Information transmission method and device, terminal and network side equipment

Abstract

The invention discloses an information transmission method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the information transmission method comprises the steps that the terminal obtains an artificial intelligence (AI) model and a first identifier associated with the AI model, and the first identifier is associated with data collection or a data set; and the terminal sends a target request to a network side device, the target request comprising the first identifier, and the target request being used for requesting model identification of the AI model.

Description

Technical Field

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

[0002] Currently, there is no direct correlation between data set collection and artificial intelligence (AI) model registration and AI model identification. The user equipment (UE) side needs to report a large amount of model description information to the network side device side so that the network side device knows the applicable scope and applicable scenarios of the registered model. However, the reporting of a large amount of model description information leads to an increase in the signaling overhead of the communication system. Summary of the invention

[0003] The embodiments of the present application provide an information transmission method, apparatus, terminal and network-side equipment, which can solve the problem of increasing the signaling overhead of the communication system.

[0004] In a first aspect, an information transmission method is provided, comprising:

[0005] The terminal obtains an artificial intelligence AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set;

[0006] The terminal sends a target request to the network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0007] In a second aspect, an information transmission method is provided, comprising:

[0008] The network side device receives a target request sent by the terminal, where the target request is used to request model recognition of an artificial intelligence AI model, and the target request includes a first identifier, where the first identifier is associated with a data collection or data set;

[0009] The network side device responds to the target request and performs model identification of the AI ​​model based on the first identifier.

[0010] In a third aspect, an information transmission device is provided, comprising:

[0011] An acquisition module, used to acquire an artificial intelligence AI model and a first identifier associated with the AI ​​model, wherein the first identifier is associated with a data collection or data set;

[0012] The first sending module is used to send a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0013] In a fourth aspect, an information transmission device is provided.

[0014] A first receiving module, configured to receive a target request sent by a terminal, wherein the target request is used to request model recognition of an artificial intelligence AI model, and the target request includes a first identifier, and the first identifier is associated with a data collection or data set;

[0015] A model identification module is used to perform model identification of the AI ​​model based on the first identifier in response to the target request.

[0016] 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.

[0017] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the processor is used to obtain an artificial intelligence AI model and a first identifier associated with the AI ​​model, wherein the first identifier is associated with a data collection or data set; the communication interface is used to send a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0018] In a 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 first aspect are implemented.

[0019] In an eighth aspect, a network side device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive a target request sent by a terminal, the target request is used to request model identification of an artificial intelligence (AI) model, and the target request includes a first identifier, and the first identifier is associated with a data collection or data set; the processor is used to respond to the target request and perform model identification of the AI ​​model based on the first identifier.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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 steps of the information transmission method as described in the first aspect, or to implement the steps of the information transmission method as described in the second aspect.

[0024] In an embodiment of the present application, an AI model and a first identifier associated with the AI ​​model are obtained through the terminal. Since the first identifier is associated with a data collection or data set, when the terminal requests a network side device to perform model recognition of the AI ​​model, the terminal only needs to send the first identifier to the network side device. There is no need to report a large amount of model description information to the network side device. The network side device can perform model recognition of the AI ​​model according to the first identifier, thereby reducing the signaling overhead of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic diagram of a wireless communication system to which an embodiment of the present application can be applied;

[0026] Figure 2 It is a schematic diagram of the structure of a neural network provided by the prior art;

[0027] Figure 3 It is a schematic diagram of the structure of a neuron provided by the prior art;

[0028] Figure 4 This is one of the flow charts of the information transmission method provided in the embodiment of the present application;

[0029] Figure 5 This is the second flow chart of the information transmission method provided in the embodiment of the present application;

[0030] Figure 6 This is one of the schematic diagrams of interaction between a terminal and a network side device provided in an embodiment of the present application;

[0031] Figure 7 This is the second schematic diagram of interaction between a terminal and a network device provided in an embodiment of the present application;

[0032] Figure 8 This is the third schematic diagram of interaction between a terminal and a network device provided in an embodiment of the present application;

[0033] Fig. 9 It is one of the structural schematic diagrams of the information transmission device provided in the embodiment of the present application;

[0034] Fig.10 This is the second structural diagram of the information transmission device provided in the embodiment of the present application;

[0035] Fig.11 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application;

[0036] Fig.12 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0037] Fig.13 It is a structural diagram of the network side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of this application.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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, a Transmission Reception Point (TRP) or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments 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.

[0043] 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.

[0044] In order to facilitate a clearer understanding of the various embodiments of the present application, some relevant background technical knowledge is first introduced as follows.

[0045] 1. Artificial Intelligence (AI)

[0046] 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 is an important task for future wireless communication networks. There are many ways to implement AI modules, such as neural networks, decision trees, support vector machines, and Bayesian classifiers.

[0047] Figure 2 It is a schematic diagram of the structure of a neural network provided by the prior art, such as Figure 2 As shown, the neural network includes an input layer, a hidden layer, and an output layer, where X1, X2, ..., X n represents n inputs, Y represents outputs, and the neural network is composed of neurons. Figure 3 It is a schematic diagram of the structure of neurons provided by the prior art, such as Figure 3 As shown, a1, a2, …, a K is the input, K is the total number of inputs, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), σ() is the activation function, and a represents the output of the neuron. Common activation functions include the sigmoid function, the hyperbolic tangent function (tanh), and the rectified linear unit (ReLU); among them, the rectified linear unit is also called the rectified linear unit.

[0048] The neuron is represented by formula (1), where:

[0049] z=a1w1+…+a k w k +a K w K +b (1)

[0050] Among them, a k represents the kth input.

[0051] The parameters of the neural network are optimized using a gradient optimization algorithm. The gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (also called a loss function), and the objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f() is constructed. With the model, the predicted output f(X) can be obtained based on the input X, and the difference between the predicted value and the true value (f(X)-Y) can be calculated. This is the loss function. The purpose is to find the appropriate w and b to minimize the value of the above loss function. The smaller the loss value, the closer the model is to the actual situation.

[0052] 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.

[0053] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), stochastic gradient descent with momentum (Nesterov), adaptive gradient descent (Adagrad), adaptive learning rate (Adadelta), root mean square error speed reduction (Root Mean Square prop, RMSprop), adaptive momentum estimation (Adaptive Moment Estimation, Adam), etc.

[0054] 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 from the loss function, add the influence of the learning rate, previous gradient, derivative or partial derivative, etc., get the gradient, and pass the gradient to the previous layer.

[0055] 2. AI Unit or AI Model

[0056] The AI ​​model may also be referred to as an AI unit, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, and a neural network function, or the AI ​​unit or AI model may also refer to a processing unit capable of implementing specific algorithms, formulas, processing procedures, and capabilities related to AI, or the AI ​​unit or AI model may be a processing method, algorithm, function, module, or unit for a specific data set, or the AI ​​unit or AI model may be a processing method, algorithm, function, module, or unit running on AI or ML related hardware such as a graphics processor (GPU), an embedded neural network processor (NPU), a tensor processing unit (TPU), or an application specific integrated circuit (ASIC). The specific data set includes at least one of the input and output of the AI ​​unit or AI model.

[0057] The identifier of the AI ​​unit or AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit or AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI ​​or ML, or an identifier of a function, feature, capability, or module related to the AI ​​or ML.

[0058] The information transmission 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.

[0059] The information transmission method provided in the embodiment of the present application can be applied to the scenario of model identification. The AI ​​model and the first identifier associated with the AI ​​model are obtained through the terminal. Since the first identifier is associated with the data collection or data set, when the terminal requests the network side device to perform model identification of the AI ​​model, the terminal only needs to send the first identifier to the network side device. There is no need to report a large amount of model description information to the network side device. The network side device can perform model identification of the AI ​​model according to the first identifier, thereby reducing the signaling overhead of the communication system.

[0060] Figure 4 is one of the flow charts of the information transmission method provided in the embodiment of the present application, such as Figure 4 As shown, the method includes steps 401 and 402; wherein:

[0061] Step 401: The terminal obtains an artificial intelligence AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set.

[0062] It should be noted that the embodiments of the present application can be applied to model recognition scenarios, and the terminals include but are not limited to the types of terminals 11 listed above, and the network side devices include but are not limited to the types of network side devices 12 listed above, and the embodiments of the present application do not limit this.

[0063] Specifically, the AI ​​model can be trained by the terminal, network-side equipment, network-side server, terminal server, core network server or third-party server using a data set. The network side device triggers data collection datacollection, and the network side device can determine the resource configuration for data collection, and the resource configuration is associated, indicated or carries a first identifier; wherein the resource configuration includes at least one of the following: measurement configuration (MeasConfig) information, such as measurement object (MeasObject), channel state information-measurement configuration (Channel State Information-MeasConfig, CSI-MeasConfig); reporting configuration (ReportConfig) information, such as CSI report configuration (CSI-ReportConfig); resource configuration information of reference signal (Reference Signal, RS) used for data collection, such as CSI resource configuration (CSI-ResourceConfig), demodulation reference signal-downlink configuration (Demodulation Reference Signal-DownlinkConfig, DMRS-DownlinkConfig), DMRS-uplink configuration (DMRS-UplinkConfig), CSI-synchronization signal block (Synchronization Signal Block, SSB) resource (Resource), CSI-interference measurement (Interference Measurement, IM)-Resource, non-zero power (Non-Zero Power, NZP)-CSI-RS-Resource, zero power (Zero The network side device configures a first identifier when configuring resources for data collection.

[0064] Optionally, when the terminal trains the AI ​​model, the network side device collects a data set according to the configured resources for data collection, and sends the data set to the terminal, so that the terminal trains the AI ​​model according to the data set, so that the terminal obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or the data set; or, the network side device configures resources for data collection to the terminal, the terminal collects a data set according to the configured resources for data collection, and uses the collected data set to train the AI ​​model, so that the terminal obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or the data set.

[0065] Optionally, when the network side device trains the AI ​​model, the network side device collects a data set according to the configured resources for data collection, and uses the collected data set to train the AI ​​model, so that the network side device obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set. The network side device then sends the AI ​​model and the first identifier associated with the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set.

[0066] Optionally, when the network-side server, terminal server, core network server or third-party server trains the AI ​​model, the network-side device collects a data set according to the configured resources for data collection, and sends the data set to the network-side server, terminal server, core network server or third-party server. The network-side server, terminal server, core network server or third-party server uses the data set to train the AI ​​model, and the AI ​​model will correspond to a first identifier, which is associated with the data collection or data set. The network-side server, terminal server, core network server or third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set; or, the network-side device configures resources for data collection to the network-side server, terminal server, core network server or third-party server, and the network-side server, terminal server, core network server or third-party server collects data according to the configured resources for data collection. set, and uses the collected data set to train the AI ​​model, the AI ​​model will correspond to a first identifier, the first identifier is associated with the data collection or data set, and the third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set; or, the network-side device configures resources for data collection to the terminal, the terminal collects a data set according to the configured resources for data collection, and sends the data set to the network-side server, terminal server, core network server or third-party server, the network-side server, terminal server, core network server or third-party server uses the data set to train the AI ​​model, then the AI ​​model will correspond to a first identifier, the first identifier is associated with the data collection or data set, the network-side server, terminal server, core network server or third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set.

[0067] After the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, it can register the model based on the first identifier to obtain a model identifier (model ID), wherein the model identifier includes a local model identifier (local model ID) or a global model identifier (global model ID), and the model identifier is associated with the first identifier. For example, the global model identifier is globally unique, and AI models with different structures, parameters, types, functions, manufacturers, sellers, and developers have different model identifiers; the local model identifier is not globally unique, and AI models with different structures, parameters, types, functions, manufacturers, sellers, and developers can have the same model identifier.

[0068] Optionally, the first identifier includes first information or coded information of the first information; the first information includes at least one of the following:

[0069] (1) Dataset information or dataset identifier (Identifier, ID);

[0070] (2) Dataset categorizing information or dataset categorizing ID;

[0071] (3) Additional conditions information or additional conditions ID;

[0072] (4) Applicable conditions information or applicable conditions ID;

[0073] (5) applicable scenarios information or applicable scenario ID;

[0074] (6) internal conditions information or internal conditions ID;

[0075] (7) Cell information or cell ID;

[0076] (8) Area information or area ID;

[0077] (9) Scenario information or scenario ID;

[0078] (10) Hardware configuration information or hardware configuration ID;

[0079] (11) Antenna configuration information or antenna configuration ID;

[0080] (12) wireless resource configuration information or wireless resource configuration ID;

[0081] (13) wireless environment information or wireless environment ID;

[0082] (14) wireless environment characteristic information or wireless environment characteristic ID;

[0083] (15) Radio Access Network (RAN) test configuration information or RAN test configuration ID;

[0084] (16) Wireless test configuration information or wireless test configuration ID.

[0085] Specifically, the dataset ID or dataset categorizing ID indicates wireless environment information such as hardware configuration, antenna configuration, wireless resource configuration, wireless environment, wireless environment characteristics, RAN test configuration, wireless test configuration, cell information, area information and scenario information, or indicates a dataset generated based on the wireless environment information. For example, RAN test may refer to RAN4 test.

[0086] For example, the dataset ID indicates some wireless environment information (or side conditions) or test configuration information used in the RAN test or wireless test configuration, and the wireless environment information or test configuration information includes channel models (e.g., clustered delay line model (CDL), tapped delay line model (TDL), urban macro base station (UMa), urban micro base station (UMi), indoor coverage scenario (Indoor), high-speed rail, etc.), channel configuration parameters (e.g., delay, cell radius, base station power, user equipment (UE) power, noise power, UE moving speed, base station antenna configuration, UE antenna configuration, etc.). These wireless environment information or test configuration information are agreed upon in advance by the protocol, and there is no real wireless air interface data collection, but each manufacturer generates relevant data sets by itself through simulation platforms or other methods.

[0087] Step 402: The terminal sends a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0088] Specifically, after the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, the terminal can send a target request to the network side device, the target request includes the first identifier, and the target request is used to request the network side device to perform model identification of the AI ​​model. Among them, model identification includes at least one of the following: model registration, model authentication, model information reporting, and model information confirmation.

[0089] In the information transmission method provided in the embodiment of the present application, the AI ​​model and the first identifier associated with the AI ​​model are obtained through the terminal. Since the first identifier is associated with the data collection or data set, when the terminal requests the network side device to perform model recognition of the AI ​​model, the terminal only needs to send the first identifier to the network side device. There is no need to report a large amount of model description information to the network side device. The network side device can perform model recognition of the AI ​​model based on the first identifier, thereby reducing the signaling overhead of the communication system.

[0090] Optionally, the first identifier is associated with the AI ​​model or with at least part of the model description information of the AI ​​model.

[0091] Specifically, after training the AI ​​model, the trainer of the AI ​​model will obtain at least part of the model description information of the AI ​​model. Therefore, the first identifier is associated with the AI ​​model or at least part of the model description information of the associated AI model.

[0092] Optionally, the target request may also include: at least part of the model description information of the AI ​​model.

[0093] Specifically, at least part of the model description information of the AI ​​model can be reported to the network side device together with the first identifier, that is, the terminal only needs to report at least part of the model description information of the AI ​​model, and does not need to report a large amount of model description information of the AI ​​model, thereby reducing the signaling overhead of the communication system.

[0094] Optionally, at least part of the model description information of the AI ​​model includes at least one of the following:

[0095] (a) Functionality of AI models;

[0096] (b) the feature or feature group that the AI ​​model can be used for;

[0097] (c) Applicability scenarios of AI models;

[0098] (d) Applicability configurations of AI models;

[0099] (e) input information of the AI ​​model or the format of the input information;

[0100] (f) output information of the AI ​​model or the format of the output information;

[0101] (g) Auxiliary information of the AI ​​model, used to assist the AI ​​model in reasoning.

[0102] Optionally, the auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; wireless environment characteristic information.

[0103] Specifically, the auxiliary information can be directly used by the AI ​​model, or it can be converted into certain identification or feature information to be implicitly used by the AI ​​model.

[0104] Optionally, the method further comprises at least one of the following:

[0105] 1) The terminal performs lifecycle management of the AI ​​model based on the first identifier.

[0106] Specifically, after obtaining the AI ​​model and the first identifier associated with the AI ​​model, the terminal can directly perform lifecycle management of the AI ​​model based on the first identifier, thereby reducing the complexity of lifecycle management of the AI ​​model and improving the flexibility of the communication system.

[0107] Optionally, the subject of the lifecycle management of the AI ​​model can be at least one of the terminal and the network side device; for example, the terminal is the subject of the lifecycle management of the AI ​​model, or the network side device is the subject of the lifecycle management of the AI ​​model, or the terminal and the network side device perform the lifecycle management of the AI ​​model together.

[0108] Optionally, the lifecycle management of the AI ​​model includes at least one of the following:

[0109] a) Activation or deactivation of AI models.

[0110] Specifically, the activation or deactivation of the AI ​​model can be performed through the instruction of the network side device, or through the terminal's own decision (the result of the activation or deactivation of the AI ​​model may be fed back to the network side device), or the terminal evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision. For example, the terminal includes multiple AI models, and the network side device indicates the first identifier of a certain AI model, and the terminal selects the AI ​​model corresponding to the first identifier indicated by the network side device for activation and use; or, the terminal activates or deactivates an AI model by itself, and the terminal feeds back the second identifier of the AI ​​model to the network side device; or, the terminal evaluates that an AI model needs to be activated or deactivated, and the terminal feeds back the second identifier of the AI ​​model to the network side device, and the network side device makes the final decision.

[0111] b) Selection or switching of AI models.

[0112] Specifically, the selection or switching of the AI ​​model can be performed through the instruction of the network side device, or through the terminal's own decision (the result of the selection or switching of the AI ​​model may be fed back to the network side device), or the terminal evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision. For example, the terminal includes multiple AI models, and the network side device instructs the terminal to switch from the first identifier of the currently used AI model to the first identifier of a certain AI model. The terminal can switch from the currently used AI model to the AI ​​model indicated by the network side device according to the instruction of the network side device; or, the terminal selects or switches a certain AI model by itself, and the terminal feeds back the second identifier of the AI ​​model to the network side device; or, the terminal evaluates that it needs to select or switch a certain AI model, and the terminal feeds back the second identifier of the AI ​​model to the network side device, and the network side device makes the final decision.

[0113] c) Reasoning of AI models.

[0114] d) Performance monitoring or performance management of AI models.

[0115] Specifically, the terminal and the network-side device can jointly implement the performance monitoring or performance management of the AI ​​model, or the terminal is solely responsible for the performance monitoring or performance management of the AI ​​model, or the network-side device is solely responsible for the performance monitoring or performance management of the AI ​​model. For example, the terminal can monitor certain performance indicators of the AI ​​model, and the terminal sends the monitored performance indicators to the network-side device, so that the network-side device can perform performance management on the performance of the AI ​​model; or the terminal can monitor certain performance indicators of the AI ​​model by itself, so that the terminal can perform performance management on the performance of the AI ​​model; or the network-side device can monitor certain performance indicators of the AI ​​model by itself, so that the network-side device can perform performance management on the performance of the AI ​​model.

[0116] e) Fall back to non-AI model.

[0117] Specifically, during the process of the terminal using the AI ​​model, the terminal can roll back the usage status of the AI ​​model to the usage status of the non-AI model, or the network side device instructs the terminal to roll back the usage status of the AI ​​model to the usage status of the non-AI model, or the terminal evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision whether to roll back the usage status of the AI ​​model to the usage status of the non-AI model.

[0118] f) Training or updating of AI models.

[0119] Specifically, when using the AI ​​model, the terminal can train or update the AI ​​model to obtain a new AI model, or the network side device can train or update the AI ​​model to obtain a new AI model, or the network side server, terminal server, core network server or third-party server can train or update the AI ​​model and then send the updated model to the terminal or network side device.

[0120] g) Registration of AI models.

[0121] Specifically, when the terminal obtains the AI ​​model, it can register the AI ​​model, or the network side device can register the AI ​​model.

[0122] h)Configuration of AI model.

[0123] Specifically, when the terminal obtains the AI ​​model, the AI ​​model can be configured, or the network side device can configure the AI ​​model.

[0124] i) Identity confirmation of AI models.

[0125] Specifically, when the terminal obtains the AI ​​model, it can initiate identity confirmation of the AI ​​model to the network side device; or when the network side device obtains the AI ​​model, it can initiate identity confirmation of the AI ​​model to the terminal.

[0126] 2) The terminal receives a second identifier sent by the network side device, where the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and the terminal performs life cycle management of the AI ​​model based on the second identifier.

[0127] Specifically, after the terminal sends a target request to the network side, since the target request may include a first identifier and at least part of the model description information of the AI ​​model, the network side device can determine a second identifier for the AI ​​model based on the first identifier reported by the terminal and at least part of the model description information of the AI ​​model, and the second identifier is used to identify the AI ​​model, and the second identifier is associated with the first identifier; the network side device then sends the second identifier to the terminal, and the terminal receives the second identifier sent by the network side device. After receiving the second identifier, the terminal can perform lifecycle management of the AI ​​model based on the second identifier, reduce the complexity of lifecycle management of the AI ​​model, and improve the flexibility of the communication system.

[0128] Optionally, the subject of the lifecycle management of the AI ​​model can be at least one of the terminal and the network side device; for example, the terminal is the subject of the lifecycle management of the AI ​​model, or the network side device is the subject of the lifecycle management of the AI ​​model, or the terminal and the network side device perform the lifecycle management of the AI ​​model together.

[0129] Figure 5This is a second flow chart of the information transmission method provided in the embodiment of the present application, such as Figure 5 As shown, the method includes steps 501 and 502; wherein:

[0130] Step 501: A network-side device receives a target request sent by a terminal, where the target request is used to request model recognition of an artificial intelligence (AI) model. The target request includes a first identifier, and the first identifier is associated with a data collection or data set.

[0131] Specifically, the AI ​​model can be trained by the terminal, network side device, network side server, terminal server, core network server or third-party server using the data set. The network side device can trigger data collection data collection.

[0132] Optionally, after triggering data collection, the network side device can determine the resource configuration for data collection, and the resource configuration is associated, indicated or carries the first identifier; wherein the resource configuration includes at least one of the following: measurement configuration (MeasConfig) information, such as measurement object (MeasObject), channel state information-measurement configuration (Channel StateInformation-MeasConfig, CSI-MeasConfig); reporting configuration (ReportConfig) information, such as CSI report configuration (CSI-ReportConfig); resource configuration information of reference signal (Reference Signal, RS) used for data collection, such as CSI resource configuration (CSI-ResourceConfig), demodulation reference signal-downlink configuration (DemodulationReference Signal-DownlinkConfig, DMRS-DownlinkConfig), DMRS-uplink configuration (DMRS-UplinkConfig), CSI-Synchronization Signal Block (Synchronization Signal Block, SSB) resource (Resource), CSI-Interference Measurement (Interference Measurement, IM)-Resource, non-zero power (Non-Zero The network side device may include: a first identifier, a first resource set configuration information ...

[0133] Optionally, when the terminal trains the AI ​​model, the network side device collects a data set according to the configured resources for data collection, and sends the data set to the terminal, so that the terminal trains the AI ​​model according to the data set, so that the terminal obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or the data set; or, the network side device configures resources for data collection to the terminal, and the terminal collects a data set according to the configured resources for data collection, and uses the collected data set to train the AI ​​model, so that the terminal obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or the data set.

[0134] Optionally, when the network side device trains the AI ​​model, the network side device collects a data set according to the configured resources for data collection, and uses the collected data set to train the AI ​​model, so that the network side device obtains the AI ​​model and a first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set. The network side device then sends the AI ​​model and the first identifier associated with the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set.

[0135] Optionally, when the network-side server, terminal server, core network server or third-party server trains the AI ​​model, the network-side device collects a data set according to the configured resources for data collection, and sends the data set to the network-side server, terminal server, core network server or third-party server. The network-side server, terminal server, core network server or third-party server uses the data set to train the AI ​​model, and the AI ​​model will correspond to a first identifier, which is associated with the data collection or data set. The network-side server, terminal server, core network server or third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set; or, the network-side device configures resources for data collection to the network-side server, terminal server, core network server or third-party server, and the network-side server, terminal server, core network server or third-party server collects data according to the configured resources for data collection. set, and uses the collected data set to train the AI ​​model, the AI ​​model will correspond to a first identifier, the first identifier is associated with the data collection or data set, and the third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set; or, the network-side device configures resources for data collection to the terminal, the terminal collects a data set according to the configured resources for data collection, and sends the data set to the network-side server, terminal server, core network server or third-party server, the network-side server, terminal server, core network server or third-party server uses the data set to train the AI ​​model, then the AI ​​model will correspond to a first identifier, the first identifier is associated with the data collection or data set, the network-side server, terminal server, core network server or third-party server then sends the AI ​​model to the terminal, so that the terminal obtains the AI ​​model and the first identifier associated with the AI ​​model, and the first identifier is associated with the data collection or data set.

[0136] After obtaining the AI ​​model and the first identifier associated with the AI ​​model, the terminal can register the model based on the first identifier to obtain a model identifier (model ID), wherein the model identifier includes a local model identifier (local model ID) or a global model identifier (global model ID), and the model identifier is associated with the first identifier. For example, the global model identifier is globally unique, and AI models with different structures, parameters, types, functions, manufacturers, sellers, and developers have different model identifiers; the local model identifier is not globally unique, and AI models with different structures, parameters, types, functions, manufacturers, sellers, and developers can have the same model identifier.

[0137] In practice, the terminal can send a target request to the network side device, and the network side device receives the target request sent by the terminal. The target request is used to request model recognition of the AI ​​model. The target request includes a first identifier, and the first identifier is associated with the data collection or data set.

[0138] Optionally, the first identifier includes first information or coded information of the first information; the first information includes at least one of the following:

[0139] (1) Dataset information or dataset identifier (Identifier, ID);

[0140] (2) dataset categorizing information or dataset feature ID;

[0141] (3) Additional conditions information or additional conditions ID;

[0142] (4) Applicable conditions information or applicable conditions ID;

[0143] (5) applicable scenarios information or applicable scenario ID;

[0144] (6) internal conditions information or internal conditions ID;

[0145] (7) Cell information or cell ID;

[0146] (8) Area information or area ID;

[0147] (9) Scenario information or scenario ID;

[0148] (10) Hardware configuration information or hardware configuration ID;

[0149] (11) Antenna configuration information or antenna configuration ID;

[0150] (12) wireless resource configuration information or wireless resource configuration ID;

[0151] (13) wireless environment information or wireless environment ID;

[0152] (14) wireless environment characteristic information or wireless environment characteristic ID;

[0153] (15) Radio Access Network (RAN) test configuration information or RAN test configuration ID;

[0154] (16) Wireless test configuration information or wireless test configuration ID.

[0155] Specifically, the dataset ID or dataset categorizing ID indicates wireless environment information such as hardware configuration, antenna configuration, wireless resource configuration, wireless environment, wireless environment characteristics, RAN test configuration, wireless test configuration, cell information, area information, and scenario information, or indicates a dataset generated based on the wireless environment information. For example, RAN test refers to RAN4 test.

[0156] For example, the dataset ID indicates some wireless environment information (or side conditions) or test configuration information used in the RAN test or wireless test configuration, and the wireless environment information or test configuration information includes channel models (e.g., clustered delay line model (CDL), tapped delay line model (TDL), urban macro base station (UMa), urban micro base station (UMi), indoor coverage scenario (Indoor), high-speed rail, etc.), channel configuration parameters (e.g., delay, cell radius, base station power, user equipment (UE) power, noise power, UE moving speed, base station antenna configuration, UE antenna configuration, etc.). These wireless environment information or test configuration information are agreed upon in advance by the protocol, and there is no real wireless air interface data collection, but each manufacturer generates relevant data sets by itself through simulation platforms or other methods.

[0157] Step 502: The network side device responds to the target request and performs model identification of the AI ​​model based on the first identifier.

[0158] Specifically, after receiving the target request sent by the terminal, the network side device responds to the target request. Since the target request includes the first identifier, the network side device can perform model identification of the AI ​​model based on the first identifier. Among them, model identification includes at least one of the following: model registration, model authentication, model information reporting, and model information confirmation.

[0159] In the information transmission method provided in the embodiment of the present application, a target request sent by a terminal is received by a network side device, and the target request is used to request model recognition of an artificial intelligence AI model. Since the target request includes a first identifier, and the first identifier is associated with a data collection or data set, the network side device only needs to obtain the first identifier sent by the terminal without obtaining a large amount of model description information, and can perform model recognition of the AI ​​model based on the first identifier, thereby reducing the signaling overhead of the communication system.

[0160] Optionally, the first identifier is associated with the AI ​​model or with at least part of the model description information of the AI ​​model.

[0161] Specifically, after training the AI ​​model, the trainer of the AI ​​model will obtain at least part of the model description information of the AI ​​model. Therefore, the first identifier is associated with the AI ​​model or at least part of the model description information of the associated AI model.

[0162] Optionally, the target request also includes: at least partial model description information of the AI ​​model.

[0163] Specifically, at least part of the model description information of the AI ​​model can be reported to the network side device together with the first identifier, that is, the terminal only needs to report at least part of the model description information of the AI ​​model, and does not need to report a large amount of model description information of the AI ​​model, thereby reducing the signaling overhead of the communication system.

[0164] Optionally, at least part of the model description information of the AI ​​model includes at least one of the following:

[0165] (a) Functionality of AI models;

[0166] (b) the feature or feature group that the AI ​​model can be used for;

[0167] (c) Applicability scenarios of AI models;

[0168] (d) Applicability configurations of AI models;

[0169] (e) input information of the AI ​​model or the format of the input information;

[0170] (f) output information of the AI ​​model or the format of the output information;

[0171] (g) Auxiliary information of the AI ​​model, used to assist the AI ​​model in reasoning.

[0172] Optionally, the auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; wireless environment characteristic information.

[0173] Specifically, the auxiliary information can be directly used by the AI ​​model, or it can be converted into certain identification or feature information to be implicitly used by the AI ​​model.

[0174] Optionally, the method further comprises at least one of the following:

[0175] 1) The network-side device performs lifecycle management of the AI ​​model based on the first identifier.

[0176] Specifically, after obtaining the first identifier, the network side device can directly perform lifecycle management of the AI ​​model based on the first identifier, thereby reducing the complexity of lifecycle management of the AI ​​model and improving the flexibility of the communication system.

[0177] Optionally, the subject of the lifecycle management of the AI ​​model can be at least one of the terminal and the network side device; for example, the terminal is the subject of the lifecycle management of the AI ​​model, or the network side device is the subject of the lifecycle management of the AI ​​model, or the terminal and the network side device perform the lifecycle management of the AI ​​model together.

[0178] Optionally, the lifecycle management of the AI ​​model includes at least one of the following:

[0179] a) Activation or deactivation of AI models.

[0180] Specifically, the activation or deactivation of the AI ​​model can be performed through the instruction of the network side device, or through the terminal's own decision (the result of the activation or deactivation of the AI ​​model may be fed back to the network side device), or the terminal evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision. For example, the terminal includes multiple AI models, and the network side device indicates the first identifier of a certain AI model, and the terminal selects the AI ​​model corresponding to the first identifier indicated by the network side device for activation and use; or, the terminal activates or deactivates an AI model by itself, and the terminal feeds back the second identifier of the AI ​​model to the network side device; or, the terminal evaluates that an AI model needs to be activated or deactivated, and the terminal feeds back the second identifier of the AI ​​model to the network side device, and the network side device makes the final decision.

[0181] b) Selection or switching of AI models.

[0182] Specifically, the selection or switching of the AI ​​model can be performed through the instruction of the network side device, or through the terminal's own decision (the result of the selection or switching of the AI ​​model may be fed back to the network side device), or the terminal evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision. For example, the terminal includes multiple AI models, and the network side device instructs the terminal to switch from the first identifier of the currently used AI model to the first identifier of a certain AI model. The terminal can switch from the currently used AI model to the AI ​​model indicated by the network side device according to the instruction of the network side device; or, the terminal selects or switches a certain AI model by itself, and the terminal feeds back the second identifier of the AI ​​model to the network side device; or, the terminal evaluates that it needs to select or switch a certain AI model, and the terminal feeds back the second identifier of the AI ​​model to the network side device, and the network side device makes the final decision.

[0183] c) Reasoning of AI models.

[0184] d) Performance monitoring or performance management of AI models.

[0185] Specifically, the terminal and the network-side device can jointly implement the performance monitoring or performance management of the AI ​​model, or the terminal is solely responsible for the performance monitoring or performance management of the AI ​​model, or the network-side device is solely responsible for the performance monitoring or performance management of the AI ​​model. For example, the terminal can monitor certain performance indicators of the AI ​​model, and the terminal sends the monitored performance indicators to the network-side device, so that the network-side device can perform performance management on the performance of the AI ​​model; or the terminal can monitor certain performance indicators of the AI ​​model by itself, so that the terminal can perform performance management on the performance of the AI ​​model; or the network-side device can monitor certain performance indicators of the AI ​​model by itself, so that the network-side device can perform performance management on the performance of the AI ​​model.

[0186] e) Fall back to non-AI model.

[0187] Specifically, during the process of the terminal using the AI ​​model, the terminal can roll back the usage status of the AI ​​model to the usage status of the non-AI model, or the network side device instructs the terminal to roll back the usage status of the AI ​​model to the usage status of the non-AI model, or the terminal device evaluates itself and sends the evaluation result to the network side device, and the network side device makes the final decision whether to roll back the usage status of the AI ​​model to the usage status of the non-AI model.

[0188] f) Training or updating of AI models.

[0189] Specifically, when using the AI ​​model, the terminal can train or update the AI ​​model to obtain a new AI model, or the network side device can train or update the AI ​​model to obtain a new AI model, or the network side server, terminal server, core network server or third-party server can train or update the AI ​​model and then send the updated model to the terminal or network side device.

[0190] g) Registration of AI models.

[0191] Specifically, when the terminal obtains the AI ​​model, it can register the AI ​​model, or the network side device can register the AI ​​model.

[0192] h)Configuration of AI model.

[0193] Specifically, when the terminal obtains the AI ​​model, the AI ​​model can be configured, or the network side device can configure the AI ​​model.

[0194] i) Identity confirmation of AI models.

[0195] Specifically, when the terminal obtains the AI ​​model, it can initiate identity confirmation of the AI ​​model to the network side; or when the network side device obtains the AI ​​model, it can initiate identity confirmation of the AI ​​model to the terminal.

[0196] 2) The network side device determines a second identifier for the AI ​​model based on the first identifier and at least part of the model description information of the AI ​​model; the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and the network side device performs lifecycle management of the AI ​​model based on the second identifier.

[0197] Specifically, the network side device can determine a second identifier for the AI ​​model based on the first identifier and at least part of the model description information of the AI ​​model, where the second identifier is used to identify the AI ​​model, and the second identifier is associated with the first identifier; after the network side device determines the second identifier for the AI ​​model, it can perform life cycle management of the AI ​​model based on the second identifier, thereby reducing the complexity of life cycle management of the AI ​​model and improving the flexibility of the communication system.

[0198] Optionally, the subject of the lifecycle management of the AI ​​model can be at least one of the terminal and the network side device; for example, the terminal is the subject of the lifecycle management of the AI ​​model, or the network side device is the subject of the lifecycle management of the AI ​​model, or the terminal and the network side device perform the lifecycle management of the AI ​​model together.

[0199] Optionally, at least part of the bits, fields or contents in the second identifier include: the first identifier, part of the contents of the first identifier, an association with the first identifier, or part of the contents associated with the first identifier.

[0200] For example, the second identifier has 100 bits, of which 10 bits, a field or some content are the first identifier or part of the content of the first identifier, or these 10 bits, a field or some content are associated with the first identifier or part of the content of the first identifier, or these 10 bits, a field or some content indicate certain information, and the certain information includes the first identifier or part of the content of the first identifier, or these 10 bits, a field or some content are the encoded information of the first identifier or part of the content of the first identifier.

[0201] Optionally, the second identifier includes: second information and third information; wherein, the second information is the identification information assigned to the AI ​​model by the network side device when performing model identification; the third information includes any one of the following: the first identifier; part of the content of the first identifier; encoding information of the first identifier; encoding information of part of the content of the first identifier; association information of the first identifier; association information of part of the content of the first identifier.

[0202] Specifically, the second information is identification information assigned to the AI ​​model by the network-side device when performing model identification, that is, after the network-side device obtains the first identification, the identification information assigned to the AI ​​model when performing model identification of the AI ​​model according to the first identification, and the assigned identification information is unrelated to the first identification. The third information includes any one of the following: the first identification; part of the content of the first identification; encoding information of the first identification; encoding information of part of the content of the first identification; association information of the first identification; association information of part of the content of the first identification. When the network-side device performs lifecycle management of the AI ​​model, the second identification composed of the second information and the third information is used to perform lifecycle management of the AI ​​model, thereby improving the management efficiency of the AI ​​model.

[0203] Optionally, the second identification is not only composed of the second information and the third information, that is, the second identification may include other information in addition to the second information and the third information.

[0204] Optionally, the network side device sends the second identifier to the terminal.

[0205] Specifically, after determining the second identifier of the AI ​​model, the network side device can send the second identifier to the terminal, so that the terminal performs life cycle management of the AI ​​model according to the second identifier, thereby reducing the complexity of life cycle management of the AI ​​model and improving the flexibility of the communication system.

[0206] Optionally, the terminal and the network-side device can perform the lifecycle management of the AI ​​model together. For example, after the terminal trains the AI ​​model, the AI ​​model and the first identifier can be sent to the network-side device, and the network-side device trains the AI ​​model again, and after training, the AI ​​model is sent to the terminal. Alternatively, after training the AI ​​model, the third-party server sends the AI ​​model and the first identifier to the terminal and the network-side device at the same time, so that the terminal and the network-side device can perform the lifecycle management of the AI ​​model together.

[0207] Figure 6 is one of the schematic diagrams of interaction between a terminal and a network side device provided in an embodiment of the present application, such as Figure 6 As shown, it includes steps 601 to 603; wherein:

[0208] Step 601: The terminal obtains an AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set.

[0209] Step 602: The terminal sends a target request to the network side device, where the target request includes a first identifier, and the target request is used to request model recognition of the AI ​​model.

[0210] Step 603: The network side device responds to the target request and performs model recognition of the AI ​​model based on the first identifier.

[0211] Figure 7 This is the second schematic diagram of the interaction between the terminal and the network device provided in the embodiment of the present application, such as Figure 7 As shown, it includes steps 701 to 703; wherein:

[0212] Step 701: The terminal obtains an AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set.

[0213] Step 702: The terminal sends a target request to the network side device, where the target request includes a first identifier.

[0214] Step 703: The network side device responds to the target request and performs lifecycle management of the AI ​​model based on the first identifier.

[0215] Figure 8 This is the third schematic diagram of the interaction between the terminal and the network device provided in the embodiment of the present application, such as Figure 8 As shown, it includes steps 801 to 805; wherein:

[0216] Step 801: The terminal obtains an AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set.

[0217] Step 802: The terminal sends a target request to the network side device, where the target request includes a first identifier and at least part of the model description information of the AI ​​model.

[0218] Step 803: The network side device determines a second identifier for the AI ​​model based on the first identifier and at least part of the model description information of the AI ​​model; the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; the network side device performs lifecycle management of the AI ​​model based on the second identifier.

[0219] Step 804: The network-side device sends a second identifier to the terminal.

[0220] Step 805: The terminal receives the second identifier sent by the network side device; the terminal performs life cycle management of the AI ​​model based on the second identifier.

[0221] The information transmission method provided in the embodiment of the present application can be executed by an information transmission device. In the embodiment of the present application, the information transmission device provided in the embodiment of the present application is described by taking the information transmission method executed by the information transmission device as an example.

[0222] Fig. 9 is one of the structural diagrams of the information transmission device provided in the embodiment of the present application, such as Fig. 9 As shown, the information transmission device 900 includes: an acquisition module 901 and a first sending module 902; wherein,

[0223] An acquisition module 901 is used to acquire an artificial intelligence AI model and a first identifier associated with the AI ​​model, wherein the first identifier is associated with a data collection or data set;

[0224] The first sending module 902 is used to send a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0225] In the information transmission device provided in the embodiment of the present application, by obtaining an AI model and a first identifier associated with the AI ​​model, since the first identifier is associated with a data collection or data set, when requesting a network side device to perform model recognition of the AI ​​model, it is only necessary to send the first identifier to the network side device, and there is no need to report a large amount of model description information to the network side device. The network side device can perform model recognition of the AI ​​model according to the first identifier, thereby reducing the signaling overhead of the communication system.

[0226] Optionally, the first identifier includes the first information or encoded information of the first information; the first information includes at least one of the following:

[0227] Dataset information or dataset identification ID;

[0228] Dataset feature dataset categorizing information or dataset feature ID;

[0229] Additional condition information or additional condition ID;

[0230] Applicability information or applicability ID;

[0231] Applicable scenario information or applicable scenario ID;

[0232] Intrinsic condition information or intrinsic condition ID;

[0233] Cell information or cell ID;

[0234] Region information or region ID;

[0235] Scene information or scene ID;

[0236] Hardware configuration information or hardware configuration ID;

[0237] Antenna configuration information or antenna configuration ID;

[0238] Radio resource configuration information or radio resource configuration ID;

[0239] Wireless environment information or wireless environment ID;

[0240] Wireless environment characteristic information or wireless environment characteristic ID;

[0241] Radio access network RAN ​​test configuration information or RAN test configuration ID;

[0242] Wireless test configuration information or wireless test configuration ID.

[0243] Optionally, the target request also includes: at least partial model description information of the AI ​​model.

[0244] Optionally, the first identifier is associated with the AI ​​model or with at least part of the model description information of the AI ​​model.

[0245] Optionally, at least part of the model description information of the AI ​​model includes at least one of the following:

[0246] Functional information of the AI ​​model;

[0247] The feature or feature group for which the AI ​​model can be used;

[0248] Applicable scenario information of the AI ​​model;

[0249] Applicable configuration information of the AI ​​model;

[0250] The input information of the AI ​​model or the format of the input information;

[0251] Output information of the AI ​​model or the format of the output information;

[0252] The auxiliary information of the AI ​​model is used to assist the AI ​​model in reasoning.

[0253] Optionally, the auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; wireless environment characteristic information.

[0254] Optionally, the information transmission device 900 further includes at least one of the following:

[0255] A first lifecycle management module, used to perform lifecycle management of the AI ​​model based on the first identifier;

[0256] A second receiving module is used to receive a second identifier sent by the network side device, where the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and based on the second identifier, the life cycle management of the AI ​​model is performed.

[0257] Optionally, the lifecycle management of the AI ​​model includes at least one of the following:

[0258] Activation or deactivation of the AI ​​model;

[0259] Selection or switching of the AI ​​model;

[0260] Reasoning of the AI ​​model;

[0261] Performance monitoring or performance management of the AI ​​model;

[0262] Fall back to non-AI model;

[0263] Training or updating of the AI ​​model;

[0264] Registration of the AI ​​model;

[0265] The configuration of the AI ​​model;

[0266] Confirmation of the identity of the AI ​​model.

[0267] Fig.10 This is a second structural diagram of the information transmission device provided in the embodiment of the present application, such as Fig.10 As shown, the information transmission device 1000 includes: a first receiving module 1001 and a model identification module 1002; wherein,

[0268] A first receiving module 1001 is used to receive a target request sent by a terminal, where the target request is used to request model recognition of an artificial intelligence AI model, and the target request includes a first identifier, where the first identifier is associated with a data collection or data set;

[0269] The model identification module 1002 is used to perform model identification of the AI ​​model based on the first identifier in response to the target request.

[0270] In the information transmission device provided in the embodiment of the present application, a target request is sent by a receiving terminal, and the target request is used to request model recognition of an artificial intelligence AI model. Since the target request includes a first identifier, and the first identifier is associated with a data collection or data set, it is only necessary to obtain the first identifier sent by the terminal without obtaining a large amount of model description information, and model recognition of the AI ​​model can be performed based on the first identifier, thereby reducing the signaling overhead of the communication system.

[0271] Optionally, the first identifier includes the first information or encoded information of the first information; the first information includes at least one of the following:

[0272] Dataset information or dataset identification ID;

[0273] Dataset feature dataset categorizing information or dataset feature ID;

[0274] Additional condition information or additional condition ID;

[0275] Applicability information or applicability ID;

[0276] Applicable scenario information or applicable scenario ID;

[0277] Intrinsic condition information or intrinsic condition ID;

[0278] Cell information or cell ID;

[0279] Region information or region ID;

[0280] Scene information or scene ID;

[0281] Hardware configuration information or hardware configuration ID;

[0282] Antenna configuration information or antenna configuration ID;

[0283] Radio resource configuration information or radio resource configuration ID;

[0284] Wireless environment information or wireless environment ID;

[0285] Wireless environment characteristic information or wireless environment characteristic ID;

[0286] Radio access network RAN ​​test configuration information or RAN test configuration ID;

[0287] Wireless test configuration information or wireless test configuration ID.

[0288] Optionally, the target request also includes: at least partial model description information of the AI ​​model.

[0289] Optionally, the first identifier is associated with the AI ​​model or with at least part of the model description information of the AI ​​model.

[0290] Optionally, at least part of the model description information of the AI ​​model includes at least one of the following:

[0291] Functional information of the AI ​​model;

[0292] The feature or feature group for which the AI ​​model can be used;

[0293] Applicable scenario information of the AI ​​model;

[0294] Applicable configuration information of the AI ​​model;

[0295] The input information of the AI ​​model or the format of the input information;

[0296] Output information of the AI ​​model or the format of the output information;

[0297] The auxiliary information of the AI ​​model is used to assist the AI ​​model in reasoning.

[0298] Optionally, the auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; wireless environment characteristic information.

[0299] Optionally, the information transmission device 1000 further includes at least one of the following:

[0300] A second lifecycle management module, used to perform lifecycle management of the AI ​​model based on the first identifier;

[0301] A first determination module is used to determine a second identifier for the AI ​​model based on the first identifier and at least part of the model description information of the AI ​​model; the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and lifecycle management of the AI ​​model is performed based on the second identifier.

[0302] Optionally, at least part of the bits, fields or contents in the second identifier include: the first identifier, part of the contents of the first identifier, an association with the first identifier, or part of the contents associated with the first identifier.

[0303] Optionally, the second identifier includes: second information and third information; wherein the second information is identification information assigned by the network side device to the AI ​​model when performing model identification;

[0304] The third information includes any one of the following: the first identifier; part of the content of the first identifier; encoding information of the first identifier; encoding information of part of the content of the first identifier; associated information of the first identifier; associated information of part of the content of the first identifier.

[0305] Optionally, the lifecycle management of the AI ​​model includes at least one of the following:

[0306] Activation or deactivation of the AI ​​model;

[0307] Selection or switching of the AI ​​model;

[0308] Reasoning of the AI ​​model;

[0309] Performance monitoring or performance management of the AI ​​model;

[0310] Fall back to non-AI model;

[0311] Training or updating of the AI ​​model;

[0312] Registration of the AI ​​model;

[0313] The configuration of the AI ​​model;

[0314] Confirmation of the identity of the AI ​​model.

[0315] Optionally, the information transmission device 1000 further includes:

[0316] The second sending module is used to send the second identifier to the terminal.

[0317] Optionally, the information transmission device 1000 further includes:

[0318] A second determination module, used to determine a resource configuration for data collection, wherein the resource configuration is associated with, indicates or carries the first identifier;

[0319] The resource configuration includes at least one of the following:

[0320] Measurement configuration information;

[0321] Report configuration information;

[0322] Resource configuration information of reference signal RS used for data collection;

[0323] Resource set configuration information of RS used for data collection;

[0324] Master Information Block MIB information;

[0325] System Information Block SIB information.

[0326] The information transmission 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.

[0327] The information transmission device provided in the embodiment of the present application can achieve Figures 4 to 8 The various processes implemented by the method embodiment and achieving the same technical effect are not described here to avoid repetition.

[0328] like Fig.11 As shown, the embodiment of the present application further provides a communication device 1100, including a processor 1101 and a memory 1102, and the memory 1102 stores a program or instruction that can be run on the processor 1101. For example, when the communication device 1100 is a terminal, the program or instruction is executed by the processor 1101 to implement the various steps of the above-mentioned information transmission method embodiment, and can achieve the same technical effect. When the communication device 1100 is a network side device, the program or instruction is executed by the processor 1101 to implement the various steps of the above-mentioned information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0329] 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 4 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, Fig.12 A schematic diagram of the hardware structure of a terminal for implementing an embodiment of the present application.

[0330] The terminal 1200 includes but is not limited to: a radio frequency unit 1201, a network module 1202, an audio output unit 1203, an input unit 1204, a sensor 1205, a display unit 1206, a user input unit 1207, an interface unit 1208, a memory 1209 and at least some of the components of the processor 1210.

[0331] Those skilled in the art will appreciate that the terminal 1200 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 1210 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Fig.12 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.

[0332] It should be understood that in the embodiment of the present application, the input unit 1204 may include a graphics processing unit (GPU) 12041 and a microphone 12042, and the graphics processing unit 12041 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 1206 may include a display panel 12061, and the display panel 12061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1207 includes a touch panel 12071 and at least one of other input devices 12072. The touch panel 12071 is also called a touch screen. The touch panel 12071 may include two parts: a touch detection device and a touch controller. Other input devices 12072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control button, a switch button, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0333] In the embodiment of the present application, after receiving downlink data from the network side device, the RF unit 1201 can transmit the data to the processor 1210 for processing; in addition, the RF unit 1201 can send uplink data to the network side device. Generally, the RF unit 1201 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0334] The memory 1209 can be used to store software programs or instructions and various data. The memory 1209 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 1209 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 1209 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0335] The processor 1210 may include one or more processing units; optionally, the processor 1210 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 1210.

[0336] Among them, the processor 1210 is used to obtain an artificial intelligence AI model and a first identifier associated with the AI ​​model, wherein the first identifier is associated with a data collection or data set; the radio frequency unit 1201 is used to send a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

[0337] The terminal obtains the AI ​​model and the first identifier associated with the AI ​​model. Since the first identifier is associated with the data collection or data set, when the terminal requests the network side device to perform model recognition of the AI ​​model, the terminal only needs to send the first identifier to the network side device. There is no need to report a large amount of model description information to the network side device. The network side device can perform model recognition of the AI ​​model according to the first identifier, thereby reducing the signaling overhead of the communication system.

[0338] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the information transmission method of the method embodiment, and achieve the same or corresponding technical effect. To avoid repetition, it will not be repeated here.

[0339] 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 5 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.

[0340] Specifically, the embodiment of the present application also provides a network side device. Fig.13 As shown, the network side device 1300 includes: an antenna 1301, a radio frequency device 1302, a baseband device 1303, a processor 1304 and a memory 1305. The antenna 1301 is connected to the radio frequency device 1302. In the uplink direction, the radio frequency device 1302 receives information through the antenna 1301 and sends the received information to the baseband device 1303 for processing. In the downlink direction, the baseband device 1303 processes the information to be sent and sends it to the radio frequency device 1302. The radio frequency device 1302 processes the received information and sends it out through the antenna 1301.

[0341] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 1303, which includes a baseband processor.

[0342] The baseband device 1303 may include, for example, at least one baseband board on which a plurality of chips are arranged. Fig.13 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 1305 through a bus interface to call the program in the memory 1305 to execute the network device operations shown in the above method embodiment.

[0343] The network side device may further include a network interface 1306, which is, for example, a Common Public Radio Interface (CPRI).

[0344] Specifically, the network side device 1300 of the embodiment of the present application further includes: instructions or programs stored in the memory 1305 and executable on the processor 1304, and the processor 1304 calls the instructions or programs in the memory 1305 to execute Fig.13 The methods executed by the modules shown achieve the same technical effects, and therefore will not be described here in detail to avoid repetition.

[0345] 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 information transmission method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0346] 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.

[0347] 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 information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0348] 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.

[0349] 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 information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0350] An embodiment of the present application also provides an information transmission system, including: a terminal and a network side device, wherein the terminal can be used to execute the steps of the terminal side information transmission method as described above, and the network side device can be used to execute the steps of the network side device side information transmission method as described above.

[0351] 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.

[0352] 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.

[0353] 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. An information transmission method, characterized in that: include: The terminal obtains an artificial intelligence AI model and a first identifier associated with the AI ​​model, where the first identifier is associated with a data collection or data set; The terminal sends a target request to the network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

2. The information transmission method according to claim 1, characterized in that: The first identifier includes the first information or the encoded information of the first information; the first information includes at least one of the following: Dataset information or dataset identification ID; Dataset feature dataset categorizing information or dataset feature ID; Additional condition information or additional condition ID; Applicability information or applicability ID; Applicable scenario information or applicable scenario ID; Intrinsic condition information or intrinsic condition ID; Cell information or cell ID; Region information or region ID; Scene information or scene ID; Hardware configuration information or hardware configuration ID; Antenna configuration information or antenna configuration ID; Radio resource configuration information or radio resource configuration ID; Wireless environment information or wireless environment ID; Wireless environment characteristic information or wireless environment characteristic ID; Radio access network RAN ​​test configuration information or RAN test configuration ID; Wireless test configuration information or wireless test configuration ID.

3. The information transmission method according to claim 1 or 2, characterized in that: The target request also includes: at least part of the model description information of the AI ​​model.

4. The information transmission method according to any one of claims 1 to 3, characterized in that: The first identifier is associated with the AI ​​model or at least part of the model description information of the AI ​​model.

5. The information transmission method according to claim 3 or 4, characterized in that: At least part of the model description information of the AI ​​model includes at least one of the following: Functional information of the AI ​​model; The feature or feature group for which the AI ​​model can be used; Applicable scenario information of the AI ​​model; Applicable configuration information of the AI ​​model; The input information of the AI ​​model or the format of the input information; Output information of the AI ​​model or the format of the output information; The auxiliary information of the AI ​​model is used to assist the AI ​​model in reasoning.

6. The information transmission method according to claim 5, characterized in that: The auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; and wireless environment characteristic information.

7. The information transmission method according to any one of claims 1 to 6, characterized in that: The method further comprises at least one of the following: The terminal performs lifecycle management of the AI ​​model based on the first identifier; The terminal receives a second identifier sent by the network side device, where the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and the terminal performs lifecycle management of the AI ​​model based on the second identifier.

8. The information transmission method according to claim 7, characterized in that: The lifecycle management of the AI ​​model includes at least one of the following: Activation or deactivation of the AI ​​model; Selection or switching of the AI ​​model; Reasoning of the AI ​​model; Performance monitoring or performance management of the AI ​​model; Fall back to non-AI model; Training or updating of the AI ​​model; Registration of the AI ​​model; The configuration of the AI ​​model; Identity confirmation of the AI ​​model.

9. An information transmission method, characterized in that: include: The network side device receives a target request sent by the terminal, where the target request is used to request model recognition of an artificial intelligence AI model, and the target request includes a first identifier, where the first identifier is associated with a data collection or data set; The network side device responds to the target request and performs model identification of the AI ​​model based on the first identifier.

10. The information transmission method according to claim 9, characterized in that: The first identifier includes the first information or the encoded information of the first information; the first information includes at least one of the following: Dataset information or dataset identification ID; Dataset feature dataset categorizing information or dataset feature ID; Additional condition information or additional condition ID; Applicability information or applicability ID; Applicable scenario information or applicable scenario ID; Intrinsic condition information or intrinsic condition ID; Cell information or cell ID; Region information or region ID; Scene information or scene ID; Hardware configuration information or hardware configuration ID; Antenna configuration information or antenna configuration ID; Radio resource configuration information or radio resource configuration ID; Wireless environment information or wireless environment ID; Wireless environment characteristic information or wireless environment characteristic ID; Radio access network RAN ​​test configuration information or RAN test configuration ID; Wireless test configuration information or wireless test configuration ID.

11. The information transmission method according to claim 9 or 10, characterized in that: The target request also includes: at least part of the model description information of the AI ​​model.

12. The information transmission method according to any one of claims 9 to 11, characterized in that: The first identifier is associated with the AI ​​model or at least part of the model description information of the AI ​​model.

13. The information transmission method according to claim 11 or 12, characterized in that: At least part of the model description information of the AI ​​model includes at least one of the following: Functional information of the AI ​​model; The feature or feature group for which the AI ​​model can be used; Applicable scenario information of the AI ​​model; Applicable configuration information of the AI ​​model; The input information of the AI ​​model or the format of the input information; Output information of the AI ​​model or the format of the output information; The auxiliary information of the AI ​​model is used to assist the AI ​​model in reasoning.

14. The information transmission method according to claim 13, characterized in that: The auxiliary information includes at least one of the following: hardware configuration information; antenna configuration information; wireless resource configuration information; and wireless environment characteristic information.

15. The information transmission method according to any one of claims 11 to 14, characterized in that: The method further comprises at least one of the following: The network-side device performs lifecycle management of the AI ​​model based on the first identifier; The network side device determines a second identifier for the AI ​​model based on the first identifier and at least part of the model description information of the AI ​​model; the second identifier is used to identify the AI ​​model; the second identifier is associated with the first identifier; and the network side device performs lifecycle management of the AI ​​model based on the second identifier.

16. The information transmission method according to claim 15, characterized in that: At least part of the bits, fields or contents in the second identifier include: the first identifier, part of the contents of the first identifier, an identifier associated with the first identifier, or part of the contents associated with the first identifier.

17. The information transmission method according to claim 15, characterized in that: The second identifier includes: second information and third information; wherein the second information is identification information assigned by the network side device to the AI ​​model when performing model identification; The third information includes any one of the following: the first identifier; part of the content of the first identifier; encoding information of the first identifier; encoding information of part of the content of the first identifier; associated information of the first identifier; associated information of part of the content of the first identifier.

18. The information transmission method according to any one of claims 15 to 17, characterized in that: The lifecycle management of the AI ​​model includes at least one of the following: Activation or deactivation of the AI ​​model; Selection or switching of the AI ​​model; Reasoning of the AI ​​model; Performance monitoring or performance management of the AI ​​model; Fall back to non-AI model; Training or updating of the AI ​​model; Registration of the AI ​​model; The configuration of the AI ​​model; Identity confirmation of the AI ​​model.

19. The information transmission method according to any one of claims 15 to 18, characterized in that: The method further comprises: The network side device sends the second identifier to the terminal.

20. The information transmission method according to any one of claims 9 to 19, characterized in that: The method further comprises: The network side device determines a resource configuration for data collection, where the resource configuration is associated with, indicates or carries the first identifier; The resource configuration includes at least one of the following: Measurement configuration information; Report configuration information; Resource configuration information of reference signal RS used for data collection; Resource set configuration information of RS used for data collection; Master Information Block MIB information; System Information Block SIB information.

21. An information transmission device, characterized in that: include: An acquisition module, used to acquire an artificial intelligence AI model and a first identifier associated with the AI ​​model, wherein the first identifier is associated with a data collection or data set; The first sending module is used to send a target request to a network side device, wherein the target request includes the first identifier, and the target request is used to request model identification of the AI ​​model.

22. An information transmission device, characterized in that: include: A first receiving module, configured to receive a target request sent by a terminal, wherein the target request is used to request model recognition of an artificial intelligence AI model, and the target request includes a first identifier, and the first identifier is associated with a data collection or data set; A model identification module is used to perform model identification of the AI ​​model based on the first identifier in response to the target request.

23. A terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the information transmission method according to any one of claims 1 to 8 are implemented.

24. A network side device, characterized in that: It 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 information transmission method as described in any one of claims 9 to 20 are implemented.

25. 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 the processor, it implements the information transmission method as described in any one of claims 1 to 8, or implements the steps of the information transmission method as described in any one of claims 9 to 20.

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