Information reporting methods and apparatuses, and first devices and second devices

Abstract

The present application belongs to the technical field of wireless communications. Disclosed are information reporting methods and apparatuses, and first devices and second devices. An information reporting method in an embodiment of the present application comprises: a first device sending a target identifier (ID) of an artificial intelligence (AI) function to a second device, wherein the target ID comprises at least one information field, and different information fields correspondingly carry different attribute information of the AI function.

Description

Information reporting method, device, first device and second device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on December 28, 2023, with application number 202311842436.0 and invention name “Information reporting method, apparatus, first device and second device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] 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

[0004] Artificial intelligence (AI) has been widely applied in various fields. Integrating AI technology into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key research direction for wireless communication networks. Currently, both equipment manufacturers and network vendors are concerned about model privacy issues.

[0005] 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

[0006] The embodiments of the present application provide an information reporting method, apparatus, first device, and second device, which can realize the interaction of AI function information between different network entities.

[0007] In a first aspect, an information reporting method is provided, which is performed by a first device. The method includes:

[0008] 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.

[0009] In a second aspect, an information reporting method is provided, which is performed by a second device, and the method includes:

[0010] The second device receives the 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.

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

[0012] The first sending module is used 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 correspond to different attribute information of the AI ​​function.

[0013] In a fourth aspect, an information reporting device is provided, comprising:

[0014] The third receiving module is used to receive the 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.

[0015] In a fifth aspect, a first device is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0016] In the sixth aspect, a first device is provided, comprising a processor and a communication interface, wherein the communication interface 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.

[0017] In the seventh aspect, a second device is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented.

[0018] In an eighth aspect, a second device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive a target identification ID of an artificial intelligence (AI) function sent by a first device, wherein the target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0019] In the 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.

[0020] In the tenth aspect, a wireless communication system is provided, comprising: a first device and a second device, wherein 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.

[0021] In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0022] In the twelfth aspect, a computer program / program product is provided, 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 to implement the steps of the method described in the second aspect.

[0023] In an embodiment of the present application, a target ID of an AI function is sent to a second device by a first device, and at least one associated information of the AI ​​function is indicated by the target ID, so that the second device can obtain at least one associated information of the AI ​​function by parsing the target ID, thereby realizing the interaction of AI function information between different network entities while taking into account flexibility and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG1 shows a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0025] FIG2 is a schematic diagram of a neural network in the prior art;

[0026] FIG3 is a schematic diagram of a neuron in the prior art;

[0027] FIG4 is a flow chart of one of the information reporting methods provided in an embodiment of the present application;

[0028] FIG5 is a schematic diagram of a target ID structure according to an embodiment of the present application;

[0029] FIG6 is a second schematic diagram of the target ID structure provided in an embodiment of the present application;

[0030] FIG7 is a second flow chart of the information reporting method provided in an embodiment of the present application;

[0031] FIG8 is a schematic diagram of a structure of an information reporting device according to an embodiment of the present application;

[0032] FIG9 is a second structural diagram of an information reporting device according to an embodiment of the present application;

[0033] FIG10 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0034] FIG11 is a schematic diagram of the hardware structure of a first device provided in an embodiment of the present application;

[0035] FIG12 is a schematic diagram of the hardware structure of the second device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0037] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0038] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0039] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0040] FIG1 is a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (Flight Vehicle), a vehicle-mounted device (VUE), a ship-mounted device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AP) or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0041] In order to facilitate a clearer understanding of the technical solutions provided by the embodiments of the present application, some relevant knowledge is first introduced as follows.

[0042] About AI:

[0043] AI technology is currently widely used in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for wireless communication networks. AI modules can be implemented in a variety of ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example for illustration, but does not limit the specific type of AI module.

[0044] FIG2 is a schematic diagram of a neural network in the prior art. As shown in FIG2 , a neural network is composed of neurons. FIG3 is a schematic diagram of a neuron in the prior art. As shown in FIG3 , a1, a2, …, a K is the input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, Tanh, and ReLU (Rectified Linear Unit).

[0045] The parameters of the neural network are optimized using a gradient optimization algorithm. A gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (also called a loss function), which is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, 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, 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 actual situation.

[0046] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0047] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), Adagrad (ADAptive GRADient descent, adaptive gradient descent), Adadelta, RMSprop (root mean square prop, root mean square error reduction), Adam (Adaptive Moment Estimation, adaptive momentum estimation), etc.

[0048] When these optimization algorithms backpropagate errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained by the loss function, add the influence of the learning rate, the previous gradient / derivative / partial derivative, etc., obtain the gradient, and pass the gradient to the previous layer.

[0049] The AI ​​unit / AI model described in the embodiments of this application may 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 may 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 may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a GPU, NPU, TPU, ASIC, etc., and this application does not make specific limitations on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit / AI model.

[0050] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This embodiment of the application does not specifically limit this.

[0051] The information reporting method, apparatus, first device and second device provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through some embodiments and their application scenarios.

[0052] FIG4 is a flow chart of an information reporting method according to an embodiment of the present application. The method is applied to a first device. As shown in FIG4 , the method includes:

[0053] Step 401: The first device sends a target identification ID of an artificial intelligence (AI) function to the second device. The target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0054] Optionally, the first device 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.; the second device may include but is not limited to the types of network side devices 12 listed above, and the embodiments of the present application are not limited to this.

[0055] It should be noted that the AI ​​function may include one or more AI models, or the AI ​​function may also be called an AI model.

[0056] Optionally, in an embodiment 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 a protocol definition, a determination by the first device, and an indication by the second device. In other words, the length of any information field in the target ID and the position of each information field in the target ID can be defined by the protocol, determined by the first device, or indicated by the second device. It is understandable that the lengths of different information fields can be the same or different, and this embodiment of the present application does not limit this.

[0057] Optionally, the information field in the target ID is used to carry attribute information. Different information fields in the target ID correspond to 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 meaning of different information fields corresponds to different attribute information of the AI ​​function.

[0058] Figure 5 is a schematic diagram of the target ID structure provided by an embodiment of the present application. Figure 5 shows that the total length of the target ID is M bits. The target ID includes N information fields, where N is a positive integer. Different information fields correspond to different attribute information carrying AI functions. Figure 5 also shows the number of bits contained in different fields.

[0059] For example, bits 1 to s1 are the first field, indicating the task identifier associated with the AI ​​function; bits s1+1 to s1+s2 are the second field, indicating the model input type of the AI ​​function; and so on.

[0060] Optionally, if the bits of the target information field in at least one information field in the target ID are all 0, it indicates that the target information field does not carry the corresponding attribute information. For example, if the bits of an information field in the target ID (such as the target information field) are all 0, it indicates 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.

[0061] Optionally, the attribute information of the AI ​​function may include at least one of the following:

[0062] a) The tasks or use cases to which the AI ​​functionality is associated.

[0063] b) Model input type for AI functions.

[0064] c) Model input format for AI functions; for example, the model input format is a matrix of size N*M, where M and N are both positive integers.

[0065] d) Model input preprocessing methods for AI functions; preprocessing methods, such as 0-1 normalization, are used to normalize the model input information so that the maximum value is 1 and the minimum value is 0.

[0066] e) Model output type of AI function.

[0067] f) Model output format of the AI ​​function; for example, the model output format is a Z*1 vector, where Z is a positive integer.

[0068] g) Post-processing methods for the model output of the AI ​​function; post-processing methods such as coordinate transformation or unit transformation.

[0069] h) Computational complexity of the AI ​​function; the computational complexity is, for example, T Flops.

[0070] i) AI function inference latency, for example, 1ms.

[0071] j) The activation and / or deactivation delay of the AI ​​function, for example 1 ms.

[0072] k) Performance monitoring capability of AI functions, for example: whether the first device does not rely on network-side devices for performance monitoring.

[0073] l) Parameter quantity of the AI ​​function; for example, T Flops.

[0074] m) Model structure identifier of the AI ​​function; model structure identifier, such as fully connected, convolutional, or transformer structure.

[0075] n) Quantization method of AI functions; quantization methods such as int8, float32, or float64.

[0076] o) Generation information of the AI ​​function; generation information such as version number, vendor information, or identification of the model training unit.

[0077] p) Validity conditions of the AI ​​function; validity conditions such as the cell ID, TRP ID, scenario ID or channel conditions associated with the AI ​​function (such as Signal to Interference Noise Ratio (SINR) distribution, Reference Signal Received Power (RSRP) distribution), etc.

[0078] q) The inference accuracy or error of AI functions.

[0079] r) The number of AI models included in the AI ​​function.

[0080] Here, the attribute information of the AI ​​function is explained through several examples.

[0081] Example 1: The task or use case associated with the AI ​​function is AI-based positioning: using AI models or functions to determine the location-related information of the terminal.

[0082] Model input types: channel impulse response, power delay profile, delay profile, RSRP, etc.

[0083] Model output types: location information, arrival time, relative arrival time, line of sight (LOS) / non-line of sight (NLOS) indication, etc.

[0084] Example 2: The task or use case associated with the AI ​​function is AI-based beam management: using AI models or functions to predict future beam information based on past beam information.

[0085] Model input type: beam ID, RSRP, beam angle, etc. of the past N beams.

[0086] Model output type: beam ID, RSRP, beam angle, etc. of the future M beams.

[0087] Example 3: The task or use case associated with the AI ​​function is AI-based channel state information (CSI) prediction: using AI models or functions to predict future channel state information based on past channel state information.

[0088] Model input type: CSI of the past N moments.

[0089] Model output type: CSI for the next N time periods.

[0090] Example 4: The task or use case associated with the AI ​​function is AI-based CSI compression: CSI compression and decompression are performed using AI models or functions.

[0091] For compression: the model input type is uncompressed CSI; the model output type is compressed CSI.

[0092] For decompression: the model input type is compressed CSI; the model output type is restored CSI.

[0093] Example 5: The task or use case associated with the AI ​​function is AI-based mobility management: using the AI ​​model to predict the future RSRP based on the terminal's past RSRP, or to predict the future cell switching status based on the past RSRP, etc.

[0094] Model input types: RSRP, signal-to-noise ratio (SNR), SINR, etc. for the past N time periods.

[0095] Model output types: RSRP, SNR, SINR, cell switching decision, etc. at the next M time points.

[0096] In an embodiment of the present application, a target ID of an AI function is sent to a second device by a first device, and at least one associated information of the AI ​​function is indicated by the target ID, so that the second device can obtain at least one associated information of the AI ​​function by parsing the target ID, thereby realizing the interaction of AI function information between different network entities while taking into account flexibility and privacy.

[0097] Optionally, in the embodiment of the present application, the at least one information field included in the target ID may include:

[0098] The first part includes one or more information fields for carrying public information of the AI ​​function;

[0099] The second part includes one or more information fields for carrying private information of the AI ​​function, or the second part is used to identify the AI ​​function.

[0100] It should be noted that the information field in the target ID is used to carry attribute information. Public information of the AI ​​function refers to one or more attribute information of the AI ​​function that the first device needs to disclose. Public information of the AI ​​function includes, for example, at least one of the following: 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.

[0101] Private information of an AI function refers to one or more attributes of the AI ​​function that the first device can selectively disclose. For example, private information of an AI function includes at least one of information about the generation of the AI ​​function, validity conditions for the AI ​​function, a pre-processing method for the model input of the AI ​​function, and a post-processing method for the model output of the AI ​​function.

[0102] Optionally, the second portion of the target ID may include one or more information fields for carrying private information about the AI ​​function, meaning the second portion represents the private information about the AI ​​function. Alternatively, the second portion of the target ID may be used to identify the AI ​​function, meaning the second portion only distinguishes different AI functions and does not represent attribute information.

[0103] Optionally, in this embodiment of the present application, the target ID further includes a first indication field, the first indication field being used to indicate that the second portion includes one or more information fields for carrying private information of the AI ​​function, or the first indication field being used to indicate that the second portion is used to identify the AI ​​function. This embodiment of the present application uses the first indication field in the target ID to indicate the function of the second portion of the target ID.

[0104] Figure 6 is a second schematic diagram of the target ID structure provided by an embodiment of the present application. In the target ID shown in Figure 6, the first indicator field is 1 bit, indicating the function of the second part. This first indicator field is an additional 1 bit in addition to the first and second parts. For example, if the first indicator field is 1, it means that the second part represents private information of the AI ​​function; if the first indicator field is 0, it means that the second part is used to identify the AI ​​function, that is, to distinguish different AI functions.

[0105] Optionally, the first indication field may not be indicated separately within the target ID.

[0106] Optionally, in a scenario where the second part of the target ID is used to identify an AI function, there may be a situation where different first devices report target IDs of different AI functions to the second device respectively. However, the second parts of the target IDs of different AI functions reported by different first devices may be identical or repeated, resulting in an inability to distinguish different AI functions through the second part. To address this problem, in an embodiment of the present application, when the second part of 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 the second device determines that the second part or the target ID cannot be used for other reasons, operation a and / or operation b is performed, wherein:

[0107] Operation a: The second device sends first indication information to the first device, where 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.

[0108] Accordingly, the first device receives the first indication information sent by the second device; based on the first indication information, the first device updates the second part of the target ID to obtain an updated second part and an updated target ID; and the first device sends the updated second part or the updated target ID to the second device. For example, the first device re-acquires the second part based on the first indication information.

[0109] The second device receives the updated second part or the updated target ID sent by the first device.

[0110] Operation b: The second device determines first information, which is used by 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 is not used by the second part of the target ID of the AI ​​function reported by another first device.

[0111] Correspondingly, the first device receives the first information sent by the second device, and the first device determines the updated second part and the updated target ID based on the first information. For example, the first device determines the first information as the updated second part.

[0112] Furthermore, the first device sends an updated target ID to the second device, wherein the second part of the updated target ID is determined according to the first information, and the second device receives the updated target ID sent by the first device.

[0113] 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 of the updated target ID is determined according to the first information. The second device receives the second indication information sent by the first device.

[0114] Optionally, the target ID may further include a second indication field, which 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.

[0115] In the embodiment 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 the different information fields in the target ID correspond to different attribute information of the AI ​​function, and the length of each information field and the position of each information field in the target ID are determined by at least one of the protocol definition, the determination of the first device and the indication of the second device. The target ID can be composed in the following ways: 1) All are private information, but the first device can selectively expose certain attribute information of the AI ​​function, and the attribute information that is not willing to be exposed is masked in the corresponding information field. For example, the information field can be all 0, indicating that the corresponding attribute information is not carried. 2) Public information + private information; wherein, for public information, the first device must provide it; for private information, the first device can selectively provide it.

[0116] FIG7 is a second flow chart of the information reporting method provided in an embodiment of the present application. The method is applied to the second device. As shown in FIG7 , the method includes:

[0117] Step 701: 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 correspond to different attribute information of the AI ​​function.

[0118] In an embodiment of the present application, a target ID of an AI function sent by a second device is received by a second device, and the target ID is used to indicate at least one associated information of the AI ​​function. Thus, by parsing the target ID, at least one associated information of the AI ​​function is obtained, thereby realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0119] Optionally, the attribute information includes at least one of the following:

[0120] The tasks or use cases associated with the AI ​​functionality;

[0121] Model input type for the AI ​​function;

[0122] Model input format for the AI ​​function;

[0123] Model input preprocessing method for the AI ​​function;

[0124] The model output type of the AI ​​function;

[0125] The model output format of the AI ​​function;

[0126] Post-processing of the model output of the AI ​​function;

[0127] The computational complexity of the AI ​​function;

[0128] The inference latency of the AI ​​function;

[0129] The activation and / or deactivation delay of the AI ​​function;

[0130] Ability to monitor the performance of said AI functionality;

[0131] The number of parameters of the AI ​​function;

[0132] The model structure identifier of the AI ​​function;

[0133] How the AI ​​functionality is quantified;

[0134] Generation information of the AI ​​function;

[0135] The validity conditions of the AI ​​function;

[0136] the reasoning accuracy or error of the AI ​​function;

[0137] The number of AI models included in the AI ​​function.

[0138] 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 the protocol definition, the first device determination, and the second device indication.

[0139] 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 corresponding attribute information.

[0140] Optionally, the at least one information field includes:

[0141] The first part includes one or more information fields for carrying public information of the AI ​​function;

[0142] The second part includes one or more information fields for carrying private information of the AI ​​function, or the second part is used to identify the AI ​​function.

[0143] Optionally, the target ID also includes:

[0144] The first indication field 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.

[0145] Optionally, the method further includes:

[0146] 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, where the first indication information is used to instruct the first device to update the second part;

[0147] The second device receives the second part of the update or the updated target ID sent by the first device.

[0148] Optionally, the method further includes:

[0149] The second device determines the first information when the second portion is used to identify the AI ​​function and the second portion is used by other AI functions;

[0150] The second device sends the first information to the first device, where the first information is used by the first device to update the second part.

[0151] Optionally, the method further includes:

[0152] The second device receives the updated target ID sent by the first device, wherein the second part of the updated target ID is determined according to the first information;

[0153] or,

[0154] The second device receives second indication information sent by the first 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.

[0155] Optionally, the target ID also includes: a second indication field, used to identify the AI ​​function.

[0156] The embodiment of the present application further provides an information reporting method, which is performed by a first device and a second device in cooperation. The method includes steps a and b, wherein:

[0157] Step a: The first device sends a target ID of the 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.

[0158] Step b: The second device receives the 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.

[0159] In an embodiment of the present application, a target ID of an AI function is sent to a second device through a first device. The target ID is used to indicate at least one associated information of the AI ​​function, so that the second device can obtain at least one associated information of the AI ​​function by parsing the target ID, thereby realizing the interaction of AI function information between different network entities while taking into account flexibility and privacy.

[0160] The information reporting method provided in the embodiment of the present application can be executed by an information reporting device. In the embodiment of the present application, the information reporting device performing the information reporting method is taken as an example to illustrate the information reporting device provided in the embodiment of the present application.

[0161] FIG8 is a schematic diagram of a structure of an information reporting apparatus provided in an embodiment of the present application. As shown in FIG8 , the information reporting apparatus 800 is applied to a first device, and the information reporting apparatus 800 includes:

[0162] The first sending module 801 is used to send a target identification ID of an artificial intelligence (AI) function to a second device. The target ID includes at least one information field, and different information fields correspond to different attribute information of the AI ​​function.

[0163] In an embodiment of the present application, by sending the target ID of the AI ​​function to the second device, and indicating at least one associated information of the AI ​​function through the target ID, the second device can obtain at least one associated information of the AI ​​function by parsing the target ID, thereby realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0164] Optionally, the attribute information includes at least one of the following:

[0165] The tasks or use cases associated with the AI ​​functionality;

[0166] Model input type for the AI ​​function;

[0167] Model input format for the AI ​​function;

[0168] Model input preprocessing method for the AI ​​function;

[0169] The model output type of the AI ​​function;

[0170] The model output format of the AI ​​function;

[0171] Post-processing of the model output of the AI ​​function;

[0172] The computational complexity of the AI ​​function;

[0173] The inference latency of the AI ​​function;

[0174] The activation and / or deactivation delay of the AI ​​function;

[0175] Ability to monitor the performance of said AI functionality;

[0176] The number of parameters of the AI ​​function;

[0177] The model structure identifier of the AI ​​function;

[0178] How the AI ​​functionality is quantified;

[0179] Generation information of the AI ​​function;

[0180] The validity conditions of the AI ​​function;

[0181] the reasoning accuracy or error of the AI ​​function;

[0182] The number of AI models included in the AI ​​function.

[0183] 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 the protocol definition, the first device determination, and the second device indication.

[0184] 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 corresponding attribute information.

[0185] Optionally, the at least one information field includes:

[0186] The first part includes one or more information fields for carrying public information of the AI ​​function;

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

[0188] Optionally, the target ID also includes:

[0189] The first indication field 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.

[0190] Optionally, the device further comprises:

[0191] a first receiving module, configured to receive first indication information sent by the second device, where the first indication information is used to instruct the first device to update the second part;

[0192] an updating module, configured to update the second portion of the target ID based on the first indication information, to obtain an updated second portion and an updated target ID;

[0193] The second sending module is configured to send the second part of the update or the target ID of the update to the second device.

[0194] Optionally, the device further comprises:

[0195] a second receiving module, configured to receive the first information sent by the second device;

[0196] The first determining module is configured to determine the updated second part and the updated target ID according to the first information.

[0197] Optionally, the device further comprises:

[0198] The third sending module is used to:

[0199] Sending an updated target ID to the second device, wherein the second part of the updated target ID is determined based on the first information;

[0200] or,

[0201] Second indication information is sent 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.

[0202] Optionally, the target ID also includes:

[0203] The second indication field is used to identify the AI ​​function.

[0204] The information reporting device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or chip. The electronic device can be a first device, or it can be another device other than the first device. For example, the first device can include but is not limited to the types of terminals 11 listed above, and the other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0205] The information reporting device provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0206] FIG9 is a second structural diagram of an information reporting apparatus provided in an embodiment of the present application. As shown in FIG9 , the information reporting apparatus 900 is applied to a second device. The information reporting apparatus 900 includes:

[0207] The third receiving module 901 is used 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 correspond to different attribute information of the AI ​​function.

[0208] In an embodiment 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 associated information of the AI ​​function, so that by parsing the target ID, at least one associated information of the AI ​​function is obtained, thereby realizing the interaction of AI function information between different network entities, while taking into account flexibility and privacy.

[0209] Optionally, the attribute information includes at least one of the following:

[0210] The tasks or use cases associated with the AI ​​functionality;

[0211] Model input type for the AI ​​function;

[0212] Model input format for the AI ​​function;

[0213] Model input preprocessing method for the AI ​​function;

[0214] The model output type of the AI ​​function;

[0215] The model output format of the AI ​​function;

[0216] Post-processing of the model output of the AI ​​function;

[0217] The computational complexity of the AI ​​function;

[0218] The inference latency of the AI ​​function;

[0219] The activation and / or deactivation delay of the AI ​​function;

[0220] Ability to monitor the performance of said AI functionality;

[0221] The number of parameters of the AI ​​function;

[0222] The model structure identifier of the AI ​​function;

[0223] How the AI ​​functionality is quantified;

[0224] Generation information of the AI ​​function;

[0225] The validity conditions of the AI ​​function;

[0226] the reasoning accuracy or error of the AI ​​function;

[0227] The number of AI models included in the AI ​​function.

[0228] 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 the protocol definition, the first device determination, and the second device indication.

[0229] 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 corresponding attribute information.

[0230] Optionally, the at least one information field includes:

[0231] The first part includes one or more information fields for carrying public information of the AI ​​function;

[0232] The second part includes one or more information fields for carrying private information of the AI ​​function, or the second part is used to identify the AI ​​function.

[0233] Optionally, the target ID also includes:

[0234] The first indication field 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.

[0235] Optionally, the device further comprises:

[0236] a fourth sending module, configured to, when the second portion is used to identify the AI ​​function and the second portion is used by other AI functions, send first indication information to the first device, wherein the first indication information is used to instruct the first device to update the second portion;

[0237] The fourth receiving module is configured to receive the second part of the update or the updated target ID sent by the first device.

[0238] Optionally, the device further comprises:

[0239] a second determining module, configured to determine first information when the second portion is used to identify the AI ​​function and the second portion is used by another AI function;

[0240] A fifth sending module is 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.

[0241] Optionally, the device further comprises:

[0242] The fifth receiving module is configured to:

[0243] receiving an updated target ID sent by the first device, wherein the second part of the updated target ID is determined based on the first information;

[0244] or,

[0245] Receive second indication information sent by the first device, where the second indication information is used to indicate that a second part of the updated target ID is determined based on the first information.

[0246] Optionally, the target ID also includes: a second indication field, used to identify the AI ​​function.

[0247] The information reporting device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or chip. The electronic device can be a second device, or it can be another device other than the second device. For example, the second device can include but is not limited to the types of network-side devices 12 listed above. Other devices can be servers, network attached storage (NAS), etc., and the embodiment of the present application does not specifically limit this.

[0248] The information reporting device provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 7 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0249] The embodiment of the present application also provides a communication device. FIG10 is a schematic diagram of the structure of the communication device provided by the embodiment of the present application. As shown in FIG10 , the embodiment of the present application also provides a communication device 1000, including a processor 1001 and a memory 1002. The memory 1002 stores a program or instruction that can be run on the processor 1001. For example, when the communication device 1000 is a first device, the program or instruction is executed by the processor 1001 to implement the various steps of the method embodiment shown in FIG4 above, and can achieve the same technical effect. When the communication device 1000 is a second device, the program or instruction is executed by the processor 1001 to implement the various steps of the method embodiment shown in FIG7 above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0250] The present application also provides a first device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps in the method embodiment shown in FIG4 . This first device embodiment corresponds to the first device method embodiment described above, and each implementation process and implementation method of the method embodiment described above are applicable to this first device embodiment and can achieve the same technical effects.

[0251] The present application also provides a first device. Figure 11 is a schematic diagram of the hardware structure of the first device provided in the present application. As shown in Figure 11, the first device 1100 includes, but is not limited to, at least some of the components of 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.

[0252] Those skilled in the art will appreciate that the first device 1100 may further include a power source (such as a battery) to power various components. The power source may be logically connected to the processor 1110 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The first device structure shown in FIG11 does not limit the first device. The first device may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0253] It should be understood that in an embodiment of the present application, the input unit 1104 may include a graphics processing unit (GPU) 11041 and a microphone 11042, and the graphics processing unit 11041 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 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1107 includes a touch panel 11071 and at least one of other input devices 11072. The touch panel 11071 is also called a touch screen. The touch panel 11071 may include two parts: a touch detection device and a touch controller. 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 an operating stick, which will not be repeated here.

[0254] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1101 may transmit the data to the processor 1110 for processing. Furthermore, the RF unit 1101 may send uplink data to the network-side device. Typically, the RF unit 1101 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0255] The memory 1109 can be used to store software programs or instructions and 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, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1109 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 random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1109 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0256] Processor 1110 may include one or more processing units. Optionally, processor 1110 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 1110.

[0257] Among them, the radio frequency unit 1101 is used to send a target identification ID of the 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.

[0258] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment shown in Figure 4, and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0259] The present application also provides a second device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG7 . This second device embodiment corresponds to the aforementioned second device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this second device embodiment and can achieve the same technical effects.

[0260] The embodiment of the present application also provides a second device. Figure 12 is a schematic diagram of the hardware structure of the second device provided in the embodiment of the present application. As shown in Figure 12, 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. The radio frequency device 122 processes the received information and sends it out through the antenna 121.

[0261] The method executed by the second device in the above embodiment may be implemented in the baseband device 123 , which includes a baseband processor.

[0262] The baseband device 123 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 12, one of the chips is, for example, a baseband processor, which is connected to the memory 125 through a bus interface to call the program in the memory 125 and execute the network device operations shown in the above method embodiment.

[0263] The second device may further include a network interface 126 , which is, for example, a Common Public Radio Interface (CPRI).

[0264] Specifically, the second device 1200 of the embodiment of the present application also includes: instructions or programs stored in 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 in Figure 7 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0265] 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 reporting method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0266] The processor is the processor in the first device or the processor in the second device in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0267] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned information reporting method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0268] 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.

[0269] An embodiment of the present application further 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 the various processes of the above-mentioned information reporting method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0270] An embodiment of the present application also provides a wireless communication system, including: a first device and a second device, wherein the first device can be used to execute the steps of the method embodiment shown in Figure 4, and the second device can be used to execute the steps of the method embodiment shown in Figure 7.

[0271] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0272] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0273] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A method for information reporting, wherein, Including: A first device sends 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.

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 of 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.

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, wherein, 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, wherein, 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, where 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, obtaining 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, 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.

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

11. A method for information reporting, wherein, Including: 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 types of the AI function; Model input formats of the AI function; Model input preprocessing methods of the AI function; Model output types of the AI function; Model output formats of the AI function; Post-processing methods 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 methods 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.

13. The information reporting method according to claim 11 or 12, 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.

14. The information reporting method according to any one of claims 11 to 13, wherein, 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.

15. The information reporting method according to any one of claims 11 to 14, wherein, 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 instruct 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, 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 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 the 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.

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

21. An information reporting device, wherein, including: 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, 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.

24. The information reporting device according to any one of claims 21 to 23, wherein, 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.

25. The information reporting device according to any one of claims 21 to 24, wherein, 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 the 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 update 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 the first information sent by the second device; A first determination module for determining the updated second part and the updated target ID according to the first information.

29. The information reporting device according to claim 28, wherein, 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, wherein, The target ID further includes: A second indication field for identifying the AI function.

31. An information reporting device, wherein, Including: 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 respectively 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; The model input type of the AI function; The model input format of the AI function; The model input preprocessing method of the AI function; The model output type of the AI function; The model output format of the AI function; The post-processing method of the model output of the AI function; The computational complexity of the AI function; The inference latency of the AI function; The latency of activation and / or deactivation of the AI function; The performance supervision ability of the AI function; The number of parameters of the AI function; The model structure identifier of the AI function; The quantization method of the AI function; The generated information of the AI function; The validity condition of the AI function; The inference accuracy or error of the AI function; The number of AI models included in the AI function.

33. The information reporting device according to claim 31 or 32, 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.

34. The information reporting device according to any one of claims 31 to 33, wherein, 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, wherein, 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 device further includes: A fifth receiving module, configured to: 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; Or, 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.

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

41. A first device, wherein, Comprising a processor and a memory, where the memory stores a program or instructions that can be run on the processor, and when the program or instructions are 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, wherein, Comprising a processor and a memory, where the memory stores a program or instructions that can be run on the processor, and when the program or instructions are 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, wherein, The program or instructions are stored on the readable storage medium, and when the program or instructions are executed by the 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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