Information reporting method and device, first equipment and second equipment

Abstract

The invention discloses an information reporting method and device, first equipment and second equipment, and belongs to the technical field of wireless communication, and the information reporting method comprises the steps that the first equipment sends a target ID of an artificial intelligence AI function to the second equipment, the target ID comprises at least one information field, and the information field comprises at least one information field; different information fields correspondingly carry different attribute information of the AI function.

Description

Technical Field

[0001] The present application belongs to the field of wireless communication technology, and specifically relates to an information reporting method, apparatus, first device and second device. Background Art

[0002] Artificial Intelligence (AI) has been widely used in various fields. Integrating AI technology into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important research direction for wireless communication networks. Currently, both equipment manufacturers and network manufacturers are concerned about model privacy issues.

[0003] In the existing technology, how to realize the interaction of AI function information between different network entities is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide an information reporting method, an apparatus, a first device, and a second device, which can realize the interaction of AI function information between different network entities.

[0005] In a first aspect, an information reporting method is provided, which is performed by a first device, and the method includes:

[0006] The first device sends a target identification ID of an artificial intelligence AI function to the second device, where the target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0007] In a second aspect, an information reporting method is provided, which is performed by a second device, and the method includes:

[0008] The second device receives a target identification ID of the artificial intelligence AI function sent by the first device, where the target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0009] In a third aspect, an information reporting device is provided, including:

[0010] The first sending module is used to send a target identification ID of an artificial intelligence AI function to a second device, wherein the target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0011] In a fourth aspect, an information reporting device is provided, comprising:

[0012] A third receiving module, configured to receive a target identification ID of an artificial intelligence (AI) function sent by a first device, where the target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0013] In a fifth aspect, a first device is provided, which includes a processor and a memory. The memory stores a program or instructions that can be run on the processor. When the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0014] In a sixth aspect, a first device is provided, including a processor and a communication interface. The communication interface is configured to send a target identification ID of an artificial intelligence (AI) function to a second device, where the target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0015] In a seventh aspect, a second device is provided, which includes a processor and a memory. The memory stores a program or instructions that can be run on the processor. When the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0016] In an eighth aspect, a second device is provided, including a processor and a communication interface. The communication interface is configured to receive a target identification ID of an artificial intelligence (AI) function sent by a first device, where the target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0017] In a ninth aspect, a readable storage medium is provided. The readable storage medium stores a program or instructions. When the program or instructions are 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.

[0018] In a tenth aspect, a wireless communication system is provided, including: a first device and a second device. The first device can be used to execute the steps of the method described in the first aspect, and the second device can be used to execute the steps of the method described in the second aspect.

[0019] In an eleventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run a program or instructions to implement the method described in the first aspect, or implement the method described in the second aspect.

[0020] In a twelfth aspect, there is provided a computer program / program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect or the steps of the method described in the second aspect.

[0021] In an embodiment of the present application, the target ID of the AI function is sent from a first device to a second device, and at least one piece of associated information of the AI function is indicated by the target ID, so that the second device can learn at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities while taking into account flexibility and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A block diagram of a wireless communication system to which embodiments of the present application can be applied is shown;

[0023] Figure 2 is a schematic diagram of a neural network in the prior art;

[0024] Figure 3 is a schematic diagram of a neuron in the prior art;

[0025] Figure 4 is one of the schematic flowcharts of the information reporting method provided by an embodiment of the present application;

[0026] Figure 5 is one of the schematic diagrams of the composition of the target ID provided by an embodiment of the present application;

[0027] Figure 6 is another schematic diagram of the composition of the target ID provided by an embodiment of the present application;

[0028] Figure 7 is another schematic flowchart of the information reporting method provided by an embodiment of the present application;

[0029] Figure 8 is one of the schematic structural diagrams of the information reporting device provided by an embodiment of the present application;

[0030] Figure 9 is another schematic structural diagram of the information reporting device provided by an embodiment of the present application;

[0031] Figure 10 is the schematic structural diagram of the communication device provided by an embodiment of the present application;

[0032] Figure 11 is the schematic hardware structure diagram of the first device provided by an embodiment of the present application;

[0033] Figure 12 is the schematic hardware structure diagram of the second device provided by an embodiment of the present application. Detailed implementation mode

[0034] The following will clearly describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are a part rather than all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application fall within the scope of protection of the present application.

[0035] The terms "first", "second", etc. in 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 such terms can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "or" in the present application means at least one of the connected objects. For example, "A or B" covers three scenarios, namely, Scenario 1: including A and not including B; Scenario 2: including B and not including A; Scenario 3: including both A and B. The character " / " generally indicates an "or" relationship between the associated objects before and after.

[0036] The term "indication" in the present application can be either a direct indication (or an explicit indication) or an indirect indication (or an implicit indication). Among them, a direct indication can be understood as that the sender clearly tells the receiver specific information, operations to be performed, or request results, etc. in the sent indication; an indirect indication can be understood as that the receiver determines the corresponding information according to the indication sent by the sender, or makes a judgment and determines the operations to be performed or request results, etc. according to the judgment result.

[0037] It should be noted that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, 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 this application are often used interchangeably, and the described technology can be used in the above-mentioned systems and radio technologies, as well as in other systems and radio technologies. The following description describes the New Radio (NR) system for example purposes, and the NR terminology is used in most of the following descriptions, but these technologies can also be applied to systems other than the NR system, such as the 6th Generation (6G) communication system. th Generation, 6G) communication system.

[0038] Figure 1A block diagram of a wireless communication system to which embodiments of the present application can be applied 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 personal computer, a laptop computer, a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device, a flight vehicle, a vehicle user equipment (VUE), a shipborne device, a pedestrian user equipment (PUE), a smart home (home devices with wireless communication functions, such as refrigerators, TVs, washing machines, or furniture, etc.), a game console, a personal computer (PC), a teller machine, or a self-service machine, etc., terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle user equipment can also be referred to as a vehicle terminal, a vehicle controller, a vehicle module, a vehicle component, a vehicle chip, or a vehicle unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiments of the present application. The network-side device 12 can include an access network device or a core network device. Among them, the access network device can also be referred to as a radio access network (RAN) device, a radio access network function, or a radio access network unit. The access network device can include a base station, a wireless local area network (WLAN) access point (AP), or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home Node B (HNB), home evolved Node B, Transmission Reception Point (TRP), or some other suitable term in the art. 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 this application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.

[0039] To facilitate a clearer understanding of the technical solutions provided in the embodiments of this application, some related knowledge is introduced as follows.

[0040] Regarding AI:

[0041] AI technology has currently been widely applied in various fields. Incorporating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important task for wireless communication networks. There are various implementation methods for the AI module, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. In the embodiments of this application, a neural network is taken as an example for illustration, but the specific type of the AI module is not limited.

[0042] Figure 2 is a schematic diagram of a neural network in the prior art, as Figure 2 shown, the neural network is composed of neurons. Figure 3 is a schematic diagram of a neuron in the prior art, as Figure 3 shown. Among them, a1, a2,..., a KLet the input be \(x\), \(w\) be the weights (multiplicative coefficients), \(b\) be the bias (additive coefficient), and \(\sigma(.)\) be the activation function. Common activation functions include Sigmoid, tanh, and ReLU (Rectified Linear Unit).

[0043] The parameters of the neural network are optimized through gradient optimization algorithms. Gradient optimization algorithms are a class of algorithms that minimize or maximize the objective function (also called the 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. After having the model, the predicted output \(f(x)\) can be obtained according to the input \(x\), and the difference between the predicted value and the true value \((f(x)-Y)\) can be calculated, which is the loss function. The goal 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 real situation.

[0044] Currently, 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 signals and the backward propagation of errors. During forward propagation, the input samples are input from the input layer, and after being processed layer by layer through each hidden layer, they are transmitted to the output layer. If the actual output of the output layer does not match the expected output, it enters the stage of backward propagation of errors. The error backpropagation is to backpropagate the output error to the input layer layer by layer through the hidden layer in a certain form, and distribute the error to all units of each layer, so as to obtain the error signals of each layer of units. This error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer in the forward propagation of signals and the backward propagation of errors is carried out cyclically. The process of continuously adjusting the weights is also 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 preset number of learning times is reached.

[0045] Common optimization algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (specifically Stochastic Gradient Descent with Momentum), Adagrad (ADAptive GRADient descent), Adadelta, RMSprop (root mean square prop), Adam (Adaptive Moment Estimation), etc.

[0046] When these optimization algorithms perform backpropagation of errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained from the loss function, and then add the influence of the learning rate, previous gradient / derivative / partial derivative, etc. to obtain the gradient, which is then passed to the previous layer.

[0047] In the embodiments of this application, the AI unit / AI model mentioned can also be referred to as an AI unit, an AI model, 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, a neural network function, etc. Or the AI unit / AI model can also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc. related to AI. Or the AI unit / AI model can be a processing method, algorithm, function, module, or unit for a specific data set. Or the AI unit / AI model can be a processing method, algorithm, function, module, or unit running on AI / ML-related hardware such as GPUs, NPUs, TPUs, ASICs, etc. This application does not make specific limitations in this regard. Optionally, the specific data set includes the input and / or output of the AI unit / AI model.

[0048] Optionally, the identifier of the AI unit / AI model can be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or the identifier of a specific data set associated with the AI unit / AI model, or the identifier of a specific scenario, environment, channel feature, device related to AI / ML, or the identifier of a function, feature, ability, or module related to AI / ML. Embodiments of this application do not make specific limitations in this regard.

[0049] Next, with reference to the accompanying drawings, some embodiments and their application scenarios will be used to elaborate in detail on the information reporting method, device, first device, and second device provided in the embodiments of this application.

[0050] Figure 4 FIG. 0 is one of the schematic flowcharts of the information reporting method provided by an embodiment of the present application. This method is applied to a first device, such as Figure 4 As shown, the method includes:

[0051] Step 401, the first device sends a target identifier ID of an artificial intelligence (AI) function to a second device. The target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

[0052] Optionally, the first device may include, but is not limited to, the types of the above-listed terminal 11, and other devices may be a server, a network attached storage (NAS), etc.; the second device may include, but is not limited to, the types of the above-listed network-side device 12. The embodiments of the present application do not make any limitation thereto.

[0053] It should be noted that the AI function may include one or more AI models, or the AI function may also be referred to as an AI model.

[0054] Optionally, in the embodiments of the present application, it is assumed that the total length of the target ID is M bits, where M is a positive integer. The length of each information field in the target ID and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device. That is to say, the length of any information field in the target ID and the position of each information field in the target ID may be defined by the protocol, determined by the first device, or indicated by the second device. It can be understood that the lengths of different information fields may be the same or different. The embodiments of the present application do not make any limitation thereto.

[0055] Optionally, the information fields in the target ID are used to carry attribute information. Different information fields in the target ID carry different attribute information of the AI function, that is, different attribute information of the AI function is represented by fields at different positions in the target ID. The meanings of different information fields correspond to different attribute information of the AI function.

[0056] Figure 5 FIG. 22 is one of the schematic diagrams of the composition of the target ID provided by an embodiment of the present application. Figure 5 It is shown that the total length of the target ID is M bits, and the target ID includes N information fields, where N is a positive integer; different information fields carry different attribute information of the AI function. Figure 5 It also shows the number of bits included in different fields.

[0057] For example, the 1st to s1st bits are the 1st field, representing the task identifier associated with the AI function; the (s1 + 1)th to (s1 + s2)th bits are the 2nd field, representing the model input type of the AI function; and so on.

[0058] Optionally, if the bits of the target information field in at least one information field of the target ID are all 0, it means that the target information field does not carry the corresponding attribute information. For example, if the bits of a certain information field (such as the target information field) in the target ID are all 0, it means that the target information field does not carry the corresponding attribute information, indicating that the first device may not want to expose the attribute information of the AI function corresponding to the target information field or for other reasons.

[0059] Optionally, the attribute information of the AI function may include at least one of the following:

[0060] a) The tasks or use cases associated with the AI function.

[0061] b) The model input type of the AI function.

[0062] c) The model input format of the AI function; the model input format is, for example, a matrix of size N * M, where both M and N are positive integers.

[0063] d) The model input preprocessing method of the AI function; the preprocessing method is, for example, 0 - 1 normalization. Through 0 - 1 normalization, the maximum value of the model input information is 1 and the minimum value is 0.

[0064] e) The model output type of the AI function.

[0065] f) The model output format of the AI function; the model output format is, for example, a vector of size Z * 1, where Z is a positive integer.

[0066] g) The post - processing method of the model output of the AI function; the post - processing method is, for example, coordinate transformation or unit transformation, etc.

[0067] h) The computational complexity of the AI function; the computational complexity is, for example, T Flops.

[0068] i) The inference latency of the AI function, for example, 1 ms.

[0069] j) The activation and / or de - activation latency of the AI function, for example, 1 ms.

[0070] k) The performance supervision ability of the AI function, for example: whether the first device performs performance supervision without relying on the network - side device.

[0071] l) The number of parameters of the AI function; for example, it is T Flops.

[0072] m) Identification of the model structure of the AI function; the model structure identification is, for example, a fully connected, convolutional, or Transformer structure, etc.

[0073] n) Quantization method of the AI function; the quantization method is, for example, int8, float32, or float64, etc.

[0074] o) Generated information of the AI function; the generated information is, for example, the version number, vendor information, or the identification of the model training unit, etc.

[0075] p) Validity conditions of the AI function; the validity conditions are, for example, the cell ID, TRP ID, scenario ID, or channel conditions (such as the Signal to Interference Noise Ratio (SINR) distribution, Reference Signal Received Power (RSRP) distribution) associated with the AI function, etc.

[0076] q) Inference accuracy or error of the AI function.

[0077] r) Number of AI models included in the AI function.

[0078] Here, the attribute information of the AI function is illustrated by several examples.

[0079] Example 1: The task or use case associated with the AI function is AI-based positioning: Using an AI model or function to determine the location-related information of a terminal.

[0080] Model input types: Channel impulse response, power delay profile, delay profile, RSRP, etc.

[0081] Model output types: Location information, time of arrival, relative time of arrival, line of sight (LOS) / Non Line of Sight (NLOS) indication, etc.

[0082] Example 2: The task or use case associated with the AI function is AI-based beam management: Using an AI model or function to predict future beam information based on past beam information.

[0083] Model input types: Beam IDs, RSRP, beam angles, etc. of the past N beams.

[0084] Model output types: Beam IDs, RSRP, beam angles, etc. of the future M beams.

[0085] Example 3: The task or use case associated with the AI function is AI-based Channel State Information (CSI) prediction: Using an AI model or function, predict future CSI based on past CSI.

[0086] Model input type: CSI for the past N time instants.

[0087] Model output type: CSI for the next N time instants.

[0088] Example 4: The task or use case associated with the AI function is AI-based CSI compression: Using an AI model or function, perform CSI compression and decompression.

[0089] For compression: The model input type is uncompressed CSI; the model output type is compressed CSI.

[0090] For decompression: The model input type is compressed CSI; the model output type is restored CSI.

[0091] Example 5: The task or use case associated with the AI function is AI-based mobility management: Using an AI model, predict future RSRP based on the terminal's past RSRP, or predict future cell handover status based on past RSRP, etc.

[0092] Model input type: RSRP, Signal Noise Ratio (SNR), SINR, etc. for the past N time instants.

[0093] Model output type: RSRP, SNR, SINR, cell handover decision, etc. for the next M time instants.

[0094] In the embodiments of the present application, the target ID of the AI function is sent from the first device to the second device, and at least one piece of associated information of the AI function is indicated by the target ID, so that the second device can obtain at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities while taking into account flexibility and privacy.

[0095] Optionally, in the embodiments of the present application, at least one information field included in the target ID may include:

[0096] The first part includes one or more information fields for carrying the public information of the AI function;

[0097] The second part includes one or more information fields for carrying the private information of the AI function, or the second part is used to identify the AI function.

[0098] It should be noted that the information field in the target ID is used to carry attribute information. The public information of the AI function refers to one or more attribute information of the AI function that the first device needs to disclose. The public information of the AI function includes, for example, at least one of the task or use case associated with the AI function, the model input type of the AI function, the model input format of the AI function, the model output format of the AI function, and the model output type of the AI function.

[0099] The private information of the AI function refers to one or more attribute information of the AI function that the first device can selectively disclose. The private information of the AI function includes, for example, at least one of the generated information of the AI function, the validity condition of the AI function, the model input preprocessing method of the AI function, and the post-processing method of the model output of the AI function.

[0100] Optionally, the second part in the target ID may include one or more information fields for carrying the private information of the AI function, that is, the second part represents the private information of the AI function. Alternatively, the second part in the target ID may be used to identify the AI function, that is, the second part only distinguishes different AI functions and does not represent attribute information.

[0101] Optionally, in the embodiment of the present application, the target ID further includes a first indication field, and the first indication field is used to indicate that the second part includes one or more information fields for carrying the private information of the AI function, or the first indication field is used to indicate that the second part is used to identify the AI function. In the embodiment of the present application, the role of the second part in the target ID is indicated by the first indication field in the target ID.

[0102] Figure 6 This is the second schematic diagram of the target ID provided by the embodiment of the present application. Figure 6 In the shown target ID, the first indication field is 1 bit and is used to indicate the role of the second part. This first indication field is an additional 1 bit other than the first part and the second part. For example, when the first indication field is 1, it means that the second part represents the private information of the AI function; when the first indication field is 0, it means that the second part is used to identify the AI function, that is, to distinguish different AI functions.

[0103] Optionally, the first indication field may not be indicated separately within the target ID either.

[0104] Optionally, in a scenario where the second part in the target ID is used to identify the AI function, there may be a situation where different first devices report different target IDs of AI functions to the second device. However, there may be problems of duplication or repetition in the second parts of the target IDs of different AI functions reported by different first devices, resulting in the inability to distinguish different AI functions through the second part. In the embodiments of the present application, when the second part in the target ID reported by the first device is used to identify the AI function, and the second part has been used by other AI functions, or the second part has been reserved for other AI functions, or for other reasons, the second device determines that the second part or the target ID cannot be used, and performs operation a and / or operation b, where:

[0105] Operation a: The second device sends first indication information to the first device, and the first indication information is used to instruct the first device to update the second part. Optionally, the first indication information can also be used to instruct the first device to report the updated second part and / or the updated target ID.

[0106] Correspondingly, the first device receives the first indication information sent by the second device; the first device updates the second part in the target ID based on the first indication information to obtain an updated second part and an updated target ID; the first device sends the updated second part or the updated target ID to the second device. For example, the first device re-obtains the second part based on the first indication information.

[0107] The second device receives the updated second part or the updated target ID sent by the first device.

[0108] Operation b: The second device determines first information for the first device to update the second part; the second device sends the first information to the first device. The second device ensures that the first information has not been used by the second parts of the target IDs of the AI functions reported by other first devices.

[0109] Correspondingly, the first device receives the first information sent by the second device; the first device determines the updated second part and the updated target ID according to the first information. For example, the first device determines the first information as the updated second part.

[0110] Further, the first device sends the updated target ID to the second device; the second part in the updated target ID is determined according to the first information. The second device receives the updated target ID sent by the first device.

[0111] Alternatively, the first device sends second indication information to the second device, where the second indication information is used to indicate that the second part in the updated target ID is determined according to the first information. The second device receives the second indication information sent by the first device.

[0112] Optionally, the target ID may further include a second indication field, where the second indication field is used to identify the AI function, that is, the second indication field is only used to distinguish different AI functions. The second indication field may be one of the fields in the target ID or an additional field.

[0113] In the embodiments of the present application, the composition of the target ID of the AI function is provided, that is, the total length of the target ID and different information fields in the target ID carry different attribute information of the AI function. The length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device. The composition method of the target ID may be: 1) all are private information, but the first device can selectively expose some attribute information of the AI function, and the information fields corresponding to the attribute information that are not willing to be exposed are masked, for example, the information field can be all 0, indicating that it does not carry the corresponding attribute information. 2) Public information + private information; among them, for public information, the first device must provide it; for private information, the first device can selectively provide it.

[0114] Figure 7 is the second schematic diagram of the process of the information reporting method provided in the embodiments of the present application. This method is applied to the second device, as Figure 7 shown, this method includes:

[0115] Step 701, the second device receives the target identifier ID of the artificial intelligence (AI) function sent by the first device. The target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

[0116] In the embodiments of the present application, by receiving the target ID of the AI function sent by the second device, the target ID is used to indicate at least one piece of associated information of the AI function, so as to obtain at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0117] Optionally, the attribute information includes at least one of the following:

[0118] The task or use case associated with the AI function;

[0119] The model input type of the AI function;

[0120] The model input format of the AI function;

[0121] The model input preprocessing method of the AI function;

[0122] The model output type of the AI function;

[0123] The model output format of the AI function;

[0124] The post-processing method of the model output of the AI function;

[0125] The computational complexity of the AI function;

[0126] The inference latency of the AI function;

[0127] The latency of activation and / or deactivation of the AI function;

[0128] The performance supervision ability of the AI function;

[0129] The number of parameters of the AI function;

[0130] The model structure identifier of the AI function;

[0131] The quantization method of the AI function;

[0132] The generated information of the AI function;

[0133] The validity condition of the AI function;

[0134] The inference accuracy or error of the AI function;

[0135] The number of AI models included in the AI function.

[0136] Optionally, the length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

[0137] Optionally, if the bits of the target information field in the at least one information field are all 0, then the target information field does not carry the corresponding attribute information.

[0138] Optionally, the at least one information field includes:

[0139] A first part, including one or more information fields for carrying public information of the AI function;

[0140] A second part, including one or more information fields for carrying private information of the AI function, or the second part is used to identify the AI function.

[0141] Optionally, the target ID further includes:

[0142] A first indication field, which is used to indicate that the second part includes one or more information fields for carrying private information of the AI function, or is used to indicate that the second part is used to identify the AI function.

[0143] Optionally, the method further includes:

[0144] When the second part is used to identify the AI function and the second part is used by other AI functions, the second device sends first indication information to the first device, and the first indication information is used to indicate the first device to update the second part;

[0145] The second device receives the updated second part or the updated target ID sent by the first device.

[0146] Optionally, the method further includes:

[0147] When the second part is used to identify the AI function and the second part is used by other AI functions, the second device determines first information;

[0148] The second device sends the first information to the first device, and the first information is used for the first device to update the second part.

[0149] Optionally, the method further includes:

[0150] The second device receives the updated target ID sent by the first device; the second part in the updated target ID is determined according to the first information;

[0151] Or,

[0152] The second device receives second indication information sent by the first device, and the second indication information is used to indicate that the second part in the updated target ID is determined according to the first information.

[0153] Optionally, the target ID further includes: a second indication field, which is used to identify the AI function.

[0154] An embodiment of the present application further provides an information reporting method, which is cooperatively executed by a first device and a second device. The method includes step a and step b, where:

[0155] Step a: The first device sends a target ID of an AI function to the second device, and the target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

[0156] Step b, the second device receives the target identification ID of the artificial intelligence (AI) function sent by the first device, and the target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0157] In the embodiment of the present application, by sending the target ID of the AI function from the first device to the second device, the target ID is used to indicate at least one piece of associated information of the AI function, so that the second device can obtain at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0158] For the information reporting method provided in the embodiment of the present application, the execution subject may be an information reporting device. In the embodiment of the present application, taking the information reporting device executing the information reporting method as an example, the information reporting device provided in the embodiment of the present application is described.

[0159] Figure 8 is one of the schematic structural diagrams of the information reporting device provided in the embodiment of the present application. As Figure 8 shown, the information reporting device 800 is applied to the first device, and the information reporting device 800 includes:

[0160] A first sending module 801, configured to send a target identification ID of an artificial intelligence (AI) function to a second device, where the target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0161] In the embodiment of the present application, by sending the target ID of the AI function to the second device, at least one piece of associated information of the AI function is indicated by the target ID, so that the second device can obtain at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0162] Optionally, the attribute information includes at least one of the following:

[0163] The task or use case associated with the AI function;

[0164] The model input type of the AI function;

[0165] The model input format of the AI function;

[0166] The model input preprocessing method of the AI function;

[0167] The model output type of the AI function;

[0168] The model output format of the AI function;

[0169] The post - processing method of the model output of the AI function;

[0170] The computational complexity of the AI function;

[0171] The inference latency of the AI function;

[0172] The latency of activation and / or de - activation of the AI function;

[0173] The performance supervision ability of the AI function;

[0174] The number of parameters of the AI function;

[0175] The model structure identifier of the AI function;

[0176] The quantization method of the AI function;

[0177] The generated information of the AI function;

[0178] The validity condition of the AI function;

[0179] The inference accuracy or error of the AI function;

[0180] The number of AI models included in the AI function.

[0181] Optionally, the length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

[0182] Optionally, if the bits of the target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

[0183] Optionally, the at least one information field includes:

[0184] The first part, including one or more information fields for carrying the public information of the AI function;

[0185] The second part, including one or more information fields for carrying the private information of the AI function, or the second part is used to identify the AI function.

[0186] Optionally, the target ID further includes:

[0187] The first indication field, used to indicate that the second part includes one or more information fields for carrying the private information of the AI function, or used to indicate that the second part is used to identify the AI function.

[0188] Optionally, the device further includes:

[0189] A first receiving module, configured to receive first indication information sent by the second device, where the first indication information is used to indicate the first device to update the second part;

[0190] An updating module, configured to update the second part in the target ID based on the first indication information to obtain an updated second part and an updated target ID;

[0191] A second sending module, configured to send the updated second part or the updated target ID to the second device.

[0192] Optionally, the apparatus further includes:

[0193] A second receiving module, configured to receive first information sent by the second device;

[0194] A first determining module, configured to determine an updated second part and an updated target ID according to the first information.

[0195] Optionally, the apparatus further includes:

[0196] A third sending module, configured to:

[0197] send the updated target ID to the second device; the second part in the updated target ID is determined according to the first information;

[0198] or

[0199] send second indication information to the second device, where the second indication information is used to indicate that the second part in the updated target ID is determined according to the first information.

[0200] Optionally, the target ID further includes:

[0201] A second indication field, configured to identify the AI function.

[0202] The information reporting apparatus in the embodiments 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 the first device or other devices other than the first device. Exemplarily, the first device may include, but is not limited to, the types of the above-listed terminal 11, and other devices may be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0203] The information reporting apparatus provided in the embodiments of the present application can implement Figure 4The various processes implemented by the method embodiments shown achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0204] Figure 9 is the second structural schematic diagram of the information reporting device provided by the embodiments of the present application. As Figure 9 shown, the information reporting device 900 is applied to the second device. The information reporting device 900 includes:

[0205] A third receiving module 901, configured to receive a target identifier ID of an artificial intelligence (AI) function sent by a first device. The target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

[0206] In the embodiments of the present application, by receiving the target ID of the AI function sent by the second device, the target ID is used to indicate at least one piece of associated information of the AI function, so as to obtain at least one piece of associated information of the AI function by parsing the target ID, realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0207] Optionally, the attribute information includes at least one of the following:

[0208] The task or use case associated with the AI function;

[0209] The model input type of the AI function;

[0210] The model input format of the AI function;

[0211] The model input preprocessing method of the AI function;

[0212] The model output type of the AI function;

[0213] The model output format of the AI function;

[0214] The post-processing method of the model output of the AI function;

[0215] The computational complexity of the AI function;

[0216] The inference latency of the AI function;

[0217] The latency of activation and / or deactivation of the AI function;

[0218] The performance supervision ability of the AI function;

[0219] The number of parameters of the AI function;

[0220] The model structure identifier of the AI function;

[0221] The quantization method of the AI function;

[0222] The generated information of the AI function;

[0223] The validity condition of the AI function;

[0224] The inference accuracy or error of the AI function;

[0225] The number of AI models included in the AI function.

[0226] Optionally, the length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

[0227] Optionally, if the bits of the target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

[0228] Optionally, the at least one information field includes:

[0229] A first part, including one or more information fields for carrying the public information of the AI function;

[0230] A second part, including one or more information fields for carrying the private information of the AI function, or the second part is used to identify the AI function.

[0231] Optionally, the target ID further includes:

[0232] A first indication field, used to indicate that the second part includes one or more information fields for carrying the private information of the AI function, or used to indicate that the second part is used to identify the AI function.

[0233] Optionally, the device further includes:

[0234] A fourth sending module, used to send first indication information to the first device when the second part is used to identify the AI function and the second part is used by other AI functions, where the first indication information is used to indicate the first device to update the second part;

[0235] A fourth receiving module, used to receive the updated second part or the updated target ID sent by the first device.

[0236] Optionally, the device further includes:

[0237] A second determination module, configured to determine first information when the second part is used to identify the AI function and the second part is used by other AI functions;

[0238] A fifth sending module, configured to send the first information to the first device, where the first information is used by the first device to update the second part.

[0239] Optionally, the device further includes:

[0240] A fifth receiving module, configured to:

[0241] Receive the updated target ID sent by the first device; the second part in the updated target ID is determined according to the first information;

[0242] Or,

[0243] Receive the second indication information sent by the first device, where the second indication information is used to indicate that the second part in the updated target ID is determined according to the first information.

[0244] Optionally, the target ID further includes: a second indication field, configured to identify the AI function.

[0245] The information reporting device in the embodiments 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. This electronic device may be a second device or other devices other than the second device. Exemplarily, the second device may include, but is not limited to, the types of the network-side device 12 listed above, and other devices may be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0246] The information reporting device provided in the embodiments of the present application can implement Figure 7 each process implemented by the method embodiment shown, and achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0247] The embodiments of the present application further provide a communication device, Figure 10 which is a schematic structural diagram of the communication device provided in the embodiments of the present application. As Figure 10 shown, the embodiments of the present application further provide a communication device 1000, including a processor 1001 and a memory 1002. A program or instruction that can run on the processor 1001 is stored on the memory 1002. For example, when the communication device 1000 is the first device, when the program or instruction is executed by the processor 1001, it implements the above Figure 4each step of the method embodiment shown, and can achieve the same technical effect. When the communication device 1000 is the second device, when the program or instruction is executed by the processor 1001, the above-mentioned Figure 7 each step of the method embodiment shown, and can achieve the same technical effect. To avoid repetition, it will not be described here again.

[0248] The embodiment of the present application further provides a first device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement as Figure 4 the steps in the method embodiment shown. The first device embodiment corresponds to the above-mentioned first device method embodiment. Each implementation process and implementation manner of the above method embodiment can be applied to the first device embodiment, and can achieve the same technical effect.

[0249] The embodiment of the present application further provides a first device, Figure 11 which is a schematic hardware structure diagram of the first device provided by the embodiment of the present application. As Figure 11 shown, the first device 1100 includes but is not limited to: at least some components such as a radio frequency unit 1101, a network module 1102, an audio output unit 1103, an input unit 1104, a sensor 1105, a display unit 1106, a user input unit 1107, an interface unit 1108, a memory 1109, and a processor 1110.

[0250] Those skilled in the art can understand that the first device 1100 may further include a power supply (such as a battery) for supplying power to each component. The power supply can be logically connected to the processor 1110 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 11 The first device structure shown in does not constitute a limitation on the first device. The first device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements, which will not be described here again.

[0251] It should be understood that in the embodiments of the present application, the input unit 1104 may include a Graphics Processing Unit (GPU) 11041 and a microphone 11042. The graphics processing unit 11041 processes the image data of static pictures or videos obtained by an image capturing device (such as a camera) in the video capturing mode or the image capturing mode. The display unit 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of, for example, a liquid crystal display, an organic light emitting diode, etc. The user input unit 1107 includes at least one of a touch panel 11071 and other input devices 11072. The touch panel 11071 is also referred to as a touch screen. The touch panel 11071 may include two parts: a touch detection device and a touch controller. The other input devices 11072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be elaborated herein.

[0252] In the embodiments of the present application, after receiving downlink data from a network side device, the radio frequency unit 1101 may transmit it to the processor 1110 for processing; in addition, the radio frequency unit 1101 may send uplink data to the network side device. Generally, the radio frequency unit 1101 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0253] The memory 1109 can be used to store software programs or instructions as well as various data. The memory 1109 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area may store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1109 may include volatile memory or 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 (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct rambus random access memory (DRRAM). The memory 1109 in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0254] The processor 1110 may include one or more processing units; optionally, the processor 1110 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor 1110.

[0255] Among them, the radio frequency unit 1101 is used to send the target identification ID of the artificial intelligence (AI) function to the second device. The target ID includes at least one information field, and different information fields respectively carry different attribute information of the AI function.

[0256] It can be understood that the implementation processes of the implementation manners mentioned in this embodiment can be referred to as Figure 4For the relevant descriptions of the method embodiments shown and to achieve the same or corresponding technical effects, to avoid repetition, they will not be elaborated here.

[0257] An embodiment of the present application further provides a second device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run programs or instructions to implement as Figure 7 the steps of the method embodiment shown. This second device embodiment corresponds to the above-mentioned second device method embodiment, and each implementation process and implementation manner of the above method embodiment can be applied to this second device embodiment, and the same technical effects can be achieved.

[0258] An embodiment of the present application further provides a second device. Figure 12 It is a schematic diagram of the hardware structure of the second device provided by the embodiment of the present application. As Figure 12 shown, the second device 1200 includes: an antenna 121, a radio frequency device 122, a baseband device 123, a processor 124, and a memory 125. The antenna 121 is connected to the radio frequency device 122. In the uplink direction, the radio frequency device 122 receives information through the antenna 121 and sends the received information to the baseband device 123 for processing. In the downlink direction, the baseband device 123 processes the information to be sent and sends it to the radio frequency device 122. After processing the received information, the radio frequency device 122 sends it out through the antenna 121.

[0259] In the above embodiments, the method executed by the second device can be implemented in the baseband device 123, and the baseband device 123 includes a baseband processor.

[0260] The baseband device 123 may include, for example, at least one baseband board, and multiple chips are provided on the baseband board. As Figure 12 shown, one of the chips is, for example, a baseband processor, which is connected to the memory 125 through a bus interface to call the programs in the memory 125 and execute the network device operations shown in the above method embodiments.

[0261] The second device may further include a network interface 126, and this interface is, for example, a Common Public Radio Interface (CPRI).

[0262] Specifically, the second device 1200 of the embodiment of the present application further includes: instructions or programs stored on the memory 125 and executable on the processor 124. The processor 124 calls the instructions or programs in the memory 125 to execute the steps of the method embodiment shown as Figure 7 above, and achieves the same technical effects. To avoid repetition, it will not be elaborated here.

[0263] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned information reporting method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0264] Wherein, the processor is the processor in the first device or the second device in the above-mentioned 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, etc. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0265] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the above-mentioned information reporting method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0266] It should be understood that the chip mentioned in the embodiment of the present application may also be referred to as a system-on-chip, a system chip, a chip system, or a system-on-chip.

[0267] Another embodiment of the present application provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement each process of the above-mentioned information reporting method embodiment and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0268] An embodiment of the present application further provides a wireless communication system, including: a first device and a second device. The first device can be used to execute the steps of the method embodiment as Figure 4 shown, and the second device can be used to execute the steps of the method embodiment as Figure 7 shown.

[0269] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising such element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0270] From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus a necessary general hardware platform, and of course, they can also be implemented by hardware. The computer software products are stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and include several instructions for causing a terminal or a network-side device to execute the methods described in the various embodiments of the present application.

[0271] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms of embodiments without departing from the purpose of the present application and the scope protected by the claims. These embodiments are all within the protection scope of the present application.

Claims

1. An information reporting method, characterized in that, Including: The first device sends a target identifier ID of an artificial intelligence (AI) function to the second device. The target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

2. The information reporting method according to claim 1, wherein The attribute information includes at least one of the following: Tasks or use cases associated with the AI function; Model input type of the AI function; Model input format of the AI function; Model input preprocessing method of the AI function; Model output type of the AI function; Model output format of the AI function; Post-processing method of the model output of the AI function; Computational complexity of the AI function; Inference latency of the AI function; Latency for activation and / or deactivation of the AI function; Performance supervision ability of the AI function; Number of parameters of the AI function; Model structure identifier of the AI function; Quantization method of the AI function; Generated information of the AI function; Validity conditions of the AI function; Inference accuracy or error of the AI function; Number of AI models included in the AI function.

3. The information reporting method according to claim 1 or 2, wherein The length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

4. The information reporting method according to any one of claims 1 to 3, characterized in that If the bits of a target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

5. The information reporting method according to any one of claims 1 to 4, characterized in that The at least one information field includes: A first part, including one or more information fields for carrying public information of the AI function; A second part, including one or more information fields for carrying private information of the AI function, or the second part is used to identify the AI function.

6. The information reporting method according to claim 5, wherein The target ID further includes: A first indication field, used to indicate that the second part includes one or more information fields for carrying private information of the AI function, or used to indicate that the second part is used to identify the AI function.

7. The information reporting method according to claim 6, wherein The method further includes: The first device receives first indication information sent by the second device, and the first indication information is used to indicate the first device to update the second part; The first device updates the second part in the target ID based on the first indication information to obtain an updated second part and an updated target ID; The first device sends the updated second part or the updated target ID to the second device.

8. The information reporting method according to claim 6, wherein The method further includes: The first device receives first information sent by the second device; The first device determines an updated second part and an updated target ID according to the first information.

9. The information reporting method according to claim 8, wherein The method further includes: The first device sends an updated target ID to the second device; the second part in the updated target ID is determined according to the first information; Or, The first device sends second indication information to the second device, and the second indication information is used to indicate that the second part in the updated target ID is determined according to the first information.

10. The information reporting method according to any one of claims 1 to 4, characterized in that The target ID further includes: A second indication field for identifying the AI function.

11. An information reporting method, characterized in that, It includes: The second device receives a target identification ID of an artificial intelligence (AI) function sent by the first device. The target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

12. The information reporting method according to claim 11, wherein The attribute information includes at least one of the following: Tasks or use cases associated with the AI function; Model input type of the AI function; Model input format of the AI function; Model input preprocessing method of the AI function; Model output type of the AI function; Model output format of the AI function; Post-processing method of the model output of the AI function; Computational complexity of the AI function; Inference latency of the AI function; Latency for activation and / or deactivation of the AI function; Performance supervision ability of the AI function; Number of parameters of the AI function; Model structure identification of the AI function; Quantization method of the AI function; Generated information of the AI function; Validity condition of the AI function; Inference accuracy or error of the AI function; Number of AI models included in the AI function.

13. The information reporting method according to claim 11 or 12, characterized in that, The length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

14. The information reporting method according to any one of claims 11 to 13, characterized in that If the bits of a target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

15. The information reporting method according to any one of claims 11 to 14, characterized in that The at least one information field includes: A first part including one or more information fields for carrying public information of the AI function; A second part including one or more information fields for carrying private information of the AI function, or the second part is used to identify the AI function.

16. The information reporting method according to claim 15, wherein The target ID further includes: A first indication field for indicating that the second part includes one or more information fields for carrying private information of the AI function, or for indicating that the second part is used to identify the AI function.

17. The information reporting method according to claim 16, wherein The method further includes: When the second part is used to identify the AI function and the second part is used by other AI functions, the second device sends first indication information to the first device, and the first indication information is used to indicate the first device to update the second part; The second device receives the updated second part or the updated target ID sent by the first device.

18. The information reporting method according to claim 16, characterized in that, The method further includes: When the second part is used to identify the AI function and the second part is used by other AI functions, the second device determines first information; The second device sends the first information to the first device, and the first information is used for the first device to update the second part.

19. The information reporting method according to claim 18, wherein The method further includes: The second device receives the updated target ID sent by the first device; the second part in the updated target ID is determined according to the first information; Or, The second device receives second indication information sent by the first device, where the second indication information is used to indicate that a second part in an updated target ID is determined according to the first information.

20. The information reporting method according to any one of claims 11 to 14, characterized in that The target ID further includes: A second indication field for identifying the AI function.

21. An information reporting device, characterized in that, It includes: A first sending module for sending a target identifier ID of an artificial intelligence (AI) function to a second device, where the target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

22. The information reporting device according to claim 21, wherein The attribute information includes at least one of the following: Tasks or use cases associated with the AI function; Model input type of the AI function; Model input format of the AI function; Model input preprocessing method of the AI function; Model output type of the AI function; Model output format of the AI function; Post-processing method of the model output of the AI function; Computational complexity of the AI function; Inference latency of the AI function; Latency for activation and / or deactivation of the AI function; Performance supervision ability of the AI function; Number of parameters of the AI function; Model structure identifier of the AI function; Quantization method of the AI function; Generated information of the AI function; Validity condition of the AI function; Inference accuracy or error of the AI function; Number of AI models included in the AI function.

23. The information reporting device according to claim 21 or 22, characterized in that, The length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

24. The information reporting device according to any one of claims 21 to 23, characterized in that, If the bits of a target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

25. The information reporting device according to any one of claims 21 to 24, characterized in that The at least one information field includes: A first part including one or more information fields for carrying public information of the AI function; A second part including one or more information fields for carrying private information of the AI function, or the second part is used to identify the AI function.

26. The information reporting device according to claim 25, wherein The target ID further includes: A first indication field for indicating that the second part includes one or more information fields for carrying private information of the AI function, or for indicating that the second part is used to identify the AI function.

27. The information reporting device according to claim 26, wherein The device further includes: A first receiving module for receiving first indication information sent by the second device, where the first indication information is used to indicate that the first device updates the second part; An updating module for updating the second part in the target ID based on the first indication information to obtain an updated second part and an updated target ID; A second sending module for sending the updated second part or the updated target ID to the second device.

28. The information reporting device according to claim 26, wherein The device further includes: A second receiving module for receiving first information sent by the second device; A first determining module for determining an updated second part and an updated target ID according to the first information.

29. The information reporting device according to claim 28, characterized in that, The device further includes: A third sending module for: Send the updated target ID to the second device; the second part of the updated target ID is determined according to the first information; Or, Send second indication information to the second device, where the second indication information is used to indicate that the second part of the updated target ID is determined according to the first information.

30. The information reporting device according to any one of claims 21 to 24, characterized in that The target ID further includes: A second indication field for identifying the AI function.

31. An information reporting device, characterized in that, Includes: A third receiving module, configured to receive a target identifier ID of an artificial intelligence (AI) function sent by a first device, where the target ID includes at least one information field, and different information fields carry different attribute information of the AI function.

32. The information reporting device according to claim 31, wherein The attribute information includes at least one of the following: Tasks or use cases associated with the AI function; Model input type of the AI function; Model input format of the AI function; Model input preprocessing method of the AI function; Model output type of the AI function; Model output format of the AI function; Post-processing method of the model output of the AI function; Computational complexity of the AI function; Inference latency of the AI function; Latency for activation and / or deactivation of the AI function; Performance supervision ability of the AI function; Number of parameters of the AI function; Model structure identifier of the AI function; Quantization method of the AI function; Generated information of the AI function; Validity condition of the AI function; Inference accuracy or error of the AI function; Number of AI models included in the AI function.

33. The information reporting device according to claim 31 or 32, characterized in that The length of the target ID is M bits, where M is a positive integer; the length of each information field and the position of each information field in the target ID are determined by at least one of protocol definition, determination by the first device, and indication by the second device.

34. The information reporting device according to any one of claims 31 to 33, characterized in that, If the bits of the target information field in the at least one information field are all 0, the target information field does not carry the corresponding attribute information.

35. The information reporting device according to any one of claims 31 to 34, characterized in that The at least one information field includes: A first part, including one or more information fields for carrying public information of the AI function; A second part, including one or more information fields for carrying private information of the AI function, or the second part is used to identify the AI function.

36. The information reporting device according to claim 35, wherein The target ID further includes: A first indication field for indicating that the second part includes one or more information fields for carrying private information of the AI function, or for indicating that the second part is used to identify the AI function.

37. The information reporting device according to claim 36, wherein The device further includes: A fourth sending module, configured to send first indication information to the first device when the second part is used to identify the AI function and the second part is used by other AI functions, where the first indication information is used to indicate that the first device updates the second part; A fourth receiving module, configured to receive the updated second part or the updated target ID sent by the first device.

38. The information reporting device according to claim 36, wherein The device further includes: A second determination module, configured to determine first information when the second part is used to identify the AI function and the second part is used by other AI functions. A fifth sending module, configured to send the first information to the first device, where the first information is used for the first device to update the second part.

39. The information reporting device according to claim 38, wherein The apparatus further includes: A fifth receiving module, configured to: Receive an updated target ID sent by the first device; a second part in the updated target ID is determined according to the first information; Or, Receive second indication information sent by the first device, where the second indication information is used to indicate that a second part in an updated target ID is determined according to the first information.

40. The information reporting device according to any one of claims 31 to 34, characterized in that, The target ID further includes: A second indication field, configured to identify the AI function.

41. A first device, characterized in that, Comprising a processor and a memory, where the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information reporting method according to any one of claims 1 to 10 are implemented.

42. A second device, characterized in that, Comprising a processor and a memory, where the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information reporting method according to any one of claims 11 to 20 are implemented.

43. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the information reporting method according to any one of claims 1 to 10 is implemented, or the steps of the information reporting method according to any one of claims 11 to 20 are implemented.

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