Artificial intelligence model training method and communication apparatus

Abstract

The present application relates to the technical field of communications, and provides an artificial intelligence model training method and a communication apparatus. In the artificial intelligence model training method, a parameter, which needs to be updated, of a first model can be determined on the basis of acquired identification information of a first scenario, and the parameter, which needs to be updated, of the first model is updated, but not all parameters are updated, thereby effectively reducing the overall overhead of model updating.

Description

Artificial intelligence model training method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on September 28, 2023, with application number 202311294251.0 and application name “Training Method and Communication Device for Artificial Intelligence Model”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and more specifically, to a training method and communication device for an artificial intelligence model. Background Art

[0003] Artificial intelligence (AI) has been widely used in the field of communication technology in order to improve the communication performance between terminal devices and network devices.

[0004] In specific AI applications, AI models can be applied to certain scenarios. When an AI model switches from one scenario to another, in order to enable the AI ​​model to be applied to the newly switched scenario, data for the newly switched scenario can be obtained and used to update the AI ​​model to obtain an updated AI model that can be applied to the newly switched scenario. However, the above solution may result in a high overall cost for model updates.

[0005] Summary of the Invention

[0006] The present application provides a training method and communication device for an artificial intelligence model, in order to reduce the overall overhead of model updates.

[0007] In a first aspect, a training method for an artificial intelligence model is provided, comprising: obtaining identification information of a first scene; determining a first parameter in the first model based on the identification information of the first scene, wherein the first parameter is used to determine a parameter that needs to be updated in the first model.

[0008] The solution described in the first aspect can be applied to a model training network element or a model deployment network element, for example, it can be executed by a model training network element or a model deployment network element or a chip or module used for a model training network element or a model deployment network element.

[0009] By determining the parameters that need to be updated in the first model based on the identification information of the scene, it is helpful to update the parameters that need to be updated in the first model, and there is no need to update all the parameters of the first model. In this way, the overall cost of model update can be effectively reduced.

[0010] A possible implementation method is to determine the first parameter in the first model based on the identification information of the first scene, including: determining the first parameter based on the identification information of the first scene and at least one association relationship, wherein the first association relationship in the at least one association relationship is used to associate the first parameter with the first scene.

[0011] By setting association relationships between multiple different scenes and parameters in the first model, and determining the first parameter associated with the first scene based on the identification information of the first scene and at least one of the above-mentioned association relationships, the parameters that need to be updated in the first model can be determined.

[0012] In a possible implementation manner, at least one association relationship is predefined or configured.

[0013] In this way, the signaling overhead for the association relationship between the interaction scenario and the parameters in the first model can be saved.

[0014] In a possible implementation manner, the method further includes: transmitting the first parameter.

[0015] In this way, other network elements can determine the parameters that need to be updated in the first model.

[0016] In a possible implementation, the method further includes: determining, based on identification information of the first scene, that the first scene does not belong to scenes currently supported by the first model; and transmitting a message for instructing to obtain data of the first scene.

[0017] The data of the first scenario can be used to update the first model, and the updated first model can be applied to the first scenario.

[0018] In this way, the data of the first scene can be obtained so that the first model can be updated and trained using the data of the first scene.

[0019] In a possible implementation, the method further includes: acquiring first data, the first data being related to the first scenario; and using the first data to update parameters that need to be updated in the first model to obtain an updated first model.

[0020] For example, the scenarios related to the first scenario may be: scenarios with the same or similar data features as the first scenario, or scenarios of the same type as the first scenario; or scenarios with similar or identical scene features as the first scenario. Similarity means that both satisfy the same condition, such as being below a certain threshold, or above a certain threshold, or similarity means that the difference is below a certain threshold. Specific thresholds, feature information, or one or more of the data features may be predefined or configured and are not limited herein.

[0021] Since the first data is related to the first scenario, the first model is updated using the first data, and the obtained updated first model can support the first scenario.

[0022] In one possible implementation, the first data includes any one of the following: data of the first scene, data of the first scene and data of scenes related to the first scene, or data generated based on the data characteristics of the first scene and the second data, the second data is data of the scene currently supported by the first model, and the scene related to the first scene belongs to the scene currently supported by the first model.

[0023] Through one or more of the above-mentioned items, the present application supports completing the update training of the parameters that need to be updated in the first model, so that the updated first model can be applied to the first scenario.

[0024] A possible implementation method of obtaining the first data includes: obtaining the first data from a data collection network element, where the first data is data of a first scene; or, obtaining the first data from a data generation network element, where the first data is data generated based on data characteristics of the first scene and second data; or, obtaining the first data from a data storage network element, where the first data is data of the first scene and data of scenes related to the first scene.

[0025] In this way, the present application supports obtaining data for updating training of parameters that need to be updated in the first model through multiple approaches.

[0026] In a possible implementation, the parameter that needs to be updated in the first model is the first parameter, or the parameter that needs to be updated in the first model is part or all of the parameters in the first model except the first parameter.

[0027] When the parameter that needs to be updated in the first model is the first parameter, the present application supports directly determining the parameter that needs to be updated in the first model; when the parameter that needs to be updated in the first model is part or all of the parameters in the first model other than the first parameter, the present application supports indicating the parameters that need to be updated in the first model with lower signaling overhead.

[0028] In one possible implementation, the data feature of the second data includes at least one of the following: signal to noise ratio (SNR), signal to interference plus noise ratio (SINR), reference signal received power (RSRP), or data distribution.

[0029] Among them, the above-mentioned data distribution may include one or more of the following situations: the latent variable variational probability distribution of the data after variational inference, the probability distribution of the data after fitting the Gaussian mixture model, and the potential distribution of the data obtained through adversarial learning).

[0030] In this way, through one or more parameters listed above, the present application supports generating a larger amount of data that can be used for updating the first model.

[0031] In a possible implementation, the method further includes: determining, based on the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model.

[0032] When it is determined based on the identification information of the first scene that the first scene does not belong to the scenes currently supported by the first model, the present application can initiate the data collection process for the first scene, and use the data related to the first scene to update the parameters that need to be updated in the first model for training, so that the updated first model can support the first scene.

[0033] On the second aspect, a training method for an artificial intelligence model is provided, including: obtaining identification information of a first scene; determining scenes related to the first scene based on the identification information of the first scene, the scenes related to the first scene belonging to scenes currently supported by the first model; obtaining quantity information of data of scenes related to the first scene, the data of scenes related to the first scene being used for updating the first model, and the updated first model supports the first scene.

[0034] The solution described in the second aspect can be applied to a model training network element or a model deployment network element, for example, executed by a model training network element or a model deployment network element or a chip or module used for a model training network element or a model deployment network element.

[0035] For example, the scenarios related to the first scenario may be: scenarios with the same or similar data features as the first scenario, or scenarios of the same type as the first scenario; or scenarios with similar or identical scene features as the first scenario. Similarity means that both satisfy the same condition, such as being below a certain threshold, or above a certain threshold, or similarity means that the difference is below a certain threshold. Specific thresholds, feature information, or one or more of the data features may be predefined or configured and are not limited herein.

[0036] By determining the scenes related to the first scene based on the identification information of the first scene and obtaining the quantity information of the data of the scenes related to the first scene, this is conducive to reducing the amount of data that needs to be obtained from the first scene, thereby reducing the overhead of data collection and further reducing the overall overhead of model updates.

[0037] In one possible implementation, when the second aspect is applied to a model training network element, the method may further include: obtaining scene data related to the first scene. In this way, the model training network element can perform training based on stored scene data related to the first scene, effectively utilizing the stored or acquired data, thereby reducing the amount of data to be acquired for the first scene and accelerating model updates.

[0038] In a possible implementation method, the method also includes: determining that the first scene does not belong to the scenes currently supported by the first model based on the identification information of the first scene; sending a first indication message, the first indication message is used to indicate the amount of data to be acquired in the first scene, the quantity information of the data of the scenes related to the first scene is used to determine the amount of data to be acquired in the first scene, and the data of the first scene is used to update the first model.

[0039] In this way, the amount of data that needs to be acquired from the first scene may be indicated.

[0040] In a possible implementation, the above-mentioned determining the scene related to the first scene based on the identification information of the first scene includes: determining based on a predefined rule, or determining based on characteristic information of the scene.

[0041] In this way, scenes related to the first scene can be determined.

[0042] In a possible implementation, the first indication information is further used to instruct acquisition of data of the first scene.

[0043] In this way, data of the first scene can be obtained.

[0044] On the third aspect, a training method for an artificial intelligence model is provided, including: obtaining identification information of a first scene; determining, based on the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model; transmitting first indication information, the first indication information being used to indicate obtaining data of the first scene, the data of the first scene being used to update the first model, and the updated first model supporting the first scene.

[0045] The solution described in the third aspect can be applied to a model deployment network element, for example, executed by a model deployment network element or a chip or module used for a model deployment network element.

[0046] By determining whether it is necessary to obtain data for the first scene based on the identification information of the scene, this helps to avoid repeated data collection. Moreover, when it is determined that it is necessary to obtain data for the first scene, it can be indicated to obtain data for the first scene, and the data for the first scene can be used to update and train the first model, so that the updated first model can support the first scene.

[0047] In addition, through the above solution, the present application can avoid the situation where the first model is updated and trained when there is no need to update and train the first model, thereby effectively reducing the overall cost of model updating.

[0048] In a fourth aspect, a training method for an artificial intelligence model is provided, including: obtaining data of a first scene; generating data based on the data of the first scene and the data features of the second data to obtain first data, the second data being the data of the scene currently supported by the first model, the first data being used to update the first model, and the updated first model supporting the first scene; and sending the first data.

[0049] The solution described in the fourth aspect can be applied to a data generation network element, for example, executed by the data generation network element or a chip or module used for the data generation network element.

[0050] By generating the first data based on the data of the first scene and the data features of the second data, and without the need to obtain a large amount of data in the first scene, the present application can support the updated first model to be used not only for the first scene, but also for updating the previously supported scenes, which can effectively reduce the overhead of data collection, and thus can also effectively reduce the overall overhead of model updates.

[0051] In a possible implementation manner, the data feature of the second data includes at least one of the following: SNR, SINR, RSRP, or data distribution.

[0052] Among them, the above-mentioned data distribution may include one or more of the following situations: the latent variable variational probability distribution of the data after variational inference, the probability distribution of the data after fitting the Gaussian mixture model, and the potential distribution of the data obtained through adversarial learning).

[0053] In this way, through one or more parameters listed above, the present application supports generating a larger amount of data that can be used for updating the first model.

[0054] In a fifth aspect, a training method for an artificial intelligence model is provided, comprising: determining identification information of a first scene; and sending identification information of the first scene.

[0055] The solution described in the fifth aspect can be applied to a data collection network element, for example, executed by the data collection network element or a chip or module for the data collection network element. In one possible implementation, determining the identification information of the first scene may include: obtaining the identification information of the first scene or sensing the identification information of the first scene, which is not limited to this.

[0056] By reporting the identification information of the first scenario, other network elements or devices can determine whether it is necessary to start update training of the first model and whether it is necessary to start the data collection process for the first scenario based on the identification information of the first scenario, thereby effectively reducing the overall overhead of model updates.

[0057] In a possible implementation, the method further includes: receiving instruction information, where the instruction information is used to instruct to obtain data of the first scene; and obtaining the data of the first scene.

[0058] In this way, data of the first scene can be obtained according to the instruction.

[0059] In a possible implementation, the indication information includes information about the amount of data to be acquired in the first scenario.

[0060] In this way, there is no need to obtain a large amount of data from the first scenario, which is conducive to reducing the cost of data collection, thereby effectively supporting the reduction of the overall cost of model updating.

[0061] In a possible implementation, the obtaining of the data of the first scene includes: obtaining the data of the first scene according to information on the amount of data to be obtained in the first scene.

[0062] In this way, data of the first scene may be acquired according to the indicated amount of data to be acquired in the first scene.

[0063] In a sixth aspect, a training method for an artificial intelligence model is provided, including: obtaining indication information, the indication information being used to indicate obtaining data of a scene related to a first scene, the indication information including identification information of the scene related to the first scene; determining the data of the scene related to the first scene based on the identification information of the scene related to the first scene and at least one association relationship, the first association relationship in the at least one association relationship being used to associate the scene related to the first scene with the data of the scene related to the first scene; and transmitting the data of the scene related to the first scene.

[0064] The solution described in the sixth aspect can be applied to a data storage network element, for example, executed by a data storage network element or a chip or module used for a data storage network element.

[0065] Optionally, the scene related to the first scene may be: a scene having the same or similar data features as the first scene, or a scene of the same type as the first scene; or a scene having similar or identical scene features as the first scene. Similarity means that both satisfy the same condition, for example, both are below a certain threshold, or above a certain threshold, or similarity means that the difference is below a certain threshold. Specific thresholds, feature information, or one or more of the data features may be predefined or configured and are not limited herein.

[0066] By sending data of scenes related to the first scene, the data of scenes related to the first scene can be used for updating and training the first model, which can effectively utilize the stored data and reduce the amount of data that needs to be obtained from the first scene, and can effectively reduce the overhead of data collection, thereby supporting the reduction of the overall overhead of model updates.

[0067] In a possible implementation, the at least one association relationship is predefined or configured.

[0068] In this way, the signaling overhead for the association between the interaction scene and the scene data can be saved.

[0069] In the seventh aspect, a communication device is provided, which includes a module or unit corresponding to the method / operation / step / action described in any one of the first to sixth aspects and any one of the aspects. The module or unit can be a hardware circuit, software, or a combination of hardware circuit and software.

[0070] In an eighth aspect, a communication device is provided, comprising a processor, wherein the processor is used to, by executing a computer program or instruction, or by a logic circuit, enable the communication device to execute the method described in the first aspect and any possibility of the first aspect; or enable the communication device to execute the method described in the second aspect and any possibility of the second aspect; or enable the communication device to execute the method described in the third aspect and any possibility of the third aspect; or enable the communication device to execute the method described in the fourth aspect and any possibility of the fourth aspect; or enable the communication device to execute the method described in the fifth aspect and any possibility of the fifth aspect; or enable the communication device to execute the method described in the sixth aspect and any possibility of the sixth aspect.

[0071] In a possible implementation, the communication device further includes a memory for storing the computer program or instruction.

[0072] In a possible implementation, the communication device further includes a communication interface, which is used to input and / or output signals.

[0073] In the ninth aspect, a communication device is provided, comprising a logic circuit and an input / output interface, the input / output interface being used to input and / or output signals, the logic circuit being used to execute the method described in the first aspect and any possibility of the first aspect; or, the logic circuit being used to execute the method described in the second aspect and any possibility of the second aspect; or, the logic circuit being used to execute the method described in the third aspect and any possibility of the third aspect; or, the logic circuit being used to execute the method described in the fourth aspect and any possibility of the fourth aspect; or, the logic circuit being used to execute the method described in the fifth aspect and any possibility of the fifth aspect; or, the logic circuit being used to execute the method described in the sixth aspect and any possibility of the sixth aspect.

[0074] In the tenth aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or the instruction is run on a computer, the method described in the first aspect and any possibility of the first aspect is executed; or, the method described in the second aspect and any possibility of the second aspect is executed; or, the method described in the third aspect and any possibility of the third aspect is executed; or, the method described in the fourth aspect and any possibility of the fourth aspect is executed; or, the method described in the fifth aspect and any possibility of the fifth aspect is executed; or, the method described in the sixth aspect and any possibility of the sixth aspect is executed.

[0075] In the eleventh aspect, a computer program product is provided, comprising instructions, which, when run on a computer, cause the method described in the first aspect and any possibility of the first aspect to be executed; or, cause the method described in the second aspect and any possibility of the second aspect to be executed; or, cause the method described in the third aspect and any possibility of the third aspect to be executed; or, cause the method described in the fourth aspect and any possibility of the fourth aspect to be executed; or, cause the method described in the fifth aspect and any possibility of the fifth aspect to be executed; or, cause the method described in the sixth aspect and any possibility of the sixth aspect to be executed.

[0076] In a twelfth aspect, a communication system is provided, comprising: one or more of a model training network element, a data collection network element, a model deployment network element, a data storage network element, or a data generation network element, wherein the network element is configured to execute the method described in the corresponding aspect.

[0077] For example, the above-mentioned communication system includes a model training network element and a model deployment network element; or, the above-mentioned communication system includes a model deployment network element and a data collection network element; or, the above-mentioned communication system includes a model training network element, a data collection network element and a data generation network element; or, the above-mentioned communication system includes a model training network element, a model deployment network element, a data collection network element and a data storage network element, etc.; or, the above-mentioned communication system includes a model training network element, a model deployment network element, a data collection network element and a data generation network element, etc.; or, the above-mentioned communication system includes a model training network element, a model deployment network element, a data collection network element and a data generation network element, etc., and this is not limited.

[0078] The description of the beneficial effects of the seventh to twelfth aspects may correspond to the description of the beneficial effects of the first to sixth aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] FIG1 is a schematic diagram of an application framework 100 in a communication system according to an embodiment of the present application.

[0080] FIG2 is a schematic diagram of an application framework 200 in a communication system according to an embodiment of the present application.

[0081] FIG3 is a schematic diagram of a communication system 300 applicable to an embodiment of the present application.

[0082] FIG4 is a schematic diagram of a communication system 400 applicable to an embodiment of the present application.

[0083] FIG5 is a schematic diagram of a neuron structure 500 .

[0084] FIG6 is a schematic diagram of a training method 600 for an AI model according to an embodiment of the present application.

[0085] FIG7 is a schematic diagram of a training method 700 for an AI model according to an embodiment of the present application.

[0086] FIG8 is a schematic diagram of an AI model training method 800 according to an embodiment of the present application.

[0087] FIG9 is a schematic diagram of a training method 900 for an AI model according to an embodiment of the present application.

[0088] FIG10 is a schematic block diagram of a communication device 1000 according to an embodiment of the present application.

[0089] FIG11 is a schematic block diagram of a communication device 1100 according to an embodiment of the present application. DETAILED DESCRIPTION

[0090] In order to facilitate understanding of the embodiments of the present application, the following points are first explained.

[0091] 1. In this application, unless otherwise specified, "plurality" means two or more.

[0092] 2. In each embodiment of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.

[0093] 3. The various numerical numbers involved in this application are only used for the convenience of description and are not used to limit the scope of protection of this application. The size of the serial numbers involved in this application does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic. For example, the terms "first", "second", "third", "fourth" and other various terminology labels (if any) in the specification and claims and drawings of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. Among them, the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than what is illustrated or described here.

[0094] At the same time, any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0095] 4. The terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or apparatus.

[0096] 5. In this application, "used to indicate" can be understood as "enabling," and "enabling" can include direct enabling and indirect enabling. When describing that certain information is used to enable A, it can include that the information directly enables A or indirectly enables A, and does not necessarily mean that the information contains A.

[0097] The information enabled by the information is called information to be enabled. In the specific implementation process, there are many ways to enable the enabled information, such as but not limited to, directly enabling the information to be enabled, such as the information to be enabled itself or the index of the information to be enabled. The information to be enabled can also be indirectly enabled by enabling other information, wherein there is an association between the other information and the information to be enabled. It is also possible to enable only a part of the information to be enabled, while the other parts of the information to be enabled are known or agreed in advance. For example, it is also possible to enable specific information with the help of the arrangement order of each piece of information agreed in advance (such as specified in the protocol), thereby reducing the enabling overhead to a certain extent. At the same time, it is also possible to identify the common parts of each piece of information and enable them uniformly to reduce the enabling overhead caused by enabling the same information separately.

[0098] 6. In this application, "pre-configuration" may include pre-definition, such as protocol definition. "Pre-definition" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., including each network element). This application does not limit the specific implementation method.

[0099] 7. "Storage" or "saving" as used in this application may refer to storage in one or more memories. The one or more memories may be provided separately or integrated into an encoder or decoder, a processor, or a communication device. The one or more memories may also be provided in part separately and in part integrated into a decoder, processor, or communication device. The type of memory may be any form of storage medium and is not limited thereto.

[0100] 8. The “protocol” referred to in this application may refer to a standard protocol in the field of communications, such as the fourth generation (4G) network, the fifth generation (5G) network protocol, the new radio (NR) protocol, the 5.5G network protocol, the sixth generation (6 th generation, 6G) network protocols and related protocols used in future communication systems, which are not limited in this application.

[0101] 9. The arrows or boxes indicated by dotted lines in the schematic diagrams in the accompanying drawings of this application specification represent optional steps or optional modules.

[0102] 10. In this application, unless otherwise specified, “ / ” indicates that the objects associated with each other are in an “or” relationship. For example, A / B can mean A or B. “And / or” in this application is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural.

[0103] First, a communication system to which the embodiments of the present application are applicable is described.

[0104] The technical solutions provided in this application can be applied to various communication systems, such as 5G or NR systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area networks (WLAN) systems, satellite communication systems, future communication systems, such as 6G mobile communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0105] A network element in a communication system can send a signal to another network element or receive a signal from another network element. The signal may include information, signaling, or data, etc. The network element can also be replaced by an entity, a network entity, a device, a communication device, a communication module, a node, a communication node, etc. The embodiment of the present application is described using a network element as an example. For example, a communication system may include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It is understandable that the terminal device in the embodiment of the present application can be replaced by the first network element, and the network device can be replaced by the second network element, and the two perform the corresponding communication method in the embodiment of the present application.

[0106] In an embodiment of the present application, the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device.

[0107] The terminal device may be a device that provides voice / data, such as a handheld device or vehicle-mounted device with a wireless connection function. At present, some examples of terminals are: mobile phones, tablet computers, laptop computers, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, wearable devices, terminal devices in 5G networks or future evolved public land mobile communication networks (PLMNs). The terminal equipment in the network (PLMN), etc., is not limited to this in the embodiments of the present application.

[0108] As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0109] In the embodiments of the present application, the device for realizing the function of the terminal device can be a terminal device, or a device capable of supporting the terminal device to realize the function, such as a chip system, which can be installed in the terminal device or used in combination with the terminal device. In the embodiments of the present application, the chip system can be composed of a chip, or it can include a chip and other discrete devices. In the embodiments of the present application, only the terminal device is used as an example for description, and the embodiments of the present application are not limited to the solutions of the embodiments of the present application.

[0110] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. A base station may broadly cover various names as follows, or replace the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, secondary station, multi-standard radio (motor slide retainer, MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. A base station may be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. The base station may also refer to a communication module, modem or chip that is set in the aforementioned equipment or device. The base station may also be a mobile switching center and a device that performs the base station function in D2D, V2X, and M2M communications, a network side device in a 6G network, a device that performs the base station function in future communication systems, etc. The base station can support networks with the same or different access technologies. Optionally, the RAN node may also be a server, a wearable device, a vehicle or an on-board device, etc. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU). The embodiments of the present application do not limit the specific technology and specific device form adopted by the network equipment.

[0111] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0112] In some deployments, the network devices mentioned in the embodiments of the present application may include a CU, a DU, or both a CU and a DU, or a control plane CU node (central unit-control plane (CU-CP)), a user plane CU node (central unit-user plane (CU-UP)), and a DU node. For example, the network devices may include a gNB-CU-CP, a gNB-CU-UP, and a gNB-DU.

[0113] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes implementing portions of the base station's functionality. For example, a RAN node can be a CU, DU, CU-CP, CU-UP, or RU. The CU and DU can be separate or included in the same network element, such as the BBU. The RU can be included in a radio frequency device or radio unit, such as an RRU, AAU, or RRH.

[0114] The RAN node may support one or more types of fronthaul interfaces, with different fronthaul interfaces corresponding to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and the RU is another type of interface, relative to the CPRI, some of the downlink and / or uplink baseband functions, such as precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix (CP) for downlink, are moved from the DU to the RU for implementation; and for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT) / cyclic prefix (CP) removal, are moved from the DU to the RU for implementation. In one possible implementation, the interface may be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the division between the DU and RU is different, corresponding to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0115] Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, the DU is configured to implement layer mapping and one or more of the preceding functions (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (e.g., resource element (RE) mapping (BF), or one or more of IFFT / CP) are moved to the RU. For uplink transmission, based on RE demapping, the DU is configured to implement demapping and one or more of the preceding functions (i.e., decoding, derate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and one or more of RE demapping), while other functions after demapping (e.g., digital BF or fast Fourier transform (FFT) / CP removal) are moved to the RU. It is understood that for the functional description of the DU and RU corresponding to various types of eCPRI, please refer to the eCPRI protocol and will not be detailed here.

[0116] In one possible design, the processing unit for implementing baseband functions in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing baseband functions in the RRU / AAU / RRH is called a baseband low layer (BBL) unit.

[0117] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, CU may also be called O-CU (Open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0118] In the embodiments of the present application, the device for implementing the functions of the network device can be a network device; it can also be a device that can support the network device to implement the functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device can be installed in the network device or used in conjunction with the network device. In the embodiments of the present application, only the device for implementing the functions of the network device is used as an example to illustrate, and does not constitute a limitation on the solutions of the embodiments of the present application.

[0119] The network device and / or terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on the water surface; it can also be deployed on aircraft, balloons and satellites in the air. The embodiments of this application do not limit the scenarios in which the network device and the terminal device are located. In addition, the terminal device and the network device can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific forms of the terminal device and the network device.

[0120] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, and therefore the demands that need to be met are becoming increasingly diverse. For example, the network needs to be able to support ultra-high speeds, ultra-low latency, and / or ultra-large connections. This feature makes network planning, network configuration, and / or resource scheduling increasingly complex. In addition, as network functionality becomes increasingly powerful, such as supporting higher spectrum, supporting high-order multiple input multiple output (MIMO) technology, supporting beamforming, and / or supporting new technologies such as beam management, network energy saving has become a hot research topic. These new demands, new scenarios, and new features have brought unprecedented challenges to network planning, operation and maintenance, and efficient operation. To meet this challenge, artificial intelligence technology can be introduced into wireless communication networks to achieve network intelligence.

[0121] In order to support AI technology in wireless networks, AI nodes may also be introduced into the network.

[0122] Optionally, the AI ​​node can be deployed in one or more of the following locations in the communication system: access network equipment, terminal equipment, or core network equipment. Alternatively, the AI ​​node can be deployed separately, for example, in a location other than any of the above devices, such as a host or cloud server in an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, such as one or more of the following: network equipment, terminal equipment, or network elements of the core network.

[0123] It is understood that the embodiments of the present application do not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on function, such as different AI nodes are responsible for different functions.

[0124] It is also understood that AI nodes can be independent devices, or integrated into the same device to implement different functions, or can be network elements in hardware devices, or can be software functions running on dedicated hardware, or can be virtualized functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of AI nodes. Among them, AI nodes can be AI network elements or AI modules.

[0125] Figure 1 is a schematic diagram of an application framework 100 in a communication system of an embodiment of the present application. As shown in Figure 1, network elements in the communication system are connected through interfaces (e.g., NG, Xn) or air interfaces. One or more AI modules are provided in these network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in operation administration and maintenance (OAM) (for clarity, only one is shown in Figure 1). The access network node can be a separate RAN node, or it can include multiple RAN nodes, for example, including CU and DU. One or more AI modules can also be provided in the CU and / or DU. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are provided in the CU-CP and / or CU-UP.

[0126] The AI ​​module is used to implement the corresponding AI function. The AI ​​modules deployed in different network elements may be the same or different. The model of the AI ​​module can implement different functions according to different parameter configurations. The model of the AI ​​module can be configured based on one or more of the following parameters: structural parameters (such as the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the bias in the activation function), input parameters (such as the type of input parameters and / or the dimension of the input parameters), or output parameters (such as the type of output parameters and / or the dimension of the output parameters). Among them, the bias in the activation function can also be called the bias of the neural network.

[0127] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or on the same node or device.

[0128] Figure 2 is a schematic diagram of an application framework 200 in a communication system according to an embodiment of the present application. As shown in Figure 2, the communication system includes a RAN intelligent controller (RIC). For example, the RIC may be the AI ​​modules 117 and 118 shown in Figure 1, which are used to implement AI-related functions. The RIC includes a near-real-time RIC (near-real time RIC, near-RT RIC) and a non-real-time RIC (non-real time RIC, non-RT RIC). The non-real-time RIC mainly processes non-real-time information, such as data that is not sensitive to latency, and the latency of the data may be in the order of seconds. The real-time RIC mainly processes near-real-time information, such as data that is relatively sensitive to latency, and the latency of the data may be in the order of tens of milliseconds.

[0129] Near real-time RIC is used for model training and reasoning. For example, it is used to train an AI model and use the AI ​​model for reasoning. Near real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. This information can be used as training data or reasoning data. Optionally, near real-time RIC can deliver the reasoning results to the RAN node and / or terminal. Optionally, the reasoning results can be exchanged between the CU and DU, and / or between the DU and RU. For example, the near real-time RIC delivers the reasoning results to the DU, and the DU sends it to the RU.

[0130] Non-real-time RIC is also used for model training and reasoning. For example, it is used to train AI models and use the models for reasoning. Non-real-time RIC can obtain network-side and / or terminal-side information from RAN nodes (such as CU, CU-CP, CU-UP, DU and / or RU) and / or terminals. This information can be used as training data or reasoning data, and the reasoning results can be submitted to the RAN node and / or terminal. Optionally, the reasoning results can be exchanged between the CU and DU, and / or between the DU and RU. For example, the non-real-time RIC submits the reasoning results to the DU, and the DU sends it to the RU.

[0131] The near-real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near-real-time RIC and non-real-time RIC can also be part of other devices. For example, the near-real-time RIC is set up in a RAN node (e.g., a CU or DU), while the non-real-time RIC is set up in an OAM, a cloud server, a core network device, or other network devices.

[0132] Figure 3 is a schematic diagram of a communication system 300 applicable to an embodiment of the present application. As shown in Figure 3, communication system 300 may include at least one network device, such as network device 110; communication system 100 may also include at least one terminal device, such as terminal device 120 and terminal device 130. Network device 110 and terminal devices (such as terminal device 120 and terminal device 130) can communicate via wireless links. Communication devices in the communication system, such as network device 110 and terminal device 120, can communicate using multi-antenna technology.

[0133] Figure 4 is a schematic diagram of a communication system 400 applicable to an embodiment of the present application. Compared to communication system 300, communication system 400 also includes an AI network element 140. AI network element 140 is used to perform AI-related operations, such as building a training data set or training an AI model.

[0134] In one possible implementation, the network device 110 may send data related to the training of the AI ​​model to the AI ​​network element 140, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​network element 140 may send the results of the operations related to the AI ​​model to the network device 110, and forward them to the terminal device through the network device 110. For example, the results of the operations related to the AI ​​model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a part of the trained AI model may be deployed on the network device 110, and another part may be deployed on the terminal device. Alternatively, the trained AI model may be deployed on the network device 110. Alternatively, the trained AI model may be deployed on the terminal device.

[0135] It should be understood that Figure 4 illustrates only the example of a direct connection between AI network element 140 and network device 110. In other scenarios, AI network element 140 may also be connected to a terminal device. Alternatively, AI network element 140 may be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 may be connected to network device 110 through a third-party network element. This embodiment of the present application does not limit the connection relationship between the AI ​​network element and other network elements.

[0136] The AI ​​network element 140 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 110 or the terminal device shown in FIG3 .

[0137] It should be noted that Figures 3 and 4 are simplified schematic diagrams for ease of understanding. For example, the communication system may further include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 3 and 4. In actual applications, the communication system may include multiple network devices (such as network device 110 and network device 150 (not shown in Figure 3)), and may also include multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0138] Next, some technical concepts involved in this application are briefly described.

[0139] Machine learning (ML): ML is an important technical approach to achieving AI. ML can be categorized into supervised learning, unsupervised learning, and reinforcement learning.

[0140] Supervised learning uses an ML algorithm to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels. This learned mapping relationship is then expressed using an ML model. The process of training an ML model is the process of learning this mapping relationship. For example, in signal detection, a noisy received signal is a sample, and the true constellation point corresponding to this signal is the label. Through training, the ML model aims to learn the mapping relationship between samples and labels, essentially making the ML model a signal detector. During training, the model parameters are optimized by calculating the error between the model's predicted values ​​and the true labels. Once the mapping relationship is learned, the learned mapping can be used to predict the sample label for each new sample. The mapping relationship learned by supervised learning can include linear and nonlinear mappings. Learning tasks can be categorized into classification and regression tasks based on the type of label.

[0141] Unsupervised learning relies solely on collected sample values, using algorithms to discover inherent patterns within them. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0142] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal decision-making actions. However, because the labels for "correct actions" are not available in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0143] Deep neural networks (DNNs) are a specific implementation of machine learning (ML). According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on DNNs can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods. See Figure 5 for an illustration of DNNs.

[0144] FIG5 is a schematic diagram of a neuron structure 500. As shown in FIG5 , it is assumed that the input of the neuron is x=[x0,…,x n ], and the weight corresponding to the input is w=[w0,…,w n ], the bias of the weighted sum is b, and the form of nonlinear function can be diversified. An example is the max{0,x} maximum function. Then the effect of the execution of a neuron can be

[0145] According to the network construction method, DNN can be divided into feedforward neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN).

[0146] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0147] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0148] The above-mentioned FNN, CNN, and RNN are common neural network structures, which are all constructed based on neurons. As mentioned above, each neuron performs a weighted sum operation on its input values, and the weighted summation result generates an output through a nonlinear function. We call the weights of the weighted summation operation of neurons in the neural network and the nonlinear function the parameters of the neural network. Taking the neuron with max{0,x} as the nonlinear function as an example, The parameters of the neuron to be operated are weights w=[w0,…,w n The weighted sum is biased by b, and the nonlinear function max{0,x}. The parameters of all neurons in a neural network constitute the parameters of the neural network.

[0149] AI Model: An AI model is an algorithm or computer program that implements AI functionality. It represents the mapping between the model's input and output. AI models can be neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other ML models.

[0150] Two-end model: A two-end model can also be called a bilateral model, collaborative model, dual model, or two-side model. A two-end model is a model composed of multiple sub-models. The sub-models must match each other. These sub-models can be deployed on different nodes.

[0151] The AI ​​model in the embodiment of the present application can be a dual-end model or a single-end model, which can be deployed on terminal devices and / or network devices.

[0152] Model application or inference: Use the trained model to solve practical problems.

[0153] AI function: It does not specifically refer to a specific AI model, but refers to a class of AI models with the same function. For example, AI functions can be channel state information (CSI) feedback, CSI prediction, beam prediction, positioning, channel estimation, etc., that is, AI functions can correspond to multiple AI models.

[0154] It is understood that the implementation of an AI model or AI function can be hardware circuitry, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application, or software application.

[0155] In the embodiment of the present application, indications include direct indications (also called explicit indications) and implicit indications. Direct indication of information A refers to including information A; implicit indication of information A refers to indicating information A through the correspondence between information A and information B and direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured. In addition, in the embodiment of the present application, information C is used to determine information D, which includes both information D being determined based only on information C and information C and other information. In addition, information C is used to determine information D, and it can also be determined indirectly, for example, information D is determined based on information E, and information E is determined based on information C.

[0156] In addition, in each embodiment of the present application, "network element A (which may be device A) sends information A to network element B (which may be device B)" can be understood as the destination end of the information A or the intermediate network element in the transmission path between the destination end and the network element B, which may include directly or indirectly sending information to network element B. "Network element B receives information A from network element A" can be understood as the source end of the information A or the intermediate network element in the transmission path between the source end and the network element A, which may include directly or indirectly receiving information from network element A. The information may be processed as necessary between the source end and the destination end of the information transmission, such as format changes, etc., but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0157] As described in the background technology section, when an AI model switches from one scene to another, in order to enable the AI ​​model to be applied to the newly switched scene, the data of the newly switched scene can be obtained, and the AI ​​model can be updated using the data of the newly switched scene to obtain an updated AI model, which can be applied to the newly switched scene. However, the above solution may result in a large overhead for model updates. For example, updating all parameters of the AI ​​model will result in a large update overhead; for another example, obtaining a large amount of data in the newly switched scene will result in a large data storage overhead, thereby resulting in a large overhead for model updates.

[0158] In view of this, the present application provides an AI model training method and communication device, which can effectively reduce the overall overhead of model updates.

[0159] For ease of understanding and explanation, the following description uses the interaction between network element 1 and network element 2 as an example. Network element 1 and network element 2 can correspond to different entities or the same entity. For example, network element 1 corresponds to entity 1 (which can be a terminal device, network device, or core network device), and network element 2 corresponds to entity 2 (which can be a terminal device, network device, or core network device); for another example, network element 1 and network element 2 correspond to entity 1, etc., without limitation.

[0160] In one possible implementation, network element 1 may be a model deployment network element or a model training network element, and network element 2 may include at least one of a scene recognition network element or a data collection network element (which may also be a data provision network element or other name, without limitation). For details on the interaction between network element 1 and network element 2, see Figure 6.

[0161] FIG6 is a schematic diagram of a training method 600 for an AI model according to an embodiment of the present application. As shown in FIG6 , the method 600 includes:

[0162] S601. Network element 1 obtains identification information of a first scenario.

[0163] For example, as shown in the upper diagram of Figure 6, network element 1 is a model training network element, and network element 1 obtains identification information of the first scene from network element 2. In other words, after network element 2 determines or obtains the identification information of the first scene, network element 2 sends the identification information of the first scene to network element 1. In this case, network element 2 can also be called a scene recognition network element or a data collection network element.

[0164] For example, as shown in the upper diagram of Figure 6, network element 1 is a model training network element, and network element 1 obtains identification information of the first scenario from the model deployment network element. In other words, after network element 2 determines or obtains the identification information of the first scenario, network element 2 sends the identification information of the first scenario to the model deployment network element, and the model deployment network element sends the identification information of the first scenario to network element 1.

[0165] For example, as shown in the lower figure of Figure 6, network element 1 is a model deployment network element, and network element 1 obtains identification information of the first scenario from network element 2. In other words, after network element 2 determines or obtains the identification information of the first scenario, network element 2 sends the identification information of the first scenario to network element 1.

[0166] In one possible implementation, the identification information of the first scene may include identification information of the first scene. For example, an identifier may be configured for each scene, and the identifier is associated with the characteristics or type of the scene. Exemplarily, for indoor scenes, identifier 1 is configured; for outdoor scenes, identifier 2 is configured; for factory scenes, identifier 3 is configured; for low-power scenarios, identifier 4 is configured; for high-power scenarios, identifier 5 is configured; for scenarios with better channel quality, identifier 6 is configured; for scenarios with poor channel quality, identifier 7 is configured; for low-latency scenarios, identifier 8 is configured; for high-latency scenarios, identifier 9 is configured, and so on. The identification information of these scenes can be perceived, acquired, and determined by sensor devices (for example, network devices, terminal devices, and cameras, etc.). For example, the network device or terminal device can determine the characteristic information of the current scene through the reference signal measurement results, and determine the identification information of the current scene based on the association between the characteristic information of the scene and the identification information of the scene, and report the identification information of the current scene.

[0167] In another possible implementation, the identification information of the first scene may include feature information of the first scene. For example, each scene may have its own unique scene feature.

[0168] Exemplarily, for indoor scenes, it includes feature information 1, and feature information 1 includes one or more of the following features: the delay spread of the reference signal channel measurement result in the scene is lower than a first threshold (the first threshold is preconfigured and predefined), the Doppler frequency deviation of the reference signal channel measurement result in the scene is lower than a second threshold (the second threshold is preconfigured and predefined), the corresponding network device indicates that the current scene is located indoors, the corresponding terminal device determines that the current scene is located indoors based on its own positioning results, and the current scene is perceived as an indoor scene through a sensor device (such as a camera).

[0169] Exemplarily, for outdoor scenes, it includes feature information 2, which includes one or more of the following features: the delay spread of the reference signal channel measurement result in the scene is higher than a third threshold (the third threshold is preconfigured and predefined), the Doppler frequency deviation of the reference signal channel measurement result in the scene is higher than a fourth threshold (the fourth threshold is preconfigured and predefined), the corresponding network device indicates that the current scene is located outdoors, the corresponding terminal device determines that the current scene is located outdoors based on its own positioning results, and the current scene is perceived as an outdoor scene through a sensor device (such as a camera).

[0170] Exemplarily, for a factory scenario, it includes feature information 3, which includes one or more of the following features: the delay spread of the reference signal channel measurement result in the scenario is lower than the fifth threshold (the fifth threshold is preconfigured and predefined), the Doppler frequency deviation of the reference signal channel measurement result in the scenario is lower than the sixth threshold (the sixth threshold is preconfigured and predefined), the corresponding network device indicates that the current scene is located in the factory, the corresponding terminal device determines that the current scene is located in the factory based on its own positioning results, and the current scene is perceived as a factory scene through sensor equipment (such as a camera).

[0171] Exemplarily, for a scenario with better channel quality, it includes feature information 4, and feature information 4 includes one or more of the following features: the signal to interference plus noise ratio (SINR) of the reference signal channel measurement result in this scenario is higher than the seventh threshold (the seventh threshold is preconfigured and predefined), and the interference level of the reference signal channel measurement result in this scenario is lower than the eighth threshold (the eighth threshold is preconfigured and predefined).

[0172] Exemplarily, for a scenario with poor channel quality, it includes feature information 5, which includes one or more of the following features: the SINR of the reference signal channel measurement result in the scenario is lower than a ninth threshold (the ninth threshold is preconfigured or predefined), and the interference level of the reference signal channel measurement result in the scenario is higher than a tenth threshold (the tenth threshold is preconfigured or predefined). The ten thresholds may be different or partially the same, and this is not limited.

[0173] The characteristic information of these scenes can be sensed, acquired and reported by sensor devices, and the network element 1 determines the identification information of the current scene based on the association between the characteristic information of the scene and the identification information of the scene.

[0174] In the above description, network element 2 can be a network element or entity with the function of perceiving and obtaining identification information (such as identification information or feature information) of the current scene, for example, a network device or a terminal device or a perception device different from a network device or a terminal device, etc. The perception device can communicate with at least one of the network device or the terminal device.

[0175] S602. Network element 1 determines a first parameter in the first model according to identification information of the first scenario, where the first parameter is used to determine a parameter that needs to be updated in the first model.

[0176] The first model can support (can also be: the first model currently supports) multiple scenarios, for example, the first model can support the second scenario, the third scenario and other scenarios. For the convenience of description, the scenarios supported by the first model can be referred to as historical scenarios, for example, the second scenario and the third scenario can be collectively referred to as historical scenarios. Among them, the number of historical scenarios supported by the first model is not limited, it can be one or more, and the number of historical scenarios supported by the first scenario can be updated or changed (for example, deleted or added, this is not limited). In addition, the historical scenario can also be a scenario for the functional service corresponding to the first model. For example, the scenario to which the first model can be specifically applied can be a historical scenario.

[0177] In one possible implementation, the first scenario does not belong to the scenarios currently supported by the first model. In this case, the parameters that need to be updated in the first model can be updated so that the updated first model can support the first scenario.

[0178] Another possible implementation method is that the first scenario belongs to the scenario currently supported by the first model, such as the first scenario belongs to a scene in the historical scene. The parameters that need to be updated in the first model can also be updated to make the updated first model more powerful.

[0179] Network element 1 may determine parameters that need to be updated in the first model based on the identification information of the first scenario. The parameters that need to be updated in the first model may not include all parameters of the first model, that is, include all or part of the parameters of the first model. The first parameters can be used to determine the parameters that need to be updated in the first model.

[0180] In one possible implementation, the parameter to be updated in the first model is the first parameter, as shown in Table 1. When the parameter to be updated in the first model is the first parameter, this application supports directly determining the parameter to be updated in the first model. The contents of Table 1 are to be understood as examples only and are not intended to be definitive.

[0181] Table 1

[0182] As shown in Table 1, three scenarios and three parameters (taking the first model including three parameters as an example) are used for description. For example, scenario 1 corresponds to parameter 1, scenario 2 corresponds to parameter 2, and scenario 3 corresponds to parameter 3. Exemplarily, for scenario 1, parameter 1 in the first model is a parameter that needs to be updated; for scenario 2, parameter 2 in the first model is a parameter that needs to be updated; and for scenario 3, parameter 3 in the first model is a parameter that needs to be updated. The present application supports establishing an association relationship between a scenario and a parameter in the first model, and storing, configuring, or predefining the association relationship between the scenario and the parameter in the first model in network element 1. In this way, the signaling overhead for the association relationship between the interactive scenario and the parameters in the first model can be saved.

[0183] In summary, by setting association relationships between multiple different scenes and parameters in the first model, and determining the first parameter associated with the first scene based on the identification information of the first scene and at least one of the above-mentioned association relationships, the parameters that need to be updated in the first model can be determined.

[0184] The above parameters 1, 2 and 3 can be understood as parameters such as w and b described in FIG5 .

[0185] In combination with the content shown in Table 1, the identification information of the first scene can be used to indicate scene 1, and the first parameter can be parameter 1.

[0186] In another possible implementation, the parameters that need to be updated in the first model are some or all of the parameters in the first model other than the first parameter, as shown in Table 2. When the parameters that need to be updated in the first model are some or all of the parameters in the first model other than the first parameter, the present application supports indicating the parameters that need to be updated in the first model with lower signaling overhead. The content described in Table 2 is to be understood as an example only and is not to be construed as a final limitation.

[0187] Table 2

[0188] As shown in Table 2, three scenarios and three parameters (taking the first model including three parameters as an example) are used for description. For example, scenario 1 corresponds to parameter 2 and parameter 3, scenario 2 corresponds to parameter 1 and parameter 3, and scenario 3 corresponds to parameter 1 and parameter 2. Exemplarily, for scenario 1, parameter 1 in the first model is a parameter that does not need to be updated; for scenario 2, parameter 2 in the first model is a parameter that does not need to be updated; for scenario 3, parameter 3 in the first model is a parameter that does not need to be updated. The present application supports establishing an association relationship between the scenario and the parameters in the first model, and storing, configuring, or predefining the association relationship between the scenario and the parameters in the first model in network element 1. In this way, network element 1 can determine the first parameter based on the association relationship between the scenario and the parameters in the first model and the identification information of the first scenario.

[0189] In combination with the content shown in Table 2, the identification information of the first scene can be used to indicate scene 1, and the first parameter can be parameter 1.

[0190] In summary, network element 1 determines the parameters that need to be updated in the first model based on the identification information of the scene, which helps to update the model of the parameters that need to be updated in the first model, and does not require updating all the parameters of the first model. In this way, the overall overhead of model update can be effectively reduced.

[0191] The method shown in FIG6 is further described below in conjunction with FIG7 and FIG8.

[0192] For ease of understanding and explanation, the following description uses the interaction between network element 1, network element 2, and network element 3 as an example. Network element 1, network element 2, and network element 3 may correspond to different entities, that is, different entities, or may correspond to the same entity or multiple entities, that is, all or part of the three network elements of network element 1, network element 2, or network element 3 belong to the same entity. For example, network element 1 corresponds to entity 1 (which may be a terminal device, network device, or core network device), network element 2 corresponds to entity 2 (which may be a terminal device, network device, or core network device), and network element 3 corresponds to entity 3 (which may be a terminal device, network device, or core network device); for another example, network element 1 and network element 2 correspond to entity 1, and network element 3 corresponds to entity 2; for another example, network element 1 and network element 3 correspond to entity 1, and network element 2 corresponds to entity 2, etc., without limitation. Functionally, network element 3 can be referred to as a data storage / generation network element. When it has a data generation function, it may include a data generator and be referred to as a data generation network element. When it is used for data storage, it may also be referred to as a data storage network element.

[0193] Figure 7 is a schematic diagram of the interactive process of the AI ​​model training method 700 according to an embodiment of the present application. As shown in Figure 7, taking network element 1 as the model deployment network element as an example, the method 700 includes:

[0194] S701. Network element 2 determines identification information of a first scenario.

[0195] Among them, network element 2 can be a network element or entity with the function of sensing and obtaining identification information (such as identification information or feature information) of the current scene, for example, a network device or a terminal device, or a perception device different from the network device or the terminal device, etc. The perception device can communicate with at least one of the network device or the terminal device. In this case, network element 2 can be called a scene recognition network element.

[0196] The network element 2 determining the identification information of the first scene may include: the network element 2 acquiring the identification information of the first scene or the network element 2 sensing the identification information of the first scene, which is not limited to this.

[0197] In one example, network element 2 performs scene recognition based on a reference signal measurement result or a perception result of one or more of a camera, a radar, or a base station, and determines identification information of the first scene. The specific manner in which network element 2 determines the identification information of the first scene is not limited.

[0198] S702 : Network element 2 sends identification information of the first scenario to network element 1 .

[0199] Correspondingly, network element 1 receives identification information of the first scenario.

[0200] By having network element 2 report the identification information of the first scenario, other network elements (for example, network element 1) can determine whether it is necessary to start update training of the first model and whether it is necessary to start the data collection process for the first scenario based on the identification information of the first scenario, thereby effectively reducing the overall overhead of model updates.

[0201] S703: Network element 1 determines, based on the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model.

[0202] For example, network element 1 may store or be configured with identification information of scenarios supported by the first model.

[0203] After network element 1 obtains the identification information of the first scene, it can determine whether the first scene belongs to the scene currently supported by the first model. For the sake of ease of description, the following description takes the example of network element 1 determining that the first scene does not belong to the scene currently supported by the first model, but the first scene may also belong to the possible example of the scene currently supported by the first model. When it is determined that the first scene does not belong to the scene currently supported by the first model based on the identification information of the first scene, the present application can start the data collection process for the first scene, and use the data related to the first scene to update the parameters that need to be updated in the first model for training, so that the updated first model can be used for the first scene.

[0204] S704 . Network element 1 sends information 1 to a data collection network element, for example, network element 2 . Information 1 is used to instruct acquisition of data of the first scenario.

[0205] Accordingly, a data collection network element, such as network element 2, receives information 1 and obtains data for the first scene based on information 1. The data for the first scene can be used to update the first model. In this way, data for the first scene can be obtained so that the first model can be updated and trained using the data for the first scene. The data collection network element may also be another network element than the scene recognition network element, such as network element 2. Optionally, the scene recognition network element may determine identification information of the first scene corresponding to the data based on the data from the data collection network element.

[0206] In addition, network element 1 determines whether it is necessary to obtain data of the first scene based on the identification information of the scene, which helps to avoid repeated data collection. When it is determined that data of the first scene needs to be obtained, network element 1 can instruct to obtain data of the first scene, and can use the data of the first scene to update and train the first model, so that the updated first model can be applied to the first scene.

[0207] In addition, through the above solution, the present application can also avoid the situation where the first model is updated and trained when there is no need to update and train the first model, thereby effectively reducing the overall cost of model updating.

[0208] S705 . The data collection network element, such as network element 2 , sends the data of the first scenario to network element 3 .

[0209] Accordingly, network element 3 receives data of the first scenario. Network element 3 may be a data storage network element that stores data of the first scenario. Network element 3 may be a data generation network element that extracts data features of the first scenario and updates the data stored by the data generation network element.

[0210] Optionally, a data collection network element, such as network element 2, may also send data of the first scenario to the model training network element, without limitation.

[0211] S706. Network element 1 determines a first parameter in the first model according to the identification information of the first scenario, where the first parameter is used to determine a parameter that needs to be updated in the first model.

[0212] For the description of S706 , please refer to S602 and will not be repeated here.

[0213] S707. Network element 1 sends the first parameter to the model training network element.

[0214] Accordingly, the model training network element receives the first parameter and determines the parameters that need to be updated in the first model based on the first parameter. In this way, other network elements can determine the parameters that need to be updated in the first model.

[0215] S708. The model training network element obtains first data, where the first data is related to the first scenario.

[0216] In one possible implementation, the first data may include any one of the following:

[0217] The data of the first scene, the data of the first scene and the data of the scenes related to the first scene, or the data obtained after data generation based on the data characteristics of the first scene and the second data, the second data is the data of the scenes currently supported by the first model, and the scenes related to the first scene belong to the scenes currently supported by the first model. Compared with the first scene, these scenes can also be called historical scenes. Through one or more of the above-mentioned items, the present application supports the completion of the update training of the parameters that need to be updated in the first model, so that the first model obtained after the update can be applied to the first scene. Optionally, the updated first model can also be applied to the aforementioned historical scenes.

[0218] When the first data is data of the first scenario, the model training network element obtains the first data from network element 2, and may also obtain the first data from a data storage network element (an example of network element 3).

[0219] When the first data is data for the first scenario and data for scenarios related to the first scenario, the model training network element obtains the first data from the data storage / generation network element. For a description of the model training network element obtaining the first data from the data storage / generation network element (network element 3, such as the data storage network element), see Figure 9. For a description of scenarios related to the first scenario, see Figure 9.

[0220] When the first data is data obtained by the data storage / generation network element after generating data according to the data of the first scenario and the data characteristics of the second data, the model training network element can obtain the first data from the data storage / generation network element (network element 3, such as the data generation network element).

[0221] In summary, the present application supports obtaining data for updating and training parameters that need to be updated in the first model through one or more of a plurality of approaches.

[0222] Furthermore, the data generation network element generates data according to the data of the first scenario and the data characteristics of the second data to obtain the first data, including:

[0223] S1-data generation network element obtains data of the first scenario from network element 2;

[0224] The S2-data generation network element generates data according to the data of the first scenario and the data features of the second data to obtain the first data.

[0225] The data generation network element can use generative adversarial networks (GANs) to generate data based on the data of the first scenario and the data features of the second data to obtain the first data. GANs are a typical unsupervised learning method that can automatically extract data features and complete data generation. GANs consist of two parts: a generator (which generates data through a neural network), whose purpose is to generate data as similar as possible to the original data to deceive the discriminator; and a discriminator (which uses a neural network to determine whether the data is real or machine-generated), whose purpose is to identify the "fake data" created by the generator.

[0226] For example, after the data generation network element obtains the data of scene 1 (scene 1 can be the first scene), the data generation network element extracts the data features of scene 1 and generates new data. When the data generation network element obtains the data of scene 2, the data generation network element extracts the data features of scene 1 and the data features of scene 2 and generates new data; after the data generation network element obtains the data of scene 3, the data generation network element extracts the data features of scene 1, the data features of scene 2, and the data features of scene 3 and generates new data. Therefore, the data that the data generation network element has obtained or saved before obtaining the data of the first scene can be collectively referred to as the second data, and the data generation network element extracts the data features of the second data and the data features of the first scene and generates the first data. In this way, the updated AI model can also support historical scenes before the first scene.

[0227] By generating the first data based on the data of the first scene and the data features of the second data, and without the need to obtain a large amount of data in the first scene, the present application can support the updated first model to be used not only for the first scene, but also for the scenes supported before the model was updated. This can effectively reduce the overhead of data collection, and thus can also effectively reduce the overall overhead of model updating.

[0228] In one possible implementation, the data feature of the second data includes any one of the following:

[0229] signal to noise ratio (SNR), signal to interference plus noise ratio (SINR), reference signal received power (RSRP), or data distribution.

[0230] The data distribution mentioned above may include one or more of the following: the latent variable variational probability distribution of the data after variational inference, the probability distribution of the data after fitting a Gaussian mixture model, and the latent distribution of the data learned through an adversarial process. In this way, by using one or more of the parameters listed above, the data generation network element can generate a large amount of data that can be used to update the first model.

[0231] S709: The model training network element uses the first data to update the parameters that need to be updated in the first model to obtain an updated first model.

[0232] The parameters that need to be updated in the first model are determined based on the first parameters (S707) received from the network element 1, that is, the first parameters are used to determine the parameters that need to be updated in the first model.

[0233] Regarding the description of the model training network element updating the parameters that need to be updated in the first model, please refer to the existing solution and will not be repeated here. Among them, because the first data is related to the first scenario, the model training network element uses the first data to update the first model, and the updated first model obtained can support the first scenario.

[0234] S710: The model training network element sends the updated first model to network element 1.

[0235] Correspondingly, network element 1 receives the updated first model.

[0236] Network element 1 can use the updated first model to infer the first scenario. For detailed description, please refer to the existing solution and will not be repeated here.

[0237] Through the above solution, this application supports reducing the overall overhead of model updates, and can also enable the updated AI model to support previous historical scenarios, that is, to maintain the ability to reason about historical scenarios (for example, the ability to reason about historical scenarios can be maintained by updating some parameters or using historical scene data to update the model).

[0238] The content shown in FIG7 is described by taking the example of the model deployment network element sending the first parameter to the model training network element. The model training network element can also determine the first parameter by itself. Please refer to the description of FIG8 .

[0239] FIG8 is a schematic diagram of the interactive process of the AI ​​model training method 800 according to an embodiment of the present application. As shown in FIG8 , taking network element 1 as the model training network element as an example, the method 800 includes:

[0240] S801. Network element 2 determines identification information of a first scenario.

[0241] For the description of S801, please refer to S701 and will not be repeated here.

[0242] S802. Network element 2 sends identification information of the first scenario to the model deployment network element and network element 1.

[0243] Correspondingly, the model deployment network element and network element 1 respectively receive identification information of the first scenario.

[0244] For the description of S802, please refer to S702 and will not be repeated here.

[0245] S803: The model deployment network element determines, based on the identification information of the first scenario, that the first scenario does not belong to the scenarios currently supported by the first model.

[0246] For the description of S803, please refer to S703 and will not be repeated here.

[0247] S804. The model deployment network element sends information 1 to the data collection network element, such as network element 2. Information 1 is used to instruct to obtain data of the first scenario.

[0248] For the description of S804, please refer to S704 and will not be repeated here.

[0249] S805 . The data collection network element, such as network element 2 , sends the data of the first scenario to network element 3 .

[0250] Correspondingly, network element 3 receives data of the first scenario.

[0251] For the description of S805, please refer to S705 and will not be repeated here.

[0252] S806. Network element 1 determines a first parameter in the first model according to the identification information of the first scenario, where the first parameter is used to determine a parameter that needs to be updated in the first model.

[0253] For the description of S806 , please refer to S602 and will not be repeated here.

[0254] The parameter that needs to be updated in the first model is the first parameter.

[0255] S807. Network element 1 obtains first data, where the first data is related to the first scenario.

[0256] For the description of S807, please refer to S708 and will not be repeated here.

[0257] S808. Network element 1 uses the first data to update the parameters that need to be updated in the first model to obtain an updated first model.

[0258] For the description of S808, please refer to S709 and will not be repeated here.

[0259] S809. Network element 1 sends the updated first model to the model deployment network element.

[0260] Correspondingly, the model deployment network element receives the updated first model.

[0261] The model deployment network element can use the updated first model to infer the first scenario. For specific descriptions, please refer to the existing solution and will not be repeated here.

[0262] Through the above solution, this application supports reducing the overall overhead of model updates and can also enable the updated AI model to support previous historical scenarios, that is, to maintain the ability to reason about historical scenarios.

[0263] The contents shown in Figures 7 and 8 are described using the example of a model deployment network element sending information 1 to a data collection network element, such as network element 2, to instruct the acquisition of data for the first scenario. The model training network element may also send information to the data collection network element, such as network element 2, to instruct the acquisition of data for the first scenario. For example, the information indicating the acquisition of data for the first scenario is used to indicate the acquisition of data for the first scenario and the amount of data to be acquired. In this case, information 1 may not be sent. Alternatively, the model deployment network element may send both information 1 and information indicating the acquisition of data for the first scenario to the data collection network element, such as network element 2. For example, the information indicating the acquisition of data for the first scenario specifically indicates the amount of data to be acquired. For the process of the model training network element sending information indicating the acquisition of data for the first scenario to the data collection network element, such as network element 2, please refer to the description of Figure 9.

[0264] Figure 9 is a schematic diagram of the interactive process of the AI ​​model training method 900 of the embodiment of the present application. As shown in Figure 9, taking network element 1 as the model training network element or model deployment network element as an example, taking network element 3 as the data storage network element as an example, the method 900 includes:

[0265] S901. Network element 1 obtains identification information of a first scenario.

[0266] For the description of S901, please refer to S601 and will not be repeated here.

[0267] S902. Network element 1 determines, based on identification information of the first scene, scenes related to the first scene, where the scenes related to the first scene belong to scenes currently supported by the first model.

[0268] For example, a scene related to the first scene may be: a scene having the same or similar data features as the first scene, or a scene of the same type as the first scene; or a scene having similar or identical scene features as the first scene. The description of the scene features of the scene can refer to the related description at S601 above and will not be repeated here. The description of the data features can also refer to the description of the data features of the second data above and will not be repeated here.

[0269] In one possible implementation, the network element 1 may determine the scenes related to the first scene according to predefined rules, or the network element 1 may determine which scenes related to the first scene correspond to which feature information based on feature information of the scenes.

[0270] For example, a predefined rule may be: if the feature information of the scene features of two scenes is consistent or similar, then they can be identified as related scenes. Similarity means that the feature information satisfies the same conditions, such as being below a certain threshold or above a certain threshold, or similarity means that the difference in feature information is below a certain threshold. The specific threshold and / or feature information may be predefined or configured and is not limited here.

[0271] Exemplarily, scenario 1 includes feature information 1, and feature information 1 includes the following features: the delay spread of the reference signal channel measurement result in this scenario is lower than the first threshold value, and the first scenario includes feature information 2, and feature information 2 includes the following features: the delay spread of the reference signal channel measurement result in this scenario is lower than the first threshold value. Accordingly, scenario 1 and the first scenario are two related scenarios. For another example, the predefined rule may be: if the data features of the scene features of the two scenarios are consistent or similar, they can be identified as related scenarios. Among them, similarity means that the data features all meet the same conditions, for example, they are all lower than a certain threshold value, or, higher than a certain threshold value, or, similarity means that the difference in data features is lower than a certain threshold value. Specific thresholds and / or data features may be predefined or configured, and are not limited here.

[0272] Exemplarily, scenario 1 includes data feature 1, which includes the following feature: the reference signal received power in scenario 1 is lower than threshold 1. The first scenario includes data feature 2, which includes the following feature: the reference signal received power in the first scenario is lower than threshold 1. Accordingly, scenario 1 and the first scenario are two related scenarios.

[0273] For another example, the predefined rule may be: if the scene classifications of two scenes belong to the same category and the classification rules are predefined or preconfigured, then they may be identified as related scenes.

[0274] For example, scene 1 is an indoor scene, and the first scene is also an indoor scene. Accordingly, scene 1 and the first scene are two related scenes.

[0275] S903. Network element 1 obtains quantity information of scene data related to the first scene.

[0276] Network element 1 can send information 2 to network element 3 (which can be a data storage network element), where information 2 is used to indicate the acquisition of information about a scene related to the first scene. For example, information 2 includes identification information of the scene related to the first scene, and network element 3 can determine at least one of the data of the scene related to the first scene or the quantity information of the data of the scene related to the first scene based on the identification information of the scene related to the first scene. This application supports configuring the association between the scene and the data of the scene in network element 3, as shown in Table 3. The content described in Table 3 is understood only as an example and is not a final limitation.

[0277] Table 3

[0278] As shown in Table 3, scenario 1 corresponds to data 1, scenario 2 corresponds to data 2, and scenario 3 corresponds to data 3. This application supports establishing an association relationship between scenarios and data, and storing or configuring the association relationship between scenarios and data in network element 3. Network element 3 can determine the corresponding data based on the association relationship between scenarios and data and the identification information of the scenarios. In this way, the signaling overhead for exchanging association relationships between scenarios and scenario data can be reduced.

[0279] Optionally, when network element 1 is a model training network element, network element 3 can send data of scenes related to the first scene, and the data of scenes related to the first scene can be used for updating and training the first model. This can effectively utilize the stored data and reduce the amount of data that needs to be obtained from the first scene, which can effectively reduce the overhead of data collection, thereby supporting the reduction of the overall overhead of model updates.

[0280] In summary, by determining the scenes related to the first scene based on the identification information of the first scene and obtaining the data of the scenes related to the first scene, this is conducive to reducing the amount of data that needs to be obtained from the first scene, thereby reducing the overhead of data collection and further reducing the overall overhead of model updates.

[0281] Optionally, when network element 1 is a model training network element, network element 1 can also obtain data of the first scene from network element 3 or a data collection network element, such as network element 2, and use the data of the first scene and the data of scenes related to the first scene to update the parameters that need to be updated in the first model to obtain an updated first model, and the updated first model can be sent to the model deployment network element.

[0282] In one possible implementation, since obtaining data of the first scene can implicitly include obtaining quantity information of the data of the first scene, in S903, when network element 1 is a model training network element, obtaining quantity information of the data of the first scene can be replaced by obtaining data of the first scene.

[0283] Optionally, the method 900 may further include:

[0284] S904. Network element 1 sends information 3 to a data collection network element, such as network element 2. Information 3 is used to indicate the amount of data to be obtained in the first scenario.

[0285] Specifically, when network element 1 determines, based on the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model, network element 1 sends information 3 to a data collection network element, such as network element 2, to indicate the amount of data to be obtained in the first scene. The amount of data to be obtained in the first scene can be determined by network element 1 based on the amount information of data of scenes related to the first scene. For example, network element 1 determines whether network element 3 has stored data of scenes related to the first scene by interacting with network element 3. For example, network element 1 sends an inquiry message carrying identification information of scenes related to the first scene to network element 3. Network element 3 queries whether data of scenes related to the first scene has been stored based on the inquiry message. When network element 1 determines that data of scenes related to the first scene has been stored, network element 1 determines that it does not need to obtain a larger amount of data in the first scene. In this way, it can indicate the amount of data that needs to be obtained from the first scene.

[0286] The information 3 may also be used to instruct to obtain data of the first scene. In this way, the data of the first scene can be obtained.

[0287] Accordingly, a data collection network element, such as network element 2, can initiate a data collection process for the first scenario based on information 3. Through the above embodiments, the present application can effectively reduce the overall overhead of model updates. In particular, the signaling interaction between different network elements described in the above method embodiments can effectively reduce the overall overhead of model updates.

[0288] Finally, the device embodiment of the embodiment of the present application is introduced.

[0289] To implement the various functions of the methods provided herein, both terminal devices and network devices may include hardware structures and / or software modules, with the aforementioned functions implemented in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular function is implemented in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0290] Figure 10 is a schematic block diagram of a communication device 1000 according to an embodiment of the present application. The communication device 1000 includes a processing circuit 1010 and a transceiver circuit 1020, and the processing circuit 1010 and the transceiver circuit 1020 can be interconnected via a bus 1030. The communication device 1000 may include one or more of network element 1, network element 2, or network element 3. In other words, the communication device 1000 may include one or more of a data collection network element (such as network element 2), a scene recognition network element (such as network element 2), a model training network element (such as network element 1), a data storage / generation network element (such as network element 3), or a model deployment network element (such as network element 1).

[0291] Optionally, the communication device 1000 may further include a memory 1040. The memory 1040 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or portable read-only memory (CD-ROM), and is used for related instructions and data.

[0292] The processing circuit 1010 may be one or more central processing units (CPUs). When the processing circuit 1010 is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processing circuit 1010 may be a signal processor, a chip, or other integrated circuit that can implement the method of the present application, or a portion of the processing circuit in the aforementioned processor, chip, or integrated circuit. In addition, the transceiver circuit 1020 may also be a transceiver, or an input / output interface, which is used for input or output of signals or data and may also be referred to as an input / output circuit.

[0293] When the communication device 1000 is the network element 1, illustratively, the processing circuit 1010 is configured to perform the following operations: obtain identification information of the first scenario; determine a first parameter in the first model according to the identification information of the first scenario, etc.

[0294] When the communication device 1000 is the network element 2, illustratively, the processing circuit 1010 is configured to perform the following operations: determine identification information of the first scenario; send the identification information of the first scenario, etc.

[0295] When the communication device 1000 is the network element 3, illustratively, the processing circuit 1010 is configured to perform the following operations: receive information 2 or information 3; send first data to the model training network element, etc.

[0296] The above contents are only for exemplary description. When the communication device 1000 is the network element 1, the network element 2 or the network element 3, it will be responsible for executing the methods or steps related to the network element 1, the network element 2 or the network element 3 in the above method embodiments.

[0297] It is understood that when the communication device 1000 is network element 1, network element 2, or network element 3, the transceiver circuit 1020 is a transceiver. When the communication device 1000 is a chip for network element 1, network element 2, or network element 3, the transceiver circuit 1020 is an input / output circuit. The above description is merely exemplary. For details, please refer to the contents of the above method embodiments. The implementation of each operation in Figure 10 can also correspond to the corresponding description of the method embodiments shown in Figures 6 to 9.

[0298] Figure 11 is a schematic block diagram of a communication device 1100 according to an embodiment of the present application. The communication device 1100 may include one or more of network element 1, network element 2, or network element 3, or may be a chip or module including one or more of network element 1, network element 2, or network element 3. In other words, the communication device 1100 may include one or more of a data collection network element (such as network element 2), a scene recognition network element (such as network element 2), a model training network element (such as network element 1), a data storage network element (such as network element 3), or a model deployment network element (such as network element 1), or may be a chip or module including one or more of a data collection network element (such as network element 2), a scene recognition network element (such as network element 2), a model training network element (such as network element 1), a data storage / generation network element (such as network element 3), or a model deployment network element (such as network element 1), for implementing the methods involved in the above embodiments.

[0299] The communication device 1100 includes a transceiver unit 1110 and a processing unit 1120. The transceiver unit 1110 and the processing unit 1120 are described below by way of example.

[0300] The transceiver unit 1110 may include a transmitting unit and a receiving unit. The transmitting unit is configured to execute a transmitting operation of the communication device, and the receiving unit is configured to execute a receiving operation of the communication device. For ease of description, this embodiment of the application combines the transmitting unit and the receiving unit into a single transceiver unit. This is described here as a unified description and will not be repeated later.

[0301] When the communication device 1100 is a network element 1, illustratively, the transceiver unit 1110 is configured to obtain identification information of a first scenario, and the processing unit 1120 is configured to execute steps related to processing and control of the network element 1. For example, the processing unit 1120 is configured to determine a first parameter in a first model based on the identification information of the first scenario.

[0302] When the communication device 1100 is a network element 2, illustratively, the transceiver unit 1110 is used to receive information 1, and the processing unit 1120 is used to execute the content of the steps involving processing and control of the network element 2. For example, the processing unit 1120 is used to collect data of the first scenario.

[0303] The above description is only for illustrative purposes.

[0304] Among them, when the communication device 1100 includes one or more of network element 1, network element 2 or network element 3, it will be responsible for executing one or more of the methods or steps related to network element 1, network element 2 or network element 3 in the aforementioned method embodiment.

[0305] Optionally, the communication device 1100 further includes a storage unit 1130, which is used to store a program or code for executing the aforementioned method.

[0306] It should be noted that the transceiver unit in FIG11 may correspond to the transceiver circuit in FIG10 , and the processing unit in FIG11 may correspond to the processing circuit in FIG10 .

[0307] The device embodiments shown in Figures 10 and 11 are used to implement the contents described in Figures 6 to 9. The specific execution steps and methods of the devices shown in Figures 10 and 11 can refer to the contents described in the above method embodiments.

[0308] The present application also provides a chip including a processor configured to retrieve and execute instructions stored in a memory, so that a communication device equipped with the chip executes the methods described in the above examples. The memory may be integrated within the chip or located outside the chip.

[0309] The present application also provides another chip, comprising: an input interface, an output interface, and a processor, wherein the input interface, the output interface, and the processor are connected via an internal connection path, and the processor is configured to execute code in a memory. When the code is executed, the processor is configured to execute the methods in the above examples. Optionally, the chip also includes a memory, which is configured to store computer programs or code.

[0310] The present application also provides a processor for coupling with a memory, and for executing the methods and functions involving a network device or a terminal device in any of the above embodiments.

[0311] In another embodiment of the present application, a computer program product including instructions is provided. When the computer program product is run on a computer, the method of the above embodiment is implemented.

[0312] The present application also provides a computer program. When the computer program is executed in a computer, the method of the aforementioned embodiment is implemented.

[0313] In another embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a computer, the method described in the above embodiment is implemented.

[0314] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0315] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. 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), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0316] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0317] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0318] Those skilled in the art will appreciate that the various exemplary units and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented using hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for ease of description and brevity, the specific operating processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other divisions may be used, such as multiple units or components can be combined or integrated into another system, or some features can be omitted or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other can be through some interface, or the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0319] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. If the above functions are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0320] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

Claims

1. A training method for an artificial intelligence model, characterized in that: include: Obtaining identification information of a first scene; A first parameter in the first model is determined according to the identification information of the first scene, where the first parameter is used to determine a parameter that needs to be updated in the first model.

2. The method according to claim 1, characterized in that The determining a first parameter in the first model according to the identification information of the first scene includes: The first parameter is determined according to identification information of the first scene and at least one association relationship, wherein a first association relationship among the at least one association relationship is used to associate the first parameter with the first scene.

3. The method according to claim 2, characterized in that The at least one association relationship is predefined or configured.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The first parameter is transmitted.

5. The method according to claim 4, characterized in that The method further comprises: Determining, according to the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model; A message for instructing to obtain data of the first scenario is transmitted.

6. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Acquire first data, where the first data is related to the first scenario; The first data is used to update the parameters that need to be updated in the first model to obtain the updated first model.

7. The method according to claim 6, characterized in that The first data includes any one of the following: The data of the first scene, the data of the first scene and the data of scenes related to the first scene, or the data generated according to the data features of the data of the first scene and the second data, the second data is the data of the scenes currently supported by the first model, and the scenes related to the first scene belong to the scenes currently supported by the first model.

8. The method according to claim 7, characterized in that The obtaining of the first data includes: Acquire the first data from a data collection network element, where the first data is data of the first scenario; or, Acquire the first data from a data generating network element, where the first data is data generated according to the data of the first scenario and data features of the second data; or, The first data is obtained from a data storage network element, where the first data is data of the first scene and data of scenes related to the first scene.

9. The method according to any one of claims 1 to 8, characterized in that The parameter to be updated in the first model is the first parameter, or, The parameters that need to be updated in the first model are part or all of the parameters in the first model except the first parameter.

10. The method according to any one of claims 7 to 9, characterized in that The data characteristics of the second data include at least one of the following: Signal-to-noise ratio, reference signal received power, or data distribution.

11. The method according to any one of claims 1 to 10, characterized in that The method further comprises: According to the identification information of the first scene, it is determined that the first scene does not belong to the scenes currently supported by the first model.

12. A method for training an artificial intelligence model, characterized in that: include: Obtaining identification information of a first scene; Determining, according to the identification information of the first scene, scenes related to the first scene, where the scenes related to the first scene belong to scenes currently supported by the first model; The quantity information of the scene data related to the first scene is obtained, the scene data related to the first scene is used for updating the first model, and the updated first model supports the first scene.

13. The method according to claim 12, characterized in that The method further comprises: Acquire scene data related to the first scene.

14. The method according to claim 12 or 13, characterized in that The method further comprises: Determining, according to the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model; Send first indication information, where the first indication information is used to indicate the amount of data to be acquired in the first scene, the quantity information of data of scenes related to the first scene is used to determine the amount of data to be acquired in the first scene, and the data of the first scene is used to update the first model.

15. The method according to any one of claims 12 to 14, characterized in that The determining, according to the identification information of the first scene, a scene related to the first scene includes: Determined based on predefined rules, or determined based on feature information of the scene.

16. The method according to any one of claims 12 to 15, characterized in that The first indication information is also used to instruct to obtain data of the first scene.

17. A method for training an artificial intelligence model, characterized in that: include: Obtaining identification information of a first scene; Determining, according to the identification information of the first scene, that the first scene does not belong to the scenes currently supported by the first model; Transmit first indication information, where the first indication information is used to indicate obtaining data of the first scene, the data of the first scene is used to update the first model, and the updated first model supports the first scene.

18. A method for training an artificial intelligence model, characterized in that: include: Get the data of the first scene; Generate data according to the data of the first scenario and the data features of the second data to obtain first data, where the second data is data of the scenario currently supported by the first model, and the first data is used to update the first model, and the updated first model supports the first scenario; The first data is sent.

19. The method according to claim 18, characterized in that The data characteristics of the second data include at least one of the following: Signal-to-noise ratio, reference signal received power, or data distribution.

20. A method for training an artificial intelligence model, characterized in that: include: Determining identification information of a first scene; Send identification information of the first scenario.

21. The method according to claim 20, characterized in that The method further comprises: receiving instruction information; Based on the indication information, data of the first scene is acquired.

22. The method according to claim 21, characterized in that The indication information includes information about the amount of data to be acquired in the first scene; The acquiring data of the first scenario based on the indication information includes: According to the information about the amount of data to be acquired in the first scene, data of the first scene is acquired.

23. A method for training an artificial intelligence model, characterized in that: include: Acquire indication information, where the indication information is used to instruct acquisition of data of a scene related to the first scene, and the indication information includes identification information of the scene related to the first scene; Determine data of a scene related to the first scene according to identification information of a scene related to the first scene and at least one association relationship, wherein a first association relationship in the at least one association relationship is used to associate the scene related to the first scene with data of a scene related to the first scene; Transmit data of a scene related to the first scene.

24. The method according to claim 23, characterized in that The at least one association relationship is predefined or configured.

25. A communication device, characterized in that: comprising a processor configured to, by executing a computer program or instruction, or, by a logic circuit, enabling the communication device to perform the method according to any one of claims 1 to 11; or, enabling the communication device to perform the method according to any one of claims 12 to 16; or, causing the communication device to execute the method according to claim 17; or, causing the communication device to perform the method according to claim 18 or 19; or, causing the communication device to perform the method according to any one of claims 20 to 22; or, The communication device is enabled to perform the method of claim 23 or 24.

26. A communication device, characterized in that: It includes a logic circuit and an input / output interface, wherein the input / output interface is used to input and / or output signals. The logic circuit is used to execute the method according to any one of claims 1 to 11; or, The logic circuit is used to execute the method according to any one of claims 12 to 16; or, The logic circuit is used to execute the method according to claim 17; or, The logic circuit is used to execute the method according to claim 18 or 19; or, The logic circuit is used to execute the method according to any one of claims 20 to 22; or, The logic circuit is configured to execute the method of claim 23 or 24.

27. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program or instruction. When the computer program or the instruction is executed on a computer, so that the method of any one of claims 1 to 11 is performed; or, so that the method of any one of claims 12 to 16 is performed; or, so that the method of claim 17 is performed; or, so that the method of claim 18 or 19 is performed; or, so that the method of any one of claims 20 to 22 is performed; or, The method of claim 23 or 24 is performed.

28. A communication system, characterized in that: The communication system includes a model deployment network element and a data collection network element; The model deployment network element is used to perform the method described in any one of claims 1 to 11; The data collection network element is used to perform the method described in any one of claims 20 to 22; or, The model deployment network element is used to perform the method described in any one of claims 12 to 16; The data collection network element is used to execute the method described in any one of claims 20 to 22.

29. A communication system, characterized in that: The communication system includes a model training network element and a data collection network element; The model training network element is used to perform the method described in any one of claims 1 to 11; The data collection network element is used to perform the method described in any one of claims 20 to 22; or, The model training network element is used to perform the method described in any one of claims 12 to 16; The data collection network element is used to execute the method described in any one of claims 20 to 22.

30. The communication system according to claim 28 or 29, characterized in that: The communication system also includes a data generating network element; The data generation network element is used to execute the method described in claim 18 or 19.

31. The communication system according to any one of claims 28 to 30, characterized in that: The communication system also includes a data storage network element, which is used to execute the method described in claim 23 or 24.

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