further comprises:

[0178] The terminal receives second data from the network-side device, where the second data is input data of the target model, and the second data includes at least one of the following:

[0179] Location information of the network side device;

[0180] The antenna orientation of the network side device;

[0181] The beam direction of the network side device;

[0182] The beam width of the network side device;

[0183] The identifier associated with the transmit beam of the network side device.

[0184] In an embodiment of the present application, the network side device can send the input required for model reasoning to the terminal when issuing the target model or configuring the scope of application of model reasoning, or the terminal can request the network side device to obtain the model input required for model reasoning.

[0185] Optionally, in some embodiments, after reporting the first data collected by the terminal to the network side device according to the reporting condition, the method further includes:

[0186] The terminal receives third configuration information from the network side device, where the third configuration information is used to configure the target model.

[0187] It should be understood that if the network side device sends the input required for model reasoning to the terminal when issuing the target model or configuring the scope of application of model reasoning, the above-mentioned second data can be carried in the above-mentioned second configuration information or third configuration information.

[0188] In order to better understand the present application, some examples are described in detail below.

[0189] In some embodiments, assuming that the network side device performs model training and the terminal side performs model reasoning, the following process may be included:

[0190] Step 0: The terminal receives a data collection condition configured by a network-side device, for example, the data collection condition is that the signal quality of the serving cell is continuously lower than X dB within a configured time window;

[0191] Step 1: The terminal meets the data collection conditions, collects and reports data;

[0192] Step 2: The terminal receives the AI ​​model, model reasoning conditions, and applicable scope of AI model reasoning sent by the network side device;

[0193] Step 3: After the model reasoning conditions are met, the terminal performs model reasoning within the applicable scope of AI model reasoning.

[0194] Optionally, before step 2 or step 3, the terminal requests the network to obtain the input required for model reasoning (ie, the second data), or the terminal receives the input required for model reasoning from a network-side device.

[0195] Reference Figure 4 , the embodiment of the present application also provides a data processing method, such as Figure 4 As shown, the data processing method includes:

[0196] Step 401: The network-side device performs a second operation, where the second operation includes at least one of the following:

[0197] Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection;

[0198] Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data;

[0199] Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model;

[0200] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0201] Optionally, the reporting condition includes at least one of the following:

[0202] The target timer times out. When at least one target condition among the data collection conditions is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data.

[0203] The amount of the first data collected is greater than a preset threshold;

[0204] The network side device indicates that reporting is implemented based on the terminal;

[0205] Cell selection occurs;

[0206] Cell reselection occurs;

[0207] A cell handover occurs;

[0208] An RRC connection establishment occurs;

[0209] RRC reestablishment occurs;

[0210] RRC connection recovery occurs;

[0211] N switching failures or abnormal switching events occur, where N is a positive integer;

[0212] Leave the cell list configured by the network side device.

[0213] Optionally, the cell list is a list used to control the first data reporting area.

[0214] Optionally, at least one of the data collection condition and the model inference condition includes at least one of the following:

[0215] The location of the terminal is within a preset range;

[0216] Within a configured time window, the average speed of the terminal exceeds a first threshold or is within a first range;

[0217] Within the configured time window, the speed of the terminal continuously exceeds the second threshold value or is within the second range;

[0218] The serving cell signal quality is lower than or equal to a third threshold or is within a third range;

[0219] The signal quality of the neighboring cell is higher than or equal to the fourth threshold or is within the fourth range;

[0220] The signal quality of the neighboring cell is higher than that of the serving cell, and the difference between the signal quality of the neighboring cell and that of the serving cell is greater than or equal to the fifth threshold or is within the fifth range;

[0221] Within the configured time window, the average signal quality of the serving cell is lower than or equal to the sixth threshold or is within the sixth range;

[0222] In the configured time window, the average signal quality of the neighboring cells is higher than or equal to the seventh threshold or is within the seventh range;

[0223] In the configured time window, the average signal quality of the neighboring cell is higher than the average signal quality of the serving cell, and the difference between the average signal quality of the neighboring cell and the average signal quality of the serving cell is greater than or equal to the eighth threshold or is within the eighth range;

[0224] Within the configured time window, the signal quality of the serving cell continues to be lower than the ninth threshold or within the ninth range;

[0225] In the configured time window, the signal quality of the neighboring cell is continuously lower than the tenth threshold or in the tenth range;

[0226] Within the configured time window, the number of abnormal switching events occurring at the terminal is greater than or equal to the eleventh threshold;

[0227] Within the configured time window, the ratio of the number of abnormal switching events occurring at the terminal to the total number of switching events is greater than or equal to the twelfth threshold;

[0228] Within the configured time window, the signal quality of the terminal in the serving cell decreases by X dB;

[0229] Within the configured time window, the signal quality of the terminal in the neighboring cell is reduced by Y dB.

[0230] Optionally, the first data includes at least one of the following:

[0231] A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0232] A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0233] The target information of the terminal and a timestamp corresponding to the target information, the target information including at least one of speed, position, direction and beam information;

[0234] An identifier of a second cell and a timestamp corresponding to the identifier of the second cell, where the second cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0235] The condition type of the target condition;

[0236] The number of abnormal switching events that occurred;

[0237] The ratio of the number of abnormal switching events to the total number of switching events.

[0238] Optionally, the method further comprises:

[0239] The network side device sends second configuration information to the terminal, where the second configuration information is used to determine the applicable scope of the target model reasoning;

[0240] The second configuration information satisfies any of the following:

[0241] The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model;

[0242] The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells;

[0243] The second configuration information includes fourth information, fifth information and sixth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate the cell list in the activated cell list that is suitable for the target model;

[0244] The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model;

[0245] The second configuration information is used to configure the intra-frequency cell to be applicable to the target model;

[0246] The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

[0247] Optionally, the cell list includes at least one of the following: a cell list within the access network notification area RNA; a cell list within the tracking area TA; a conditional switching CHO candidate cell; a conditional primary and secondary cell addition or modification CPAC candidate cell list; a layer one or layer two triggered mobility LTM candidate cell list.

[0248] Optionally, the method further comprises:

[0249] The network side device sends second data to the terminal, where the second data is input data of the target model, and the second data includes at least one of the following:

[0250] Location information of the network side device;

[0251] The antenna orientation of the network side device;

[0252] The beam direction of the network side device;

[0253] The beam width of the network side device;

[0254] The identifier associated with the transmit beam of the network side device.

[0255] Optionally, after sending the reporting condition to the terminal, the method further includes:

[0256] The network side device sends third configuration information to the terminal, where the third configuration information is used to configure the target model.

[0257] The data processing method provided in the embodiment of the present application can be executed by a data processing device. In the embodiment of the present application, the data processing device provided in the embodiment of the present application is described by taking the data processing method executed by the data processing device as an example.

[0258] Reference Figure 5 , the embodiment of the present application also provides a data processing device, such as Figure 5 As shown, the data processing device 500 includes:

[0259] The first execution module 501 is configured to execute a first operation, wherein the first operation includes at least one of the following:

[0260] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0261] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0262] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0263] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0264] Optionally, the reporting condition includes at least one of the following:

[0265] The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data.

[0266] The amount of the first data collected is greater than a preset threshold;

[0267] The network side device indicates that reporting is implemented based on the terminal;

[0268] Cell selection occurs;

[0269] Cell reselection occurs;

[0270] A cell handover occurs;

[0271] An RRC connection establishment occurs;

[0272] RRC reestablishment occurs;

[0273] RRC connection recovery occurs;

[0274] N switching failures or abnormal switching events occur, where N is a positive integer;

[0275] Leave the cell list configured by the network side device.

[0276] Optionally, the cell list is a list used to control the first data reporting area.

[0277] Optionally, at least one of the data collection condition and the model inference condition includes at least one of the following:

[0278] The location of the terminal is within a preset range;

[0279] Within a configured time window, the average speed of the terminal exceeds a first threshold or is within a first range;

[0280] Within the configured time window, the speed of the terminal continuously exceeds the second threshold value or is within the second range;

[0281] The serving cell signal quality is lower than or equal to a third threshold or is within a third range;

[0282] The signal quality of the neighboring cell is higher than or equal to the fourth threshold or is within the fourth range;

[0283] The signal quality of the neighboring cell is higher than that of the serving cell, and the difference between the signal quality of the neighboring cell and that of the serving cell is greater than or equal to the fifth threshold or is within the fifth range;

[0284] Within the configured time window, the average signal quality of the serving cell is lower than or equal to the sixth threshold or is within the sixth range;

[0285] In the configured time window, the average signal quality of the neighboring cells is higher than or equal to the seventh threshold or is within the seventh range;

[0286] In the configured time window, the average signal quality of the neighboring cell is higher than the average signal quality of the serving cell, and the difference between the average signal quality of the neighboring cell and the average signal quality of the serving cell is greater than or equal to the eighth threshold or is within the eighth range;

[0287] Within the configured time window, the signal quality of the serving cell continues to be lower than the ninth threshold or within the ninth range;

[0288] In the configured time window, the signal quality of the neighboring cell is continuously lower than the tenth threshold or in the tenth range;

[0289] Within the configured time window, the number of abnormal switching events occurring at the terminal is greater than or equal to the eleventh threshold;

[0290] Within the configured time window, the ratio of the number of abnormal switching events occurring at the terminal to the total number of switching events is greater than or equal to the twelfth threshold;

[0291] Within the configured time window, the signal quality of the terminal in the serving cell decreases by X dB;

[0292] Within the configured time window, the signal quality of the terminal in the neighboring cell is reduced by Y dB.

[0293] Optionally, the method further comprises:

[0294] The first execution module 501 is further configured to stop collecting the first data when a stop collection condition is met; wherein the stop collection condition includes at least one of the following:

[0295] The target condition is not met;

[0296] The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the duration of collecting the first data.

[0297] Optionally, the first data includes at least one of the following:

[0298] A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0299] The target information of the terminal and a timestamp corresponding to the target information, the target information including at least one of speed, position, direction and beam information;

[0300] An identifier of a second cell and a timestamp corresponding to the identifier of the second cell, where the second cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0301] The condition type of the target condition;

[0302] The number of abnormal switching events that occurred;

[0303] The ratio of the number of abnormal switching events to the total number of switching events.

[0304] Optionally, the first execution module 501 is further used to: receive second configuration information from the network side device, where the second configuration information is used to determine the applicable scope of the target model reasoning;

[0305] The second configuration information satisfies any of the following:

[0306] The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model;

[0307] The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells;

[0308] The second configuration information includes fourth information, fifth information and sixth information, or the second configuration information includes fourth information and fifth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate a cell list in the activated cell list that is applicable to the target model;

[0309] The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model;

[0310] The second configuration information is used to configure the intra-frequency cell to be applicable to the target model;

[0311] The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

[0312] Optionally, the cell list includes at least one of the following: a cell list within the access network notification area RNA; a cell list within the tracking area TA; a conditional switching CHO candidate cell; a conditional primary and secondary cell addition or modification CPAC candidate cell list; a layer one or layer two triggered mobility LTM candidate cell list.

[0313] Optionally, the first execution module 501 is further used to: receive second data from the network side device, where the second data is input data of the target model, and the second data includes at least one of the following:

[0314] Location information of the network side device;

[0315] The antenna orientation of the network side device;

[0316] The beam direction of the network side device;

[0317] The beam width of the network side device;

[0318] The identifier associated with the transmit beam of the network side device.

[0319] Optionally, the first execution module 501 is further used to: receive third configuration information from the network side device, where the third configuration information is used to configure the target model.

[0320] Reference Figure 6 , the embodiment of the present application also provides a data processing device, such as Figure 6 As shown, the data processing device 600 includes:

[0321] The second execution module 601 is configured to execute a second operation, where the second operation includes at least one of the following:

[0322] Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection;

[0323] Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data;

[0324] Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model;

[0325] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0326] Optionally, the reporting condition includes at least one of the following:

[0327] The target timer times out. When at least one target condition among the data collection conditions is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data.

[0328] The amount of the first data collected is greater than a preset threshold;

[0329] The network side device indicates that reporting is implemented based on the terminal;

[0330] Cell selection occurs;

[0331] Cell reselection occurs;

[0332] A cell handover occurs;

[0333] An RRC connection establishment occurs;

[0334] RRC reestablishment occurs;

[0335] RRC connection recovery occurs;

[0336] N switching failures or abnormal switching events occur, where N is a positive integer;

[0337] Leave the cell list configured by the network side device.

[0338] Optionally, the cell list is a list used to control the first data reporting area.

[0339] Optionally, at least one of the data collection condition and the model inference condition includes at least one of the following:

[0340] The location of the terminal is within a preset range;

[0341] Within a configured time window, the average speed of the terminal exceeds a first threshold or is within a first range;

[0342] Within the configured time window, the speed of the terminal continuously exceeds the second threshold value or is within the second range;

[0343] The serving cell signal quality is lower than or equal to a third threshold or is within a third range;

[0344] The signal quality of the neighboring cell is higher than or equal to the fourth threshold or is within the fourth range;

[0345] The signal quality of the neighboring cell is higher than that of the serving cell, and the difference between the signal quality of the neighboring cell and that of the serving cell is greater than or equal to the fifth threshold or is within the fifth range;

[0346] Within the configured time window, the average signal quality of the serving cell is lower than or equal to the sixth threshold or is within the sixth range;

[0347] In the configured time window, the average signal quality of the neighboring cells is higher than or equal to the seventh threshold or is within the seventh range;

[0348] In the configured time window, the average signal quality of the neighboring cell is higher than the average signal quality of the serving cell, and the difference between the average signal quality of the neighboring cell and the average signal quality of the serving cell is greater than or equal to the eighth threshold or is within the eighth range;

[0349] Within the configured time window, the signal quality of the serving cell continues to be lower than the ninth threshold or within the ninth range;

[0350] In the configured time window, the signal quality of the neighboring cell is continuously lower than the tenth threshold or in the tenth range;

[0351] Within the configured time window, the number of abnormal switching events occurring at the terminal is greater than or equal to the eleventh threshold;

[0352] Within the configured time window, the ratio of the number of abnormal switching events occurring at the terminal to the total number of switching events is greater than or equal to the twelfth threshold;

[0353] Within the configured time window, the signal quality of the terminal in the serving cell decreases by X dB;

[0354] Within the configured time window, the signal quality of the terminal in the neighboring cell is reduced by Y dB.

[0355] Optionally, the condition for stopping collecting the first data includes at least one of the following:

[0356] The target condition is not met;

[0357] The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data.

[0358] Optionally, the first data includes at least one of the following:

[0359] A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0360] The target information of the terminal and a timestamp corresponding to the target information, the target information including at least one of speed, position, direction and beam information;

[0361] An identifier of a second cell and a timestamp corresponding to the identifier of the second cell, where the second cell includes at least one of a serving cell, a neighboring cell, and a target cell;

[0362] The condition type of the target condition;

[0363] The number of abnormal switching events that occurred;

[0364] The ratio of the number of abnormal switching events to the total number of switching events.

[0365] Optionally, the second execution module 601 is further used to: send second configuration information to the terminal, where the second configuration information is used to determine the applicable scope of the target model reasoning;

[0366] The second configuration information satisfies any of the following:

[0367] The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model;

[0368] The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells;

[0369] The second configuration information includes fourth information, fifth information and sixth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate the cell list in the activated cell list that is suitable for the target model;

[0370] The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model;

[0371] The second configuration information is used to configure the intra-frequency cell to be applicable to the target model;

[0372] The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

[0373] Optionally, the cell list includes at least one of the following: a cell list within the access network notification area RNA; a cell list within the tracking area TA; a conditional switching CHO candidate cell; a conditional primary and secondary cell addition or modification CPAC candidate cell list; a layer one or layer two triggered mobility LTM candidate cell list.

[0374] Optionally, the second execution module 601 is further used to: send second data to the terminal, where the second data is input data of the target model, and the second data includes at least one of the following:

[0375] Location information of the network side device;

[0376] The antenna orientation of the network side device;

[0377] The beam direction of the network side device;

[0378] The beam width of the network side device;

[0379] The identifier associated with the transmit beam of the network side device.

[0380] Optionally, the second execution module 601 is further used to: send third configuration information to the terminal, where the third configuration information is used to configure the target model.

[0381] The data processing device in the embodiment of the present application may 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 a chip. The electronic device may be a terminal, or may be other devices other than a terminal. For example, the terminal may include but is not limited to the types of terminals 11 listed above, and other devices may be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0382] The data processing device provided in the embodiment of the present application can realize Figure 3 to Figure 4 The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0383] like Figure 7 As shown, an embodiment of the present application also provides a communication device 700, including a processor 701 and a memory 702, and the memory 702 stores a program or instruction that can be executed on the processor 701. When the program or instruction is executed by the processor 701, the various steps of the above-mentioned data processing method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0384] The embodiment of the present application also provides a terminal, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the following Figure 3 The steps in the method embodiment shown. This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to this terminal embodiment and can achieve the same technical effect. Specifically, Figure 8A schematic diagram of the hardware structure of a terminal for implementing an embodiment of the present application.

[0385] The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of a processor 810.

[0386] Those skilled in the art will appreciate that the terminal 800 may also include a power source (such as a battery) for supplying power to various components, and the power source may be logically connected to the processor 810 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 8 The terminal structure shown in the figure does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0387] It should be understood that in the embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 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 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 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 a joystick, which will not be repeated here.

[0388] In the embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 801 can transmit the data to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device. Generally, the radio frequency unit 801 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0389] The memory 809 can be used to store software programs or instructions and various data. The memory 809 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, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 809 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. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0390] The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 810.

[0391] The radio frequency unit 801 is configured to perform a first operation, where the first operation includes at least one of the following:

[0392] receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition;

[0393] Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition;

[0394] When the model reasoning conditions are met, model reasoning is performed based on the target model;

[0395] Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

[0396] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the terminal side method embodiment and achieve the same or corresponding technical effect. To avoid repetition, it will not be repeated here.

[0397] The embodiment of the present application also provides a network side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the following Figure 4 The steps of the method embodiment shown. This network side device embodiment corresponds to the above network side device method embodiment, and each implementation process and implementation mode of the above method embodiment can be applied to this network side device embodiment and can achieve the same technical effect.

[0398] Specifically, the embodiment of the present application also provides a network side device. Fig. 9 As shown, the network side device 900 includes: an antenna 901, a radio frequency device 902, a baseband device 903, a processor 904 and a memory 905. The antenna 901 is connected to the radio frequency device 902. In the uplink direction, the radio frequency device 902 receives information through the antenna 901 and sends the received information to the baseband device 903 for processing. In the downlink direction, the baseband device 903 processes the information to be sent and sends it to the radio frequency device 902. The radio frequency device 902 processes the received information and sends it out through the antenna 901.

[0399] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 903, which includes a baseband processor.

[0400] The baseband device 903 may include, for example, at least one baseband board on which a plurality of chips are arranged. Fig. 9 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 905 through a bus interface to call the program in the memory 905 to execute the network side device operations shown in the above method embodiment.

[0401] The network side device may further include a network interface 906, which is, for example, a Common Public Radio Interface (CPRI).

[0402] Specifically, the network side device 900 of the embodiment of the present application further includes: an instruction or program stored in the memory 905 and executable on the processor 904, and the processor 904 calls the instruction or program in the memory 905 to execute Figure 7 The methods executed by the modules shown achieve the same technical effects, and therefore will not be described here in detail to avoid repetition.

[0403] 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 data processing method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0404] 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-transient readable storage medium.

[0405] 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 data processing method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0406] 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.

[0407] The embodiments of the present application further provide a computer program / program product, which is stored in a storage medium and is executed by at least one processor to implement the various processes of the above-mentioned data processing method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0408] An embodiment of the present application also provides a wireless communication system, including: a terminal and a network side device, wherein the terminal can be used to execute the steps of the terminal side data processing method as described above, and the network side device can be used to execute the steps of the network side device data processing method as described above.

[0409] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including 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 one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment 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 reverse 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.

[0410] Through the description of the above implementation methods, 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 hardware platform, and of course, can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including several instructions to enable a terminal or a network-side device to execute the methods described in each embodiment of the present application.

[0411] 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 the present application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of the present application and the scope of protection of the claims, and these implementation methods are all within the protection of the present application.

Claims

1. A data processing method, characterized in that: include: The terminal performs a first operation, where the first operation includes at least one of the following: receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition; Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition; When the model reasoning conditions are met, model reasoning is performed based on the target model; Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

2. The method according to claim 1, characterized in that The reporting conditions include at least one of the following: The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the continuous collection time of the first data. The amount of the first data collected is greater than a preset threshold; The network side device indicates that reporting is implemented based on the terminal; Cell selection occurs; Cell reselection occurs; A cell handover occurs; An RRC connection establishment occurs; RRC reestablishment occurs; RRC connection recovery occurs; N switching failures or abnormal switching events occur, where N is a positive integer; Leave the cell list configured by the network side device.

3. The method according to claim 2, characterized in that The cell list is a list used to control the first data reporting area.

4. The method according to any one of claims 1 to 3, characterized in that: At least one of the data collection condition and the model inference condition includes at least one of the following: The location of the terminal is within a preset range; Within a configured time window, the average speed of the terminal exceeds a first threshold or is within a first range; Within the configured time window, the speed of the terminal continuously exceeds the second threshold value or is within the second range; The serving cell signal quality is lower than or equal to a third threshold or is within a third range; The signal quality of the neighboring cell is higher than or equal to the fourth threshold or is within the fourth range; The signal quality of the neighboring cell is higher than that of the serving cell, and the difference between the signal quality of the neighboring cell and that of the serving cell is greater than or equal to the fifth threshold or is within the fifth range; Within the configured time window, the average signal quality of the serving cell is lower than or equal to the sixth threshold or is within the sixth range; In the configured time window, the average signal quality of the neighboring cells is higher than or equal to the seventh threshold or is within the seventh range; In the configured time window, the average signal quality of the neighboring cell is higher than the average signal quality of the serving cell, and the difference between the average signal quality of the neighboring cell and the average signal quality of the serving cell is greater than or equal to the eighth threshold or is within the eighth range; Within the configured time window, the signal quality of the serving cell continues to be lower than the ninth threshold or within the ninth range; In the configured time window, the signal quality of the neighboring cell is continuously lower than the tenth threshold or in the tenth range; Within the configured time window, the number of abnormal switching events occurring at the terminal is greater than or equal to the eleventh threshold; Within the configured time window, the ratio of the number of abnormal switching events occurring at the terminal to the total number of switching events is greater than or equal to the twelfth threshold; Within the configured time window, the signal quality of the terminal in the serving cell decreases by X dB; Within the configured time window, the signal quality of the terminal in the neighboring cell is reduced by Y dB.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: When a stop collection condition is met, the terminal stops collecting the first data; wherein the stop collection condition includes at least one of the following: The target condition is not met; The target timer times out. When the target condition is met, the target timer is started or restarted. The running time of the target timer represents the duration of collecting the first data.

6. The method according to any one of claims 1 to 5, characterized in that: The first data includes at least one of the following: A signal quality of a first cell and a timestamp corresponding to the signal quality, wherein the first cell includes at least one of a serving cell, a neighboring cell, and a target cell; The target information of the terminal and a timestamp corresponding to the target information, the target information including at least one of speed, position, direction and beam information; An identifier of a second cell and a timestamp corresponding to the identifier of the second cell, where the second cell includes at least one of a serving cell, a neighboring cell, and a target cell; The condition type of the target condition; The number of abnormal switching events that occurred; The ratio of the number of abnormal switching events to the total number of switching events.

7. The method according to any one of claims 1 to 6, characterized in that: When the model reasoning condition is met, before performing model reasoning based on the target model, the method further includes: The terminal receives second configuration information from the network side device, where the second configuration information is used to determine the applicable scope of the target model reasoning; The second configuration information satisfies any of the following: The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model; The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells; The second configuration information includes fourth information, fifth information and sixth information, or the second configuration information includes fourth information and fifth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate a cell list in the activated cell list that is applicable to the target model; The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model; The second configuration information is used to configure the intra-frequency cell to be applicable to the target model; The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

8. The method according to claim 2 or 7, characterized in that: The cell list includes at least one of the following: a cell list within the access network notification area RNA; a cell list within the tracking area TA; a conditional switching CHO candidate cell; a conditional primary and secondary cell addition or modification CPAC candidate cell list; a layer one or layer two triggered mobility LTM candidate cell list.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: The terminal receives second data from the network-side device, where the second data is input data of the target model, and the second data includes at least one of the following: Location information of the network side device; The antenna orientation of the network side device; The beam direction of the network side device; The beam width of the network side device; The identifier associated with the transmit beam of the network side device.

10. The method according to any one of claims 1 to 9, characterized in that: After reporting the first data collected by the terminal to the network side device according to the reporting condition, the method further includes: The terminal receives third configuration information from the network side device, where the third configuration information is used to configure the target model.

11. A data processing method, characterized in that: include: The network-side device performs a second operation, where the second operation includes at least one of the following: Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection; Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data; Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model; Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

12. The method according to claim 11, characterized in that The method further comprises: The network side device sends second configuration information to the terminal, where the second configuration information is used to determine the applicable scope of the target model reasoning; The second configuration information satisfies any of the following: The second configuration information is used to configure the frequency point, cell or cell list applicable to the target model; The second configuration information includes first information, second information and third information, or the second configuration information includes first information and second information, wherein the first information is used to configure multiple cells, the second information is used to activate at least some of the multiple cells, and the third information is used to indicate the cells suitable for the target model among the activated cells; The second configuration information includes fourth information, fifth information and sixth information, the fourth information is used to configure multiple cell lists, the fifth information is used to activate at least part of the cell lists in the multiple cell lists, and the sixth information is used to indicate the cell list in the activated cell list that is suitable for the target model; The second configuration information is used to configure the serving cell and at least one of the first M neighboring cells whose predicted signal qualities are ranked from high to low to be applicable to the target model; The second configuration information is used to configure the intra-frequency cell to be applicable to the target model; The second configuration information is used to configure the first P neighboring cells in the same-frequency cell and the different-frequency cell, whose signal quality is ranked from high to low, to be applicable to the target model, where P is a positive integer.

13. The method according to claim 11 or 12, characterized in that: The method further comprises: The network side device sends second data to the terminal, where the second data is input data of the target model, and the second data includes at least one of the following: Location information of the network side device; The antenna orientation of the network side device; The beam direction of the network side device; The beam width of the network side device; The identifier associated with the transmit beam of the network side device.

14. The method according to any one of claims 11 to 13, characterized in that: After sending the reporting condition to the terminal, the method further includes: The network side device sends third configuration information to the terminal, where the third configuration information is used to configure the target model.

15. A data processing device, characterized in that: include: The first execution module is configured to execute a first operation, wherein the first operation includes at least one of the following: receiving first configuration information from a network side device, and collecting first data when at least one target condition in the data collection condition is satisfied, wherein the first configuration information is used to configure the data collection condition; Receiving a reporting condition from a network side device, and reporting the first data collected by the terminal to the network side device according to the reporting condition; When the model reasoning conditions are met, model reasoning is performed based on the target model; Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

16. A data processing device, characterized in that: include: The second execution module is configured to execute a second operation, where the second operation includes at least one of the following: Sending first configuration information to the terminal, where the first configuration information is used to configure a data collection condition for performing first data collection; Sending a reporting condition to the terminal, where the reporting condition is used to trigger the terminal to report the collected first data; Sending a model inference condition to a terminal, wherein the model inference condition is used to trigger the terminal to perform model inference based on a target model; Among them, the first data is used to train the artificial intelligence AI model to obtain the target model.

17. A terminal, characterized in that: The method comprises 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 data processing method according to any one of claims 1 to 10 are implemented.

18. A network side device, characterized in that: The method comprises 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 data processing method according to any one of claims 11 to 14 are implemented.

19. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the data processing method according to any one of claims 1 to 14 are implemented.

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