Method for transmitting artificial intelligence model, and communication device

Abstract

The present application provides a method for transmitting an AI model, and a communication device. In the method, a first node can request a second node for pre-configuration information of an AI model that can be provided by the second node and a vendor identifier corresponding to the second node, wherein the first node carries in a request message a vendor identifier corresponding to the first node itself, the first node can determine, on the basis of the pre-configuration information of the AI model, an AI model to be requested from the second node, and the second node can determine, on the basis of the vendor identifier corresponding to the first node, whether the first node belongs to the same vendor and thus determine how to subsequently send the AI model needed by the first node. The method provided by the present application can realize cross-network entity transmission of an AI model and avoid the privacy problem of the AI model.

Description

A method and communication device for transmitting artificial intelligence models

[0001] This application claims priority to the Chinese patent application with application number 202311427893.3 filed with the State Intellectual Property Office of China on October 30, 2023, and priority to the Chinese patent application with the invention name “A method and communication device for transmitting artificial intelligence models”, all contents of which are incorporated by reference into this application. Technical Field

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

[0003] Currently, Release 18 (R18) of the RAN3 standard specifies that artificial intelligence (AI) or machine learning (ML) models be trained and inferred separately in different network entities (e.g., gNB and Operation Administration and Maintenance (OAM) elements). For example, specific scenarios include the following: AI training and inference in the gNB, or AI training in the OAM and inference in the gNB.

[0004] Currently, R18 does not involve a mechanism for transmitting AI models across network entities. However, cross-node model transmission has the following benefits: (1) Improving the accuracy of AI prediction information or decision-making. For example, if the accuracy of the AI ​​model of some nodes is too low, high-precision inference information can be obtained by transmitting the high-precision AI model of other nodes across nodes. (2) Nodes without AI model training resources can obtain AI models. For example, if the computing power and / or storage resources of some nodes are insufficient and they cannot train AI models, they can obtain trained AI models by transmitting the AI ​​models of other nodes across nodes. (3) Reducing node resource consumption. For example, in a scenario where the same manufacturer (or manufacturers that have signed an agreement) can share models, it is not necessary for all nodes to train AI models. Some nodes with low computing power can obtain AI models of specific accuracy by transmitting the AI ​​models of other nodes across nodes.

[0005] Furthermore, in the application scenario of transmitting AI models across network entities, when different network entities belong to different manufacturers, cross-node transmission of AI models may raise privacy issues related to AI models. For example, AI models trained by each manufacturer cannot be disclosed to other manufacturers; for another example, the training data involved in each AI model may also contain user privacy data and cannot be disclosed to other manufacturers.

[0006] Therefore, how to achieve cross-network entity transmission of AI models has become a technical problem that needs to be solved.

[0007] Summary of the Invention

[0008] The present application provides a method and communication device for transmitting an artificial intelligence model, which can realize the cross-network entity transmission of the AI ​​model and avoid the privacy issues of the AI ​​model.

[0009] In a first aspect, a method for transmitting an artificial intelligence model is provided. The method can be executed by a device corresponding to a first node, or by a component (e.g., a chip or circuit) of the device corresponding to the first node, without limitation. For example, the first node can be any of the following: a gNB, a gNB-CU, a gNB-DU, a UE, a 5GC, an OAM, etc.

[0010] The method includes: a first node sends a first request message to a second node, the first request message is used to request pre-configuration information of an artificial intelligence (AI) model that can be provided by the second node and the manufacturer identifier corresponding to the second node, and the first request message carries the manufacturer identifier corresponding to the first node; the first node receives a first response message from the second node, the first response message carries the pre-configuration information of the AI ​​model that can be provided by the second node and the manufacturer identifier corresponding to the second node, the pre-configuration information of the AI ​​model includes a first association relationship, the first association relationship is an association relationship between at least two of the following: a model identifier corresponding to the AI ​​model, a model algorithm corresponding to the AI ​​model, a model algorithm identifier corresponding to the AI ​​model algorithm, a business corresponding to the AI ​​model, a business identifier corresponding to the AI ​​model business, a purpose corresponding to the AI ​​model, a purpose identifier corresponding to the purpose, reasoning information corresponding to the AI ​​model, and model information corresponding to the AI ​​model, the first association relationship is used by the first node to determine the first AI model to be requested, wherein the first AI model is one or more of the AI ​​models that can be provided by the second node.

[0011] In this application, the second node can be any one of the following nodes: gNB, gNB-CU, gNB-DU, UE, 5GC, OAM, etc.

[0012] In one possible implementation, the first node and the second node may be different nodes from the same manufacturer; in another possible implementation, the first node and the second node may be nodes from different manufacturers. It can also be understood that in this application, the first node and the second node are not limited.

[0013] In this application, for example, when the first node is operating manufacturer #1, the manufacturer identifier corresponding to the first node can be understood as the vendor ID of operating manufacturer #1, and the manufacturer identifier can be used by the second node to determine whether it is the same manufacturer or a different manufacturer as the first node. In addition, the first node also requests the manufacturer identifier corresponding to the second node from the second node, and the pre-configuration information of the AI ​​model provided by the second node can be associated with the manufacturer identifier of the second node. Alternatively, it can be understood that the first node will determine that the received pre-configuration information of the AI ​​model is sent by the manufacturer corresponding to the second node.

[0014] In this application, model information is used to characterize the characteristics of the AI ​​model, and may include one or more of the following: the input of the AI ​​model, the output of the AI ​​model, the number of layers of the AI ​​model, and the dimension of the AI ​​model.

[0015] Based on the above technical solution, in this application, the first node can request the pre-configuration information of the AI ​​model that the second node can provide and the manufacturer identifier corresponding to the second node from the second node, and the first node carries its own corresponding manufacturer identifier in the request message. The first node can determine the AI ​​model that needs to be requested from the second node based on the pre-configuration information of the AI ​​model, and the second node can determine whether it belongs to the same manufacturer as the first node based on the manufacturer identifier corresponding to the first node, thereby determining how to subsequently send the AI ​​model required by the first node. The method provided in this application can realize the transmission of AI models across network entities and avoid the privacy issues of AI models.

[0016] In one possible implementation, the pre-configuration information of the AI ​​model includes one or more of the following: information on the AI ​​model scrambling method supported by the second node, wherein the information on the AI ​​model scrambling method includes: the association between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the AI ​​model update method supported by the second node, wherein the information on the model update method includes: the association between the AI ​​model update method identifier and the corresponding update method; information on the AI ​​model transmission method supported by the second node, wherein the information on the AI ​​model transmission method includes: the association between the AI ​​model transmission method identifier and the corresponding transmission method.

[0017] In one possible implementation, the first request message also carries pre-configuration information of the AI ​​model provided by the first node to the second node, and the pre-configuration information of the AI ​​model provided by the first node to the second node includes one or more of the following: information on the AI ​​model scrambling method supported by the first node, wherein the information on the AI ​​model scrambling method includes: the association between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the AI ​​model update method supported by the first node, wherein the information on the AI ​​model update method includes: the association between the AI ​​model update method identifier and the corresponding update method; information on the AI ​​model transmission method supported by the first node, wherein the information on the AI ​​model transmission method includes: the association between the AI ​​model transmission method identifier and the corresponding transmission method.

[0018] Based on the above implementation method, in this application, the second node and the first node can communicate with each other about AI model encryption method information, AI model update method information, AI model update method information, and AI model transmission method information, thereby ensuring that the AI ​​model is transmitted in an appropriate manner in the subsequent transmission of the AI ​​model.

[0019] In one possible implementation, the method further includes: the first node sending a second request message to the second node, where the second request message is used to request the first AI model, the second request message carries first indication information, and the first indication information is used to indicate pre-configuration information of the first AI model; the first node receives a second response message from the second node, where the second response message carries second indication information, and the second indication information is used to indicate pre-configuration information of the second AI model, wherein the pre-configuration information of the second AI model is information in the pre-configuration information of the first AI model that the second node supports sending to the first node, and the second AI model is one or more AI models in the first AI model.

[0020] In one possible implementation, the pre-configuration information of the first AI model includes one or more of the following: a model identifier corresponding to the first AI model, model information corresponding to the first AI model, a model algorithm identifier corresponding to the first AI model, a service identifier corresponding to the first AI model, a usage identifier corresponding to the first AI model, an AI model scrambling method identifier supported by the first node, an AI model update method identifier supported by the first node, an AI model transmission method identifier supported by the first node, a reason for requesting the first AI model, and a model accuracy corresponding to the first AI model.

[0021] Based on the above implementation, in this application, the first node can request the required AI model from the second node based on the received first association. Specifically, the pre-configured information of the AI ​​model transmitted by the first and second nodes includes the first association. Subsequently, the first node can only transmit the corresponding identifier to the second node based on the first association, thereby indicating to the second node the AI ​​model to be requested, which can reduce signaling overhead.

[0022] Optionally, the second request message also carries one or more of the following: the period for the second node to report the AI ​​model; the message identifier corresponding to the second request message, wherein the message identifier is used to associate the second response message; the message type corresponding to the second request message.

[0023] In this application, the "message type corresponding to the second request message" may, for example, include the request message type for starting to send the requested AI model (start type), the request message type for stopping sending the requested AI model (stop type), the request message type for adding the requested AI model (add type), the request message type for deleting the requested AI model (del type), and the request message type for updating the requested AI model (revise type).

[0024] In one possible implementation, the pre-configuration information of the second AI model includes one or more of the following: a model identifier corresponding to the second AI model, model information corresponding to the second AI model, a model algorithm identifier corresponding to the second AI model, a service identifier corresponding to the second AI model, and model accuracy feedback information corresponding to the second AI model, wherein the model accuracy feedback information is used to indicate that the second node supports sending the model accuracy corresponding to the second AI model to the first node.

[0025] In one possible implementation, the second response message also carries one or more of the following: reporting cycle feedback information, where the reporting cycle feedback information is used to indicate the AI ​​model reporting cycle supported by the second node; a message identifier; and failure cause indication information, where the failure cause indication information is used to indicate the identifier and reason why the second node does not support the first AI model sent to the first node.

[0026] In the present application, based on the pre-configuration information of the first AI model required as indicated by the first node, the second node can determine the pre-configuration information of the AI ​​model that can be provided to the first node, thereby realizing negotiation between the two nodes.

[0027] In a possible implementation, the method further includes: the first node receiving an AI model update message from the second node, where the AI ​​model update message carries the scrambled second AI model, or the AI ​​model update message carries the address corresponding to the scrambled second AI model, or the AI ​​model update message carries the second AI model, or the AI ​​model update message carries the address corresponding to the second AI model.

[0028] In this application, if the first node and the second node belong to the same manufacturer, the second node can send the real AI model or the address of the AI ​​model to the first node. If the first node and the second node belong to different manufacturers, the second node can send the scrambled AI model or the address of the scrambled AI model to the first node, thereby avoiding privacy issues of the AI ​​model.

[0029] Optionally, the method also includes: the first node determines fourth indication information based on the second indication information, where the fourth indication information is used to indicate pre-configuration information of the third AI model, wherein the pre-configuration information of the third AI model is information determined by the first node to request from the second node in the pre-configuration information of the second AI model, and the third AI model is one or more AI models in the second AI model; the first node sends an AI model confirmation message to the second node, and the AI ​​model confirmation message carries the fourth indication information.

[0030] Optionally, the method further includes: the first node receiving an AI model update message from the second node, where the AI ​​model update message carries the scrambled third AI model, or the AI ​​model update message carries the address corresponding to the scrambled third AI model, or the AI ​​model update message carries the third AI model, or the AI ​​model update message carries the address corresponding to the third AI model.

[0031] A second aspect provides a method for transmitting an AI model. The method can be executed by a device corresponding to a second node, or by a component (e.g., a chip or circuit) of the device corresponding to the second node, without limitation. For example, the second node can be any of the following: a gNB, a gNB-CU, a gNB-DU, a UE, a 5GC, an OAM, etc.

[0032] It should be noted that the beneficial effects achieved by the various methods in the second aspect that are the same as those achieved in the first aspect will not be described again, and can be understood by referring to the beneficial effects in the first aspect.

[0033] The method includes: the second node receives a first request message from the first node, the first request message is used to request the pre-configuration information of the artificial intelligence (AI) model that the second node can provide and the manufacturer identifier corresponding to the second node, and the first request message carries the manufacturer identifier corresponding to the first node; the second node sends a first response message to the first node, the first response message carries the pre-configuration information of the AI ​​model that the second node can provide and the manufacturer identifier corresponding to the second node, the pre-configuration information of the AI ​​model includes a first association relationship, the first association relationship is an association relationship between at least two of the following: a model identifier corresponding to the AI ​​model, a model algorithm corresponding to the AI ​​model, a model algorithm identifier corresponding to the AI ​​model algorithm, a business corresponding to the AI ​​model, a business identifier corresponding to the AI ​​model business, a purpose corresponding to the AI ​​model, a purpose identifier corresponding to the purpose, reasoning information corresponding to the AI ​​model, and model information corresponding to the AI ​​model, the first association relationship is used by the first node to determine the first AI model to be requested, wherein the first AI model is one or more of the AI ​​models that the second node can provide.

[0034] In one possible implementation, the pre-configuration information of the AI ​​model includes one or more of the following: information on the AI ​​model scrambling method supported by the second node, wherein the information on the AI ​​model scrambling method includes: the association between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the AI ​​model update method supported by the second node, wherein the information on the model update method includes: the association between the AI ​​model update method identifier and the corresponding update method; information on the AI ​​model transmission method supported by the second node, wherein the information on the AI ​​model transmission method includes: the association between the AI ​​model transmission method identifier and the corresponding transmission method.

[0035] In one possible implementation, the method further includes: the second node receiving a second request message from the first node, where the second request message is used to request the first AI model and carries first indication information, where the first indication information is used to indicate pre-configuration information of the first AI model; and the second node sending a second response message to the first node, where the second response message carries second indication information, where the second indication information is used to indicate pre-configuration information of the second AI model, where the pre-configuration information of the second AI model is information in the pre-configuration information of the first AI model that the second node supports sending to the first node, and the second AI model is one or more AI models in the first AI model.

[0036] In one possible implementation, the first request message also carries pre-configuration information of the AI ​​model provided by the first node to the second node, and the pre-configuration information of the AI ​​model provided by the first node to the second node includes one or more of the following: information on the AI ​​model scrambling method supported by the first node, wherein the information on the AI ​​model scrambling method includes: the association between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the AI ​​model update method supported by the first node, wherein the information on the AI ​​model update method includes: the association between the AI ​​model update method identifier and the corresponding update method; information on the AI ​​model transmission method supported by the first node, wherein the information on the AI ​​model transmission method includes: the association between the AI ​​model transmission method identifier and the corresponding transmission method.

[0037] In one possible implementation, the pre-configuration information of the first AI model includes one or more of the following: a model identifier corresponding to the first AI model, model information corresponding to the first AI model, a model algorithm identifier corresponding to the first AI model, a service identifier corresponding to the first AI model, a usage identifier corresponding to the first AI model, an AI model scrambling method identifier supported by the first node, an AI model update method identifier supported by the first node, an AI model transmission method identifier supported by the first node, a reason for requesting the first AI model, and a model accuracy corresponding to the first AI model.

[0038] In one possible implementation, the second request message also carries one or more of the following: the period for the second node to report the AI ​​model; the message identifier corresponding to the second request message, wherein the message identifier is used to associate the second response message; and the message type corresponding to the second request message.

[0039] In one possible implementation, the pre-configuration information of the second AI model includes one or more of the following: a model identifier corresponding to the second AI model, model information corresponding to the second AI model, a model algorithm identifier corresponding to the second AI model, a service identifier corresponding to the second AI model, and model accuracy feedback information corresponding to the second AI model, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second AI model sent to the first node.

[0040] In one possible implementation, the second response message also carries one or more of the following: reporting cycle feedback information, where the reporting cycle feedback information is used to indicate the AI ​​model reporting cycle supported by the second node; a message identifier; and failure cause indication information, where the failure cause indication information is used to indicate the identifier and reason why the second node does not support the first AI model sent to the first node.

[0041] In one possible implementation, the method further includes: the second node sending an AI model update message to the first node, where the AI ​​model update message carries the scrambled second AI model, or the AI ​​model update message carries the address corresponding to the scrambled second AI model, or the AI ​​model update message carries the second AI model, or the AI ​​model update message carries the address corresponding to the second AI model.

[0042] In one possible implementation, the method further includes: the second node receiving an AI model confirmation message from the first node, where the AI ​​model confirmation message carries fourth indication information, and the fourth indication information is used to indicate pre-configuration information of a third AI model, wherein the pre-configuration information of the third AI model is information determined by the first node to request from the second node in the pre-configuration information of the second AI model, and the third AI model is one or more AI models in the second AI model.

[0043] In one possible implementation, the method further includes: the AI ​​of the second node receiving a model update message from the first node, where the AI ​​model update message carries the scrambled third AI model, or the AI ​​model update message carries an address corresponding to the scrambled third AI model, or the AI ​​model update message carries the third AI model, or the AI ​​model update message carries the address corresponding to the third AI model.

[0044] In a third aspect, a method for transmitting training data is provided. The method can be executed by a device corresponding to a first node, or by a component (e.g., a chip or circuit) of the device corresponding to the first node, without limitation. For example, the first node can be any of the following: a gNB, a gNB-CU, a gNB-DU, a UE, a 5GC, an OAM, etc.

[0045] The method includes: a first node sends a first request message to a second node, the first request message is used to request pre-configuration information of training data that can be provided by the second node and the manufacturer identifier corresponding to the second node, wherein the first request message carries the manufacturer identifier corresponding to the first node; the first node receives a first response message from the second node, the first response message carries the pre-configuration information of training data that can be provided by the second node and the manufacturer identifier corresponding to the second node, the pre-configuration information of the training data includes a second association relationship, the second association relationship is an association relationship between at least two of the following: a data identifier corresponding to the training data, a model algorithm corresponding to the training data, a model algorithm identifier corresponding to the model algorithm, a business corresponding to the training data, a business identifier corresponding to the business, a purpose corresponding to the training data, and a purpose identifier corresponding to the purpose, the pre-configuration information of the training data is used by the first node to determine the first training data to be requested, and the first training data is part or all of the training data that can be provided by the second node.

[0046] Based on the above technical solution, in this application, the first node can request the pre-configuration information of the training data that the second node can provide and the manufacturer identifier corresponding to the second node from the second node, and the first node carries its own corresponding manufacturer identifier in the request message. The first node can determine the training data that needs to be requested from the second node based on the pre-configuration information of the training data, and the second node can determine whether it belongs to the same manufacturer as the first node based on the manufacturer identifier corresponding to the first node, thereby determining how to subsequently send the training data required by the first node. The method provided in this application can realize the transmission of training data across network entities and avoid the privacy issues of training data.

[0047] The beneficial effects achieved by the following implementation methods can refer to the beneficial effects achieved by the various implementation methods in the first aspect mentioned above. The only difference is that the "AI model" in the beneficial effects of the first aspect can be replaced with "training data" for understanding.

[0048] In one possible implementation, the pre-configuration information of the training data that the second node can provide includes one or more of the following: information on the training data scrambling method supported by the second node, wherein the information on the training data scrambling method includes: the association between the training data scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the training data updating method supported by the second node, wherein the information on the training data updating method includes: the association between the training data updating method identifier and the corresponding updating method; information on the training data transmission method supported by the second node, wherein the information on the training data transmission method includes: the association between the training data transmission method identifier and the corresponding transmission method.

[0049] In a possible implementation, the method further includes: the first node sends a second request message to the second node, the second request message is used to request the first training data, the second request message carries first indication information, and the first indication information is used to indicate pre-configuration information of the first training data; the first node receives a second response message from the second node, the second response message carries second indication information, and the second indication information is used to indicate pre-configuration information of the second training data, wherein the pre-configuration information of the second training data is information in the pre-configuration information of the first training data that the second node supports sending to the first node, and the second training data is part or all of the first training data.

[0050] In one possible implementation, the first request carries pre-configuration information of training data provided by the first node to the second node, and the pre-configuration information of training data provided by the first node to the second node includes one or more of the following: information on the training data scrambling method supported by the first node, wherein the information on the training data scrambling method includes: an association between a training data scrambling method identifier and a corresponding scrambling method and / or scrambling level; information on the training data update method supported by the first node, wherein the information on the training data update method includes: an association between a training data update method identifier and a corresponding update method; information on the training data transmission method supported by the first node, wherein the information on the training data transmission method includes: an association between a training data transmission method identifier and a corresponding transmission method.

[0051] In one possible implementation, the pre-configuration information of the first training data includes one or more of the following: a data identifier corresponding to the first training data, a model algorithm identifier corresponding to the first training data, a service identifier corresponding to the first training data, a usage identifier corresponding to the first training data, a training data scrambling method identifier supported by the first node, a training data update method identifier supported by the first node, a training data transmission method identifier supported by the first node, a reason for requesting the first training data, and a model accuracy corresponding to the first training data.

[0052] In one possible implementation, the second request message also carries one or more of the following: the period for the second node to report training data; the message identifier corresponding to the second request message, wherein the message identifier is used to associate the second response message; and the message type corresponding to the second request message.

[0053] In one possible implementation, the pre-configuration information of the second training data includes one or more of the following: a model identifier corresponding to the second training data, a model algorithm identifier corresponding to the second training data, a service identifier corresponding to the second training data, and model accuracy feedback information corresponding to the second training data, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second training data sent to the first node.

[0054] In one possible implementation, the second response message also carries one or more of the following: reporting cycle feedback information, the reporting cycle feedback information is used to indicate the reporting cycle of the second response message supported by the second node; a message identifier; and failure cause indication information, the failure cause indication information is used to indicate the identifier and reason why the second node does not support the first training data sent to the first node.

[0055] In a possible implementation, the method further includes: the first node receiving a training data update message from the second node, where the training data update message carries the scrambled second training data, or the training data update message carries an address corresponding to the scrambled second training data.

[0056] In a possible implementation, the method further includes: the first node determining fourth indication information based on the second indication information, where the fourth indication information is used to indicate pre-configuration information of the third training data, wherein the pre-configuration information of the third training data is information determined by the first node to request from the second node in the pre-configuration information of the second training data, and the third training data is part or all of the second training data; and the first node sends a training data confirmation message to the second node, where the training data confirmation message carries the fourth indication information.

[0057] In a possible implementation, the method further includes: the first node receiving a training data update message from the second node, the training data update message carrying the encrypted third training data AI model, or the training data update message carrying the address corresponding to the encrypted third training data.

[0058] A fourth aspect provides a method for transmitting training data. The method may be performed by a device corresponding to a second node, or by a component (e.g., a chip or circuit) of the device corresponding to the second node, without limitation. For example, the second node may be any of the following: a gNB, a gNB-CU, a gNB-DU, a UE, a 5GC, an OAM, etc.

[0059] The method includes: a second node receives a first request message from a first node, the first request message is used to request pre-configuration information of training data that can be provided by the second node and the manufacturer identifier corresponding to the second node, wherein the first request message carries the manufacturer identifier corresponding to the first node; the second node sends a first response message to the first node, the first response message carries the pre-configuration information of training data that can be provided by the second node and the manufacturer identifier corresponding to the second node, the pre-configuration information of the training data includes a second association relationship, the second association relationship is an association relationship between at least two of the following: a data identifier corresponding to the training data, a model algorithm corresponding to the training data, a model algorithm identifier corresponding to the model algorithm, a business corresponding to the training data, a business identifier corresponding to the business, a purpose corresponding to the training data, and a purpose identifier corresponding to the purpose, the pre-configuration information of the training data is used by the first node to determine the first training data to be requested, and the first training data is part or all of the training data that can be provided by the second node.

[0060] In one possible implementation, the pre-configuration information of the training data that the second node can provide includes one or more of the following: information on the training data scrambling method supported by the second node, wherein the information on the training data scrambling method includes: the association between the training data scrambling method identifier and the corresponding scrambling method and / or scrambling level; information on the training data updating method supported by the second node, wherein the information on the training data updating method includes: the association between the training data updating method identifier and the corresponding updating method; information on the training data transmission method supported by the second node, wherein the information on the training data transmission method includes: the association between the training data transmission method identifier and the corresponding transmission method.

[0061] In a possible implementation, the method further includes: the second node receiving a second request message from the first node, the second request message being used to request first training data, the second request message carrying first indication information, the first indication information being used to indicate pre-configuration information of the first training data; the second node sending a second response message to the first node, the second response message carrying second indication information, the second indication information being used to indicate pre-configuration information of the second training data, wherein the pre-configuration information of the second training data is information in the pre-configuration information of the first training data that the second node supports sending to the first node, and the second training data is part or all of the first training data.

[0062] In one possible implementation, the first request carries pre-configuration information of training data provided by the first node to the second node, and the pre-configuration information of training data provided by the first node to the second node includes one or more of the following: information on the training data scrambling method supported by the first node, wherein the information on the training data scrambling method includes: an association between a training data scrambling method identifier and a corresponding scrambling method and / or scrambling level; information on the training data update method supported by the first node, wherein the information on the training data update method includes: an association between a training data update method identifier and a corresponding update method; information on the training data transmission method supported by the first node, wherein the information on the training data transmission method includes: an association between a training data transmission method identifier and a corresponding transmission method.

[0063] In one possible implementation, the pre-configuration information of the first training data includes one or more of the following: a data identifier corresponding to the first training data, a model algorithm identifier corresponding to the first training data, a service identifier corresponding to the first training data, a usage identifier corresponding to the first training data, a training data scrambling method identifier supported by the first node, a training data update method identifier supported by the first node, a training data transmission method identifier supported by the first node, a reason for requesting the first training data, and a model accuracy corresponding to the first training data.

[0064] In one possible implementation, the second request message also carries one or more of the following: the period for the second node to report training data; the message identifier corresponding to the second request message, wherein the message identifier is used to associate the second response message; and the message type corresponding to the second request message.

[0065] In one possible implementation, the pre-configuration information of the second training data includes one or more of the following: a model identifier corresponding to the second training data, a model algorithm identifier corresponding to the second training data, a service identifier corresponding to the second training data, and model accuracy feedback information corresponding to the second training data, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second training data sent to the first node.

[0066] In one possible implementation, the second response message also carries one or more of the following: reporting cycle feedback information, the reporting cycle feedback information is used to indicate the reporting cycle of the training data supported by the second node; a message identifier; and failure cause indication information, the failure cause indication information is used to indicate the identifier and reason why the second node does not support the first training data sent to the first node.

[0067] In a possible implementation, the method further includes: the second node sending a training data update message to the first node, where the training data update message carries the scrambled second training data, or the training data update message carries an address corresponding to the scrambled second training data.

[0068] In a possible implementation, the method further includes: the second node sending a training data confirmation message to the first node, the training data confirmation message carrying the fourth indication information, the fourth indication information being used to indicate pre-configuration information of the third training data, wherein the pre-configuration information of the third training data is information determined by the first node to request from the second node in the pre-configuration information of the second training data, and the third training data is part or all of the second training data.

[0069] In a possible implementation, the method further includes: the second node sending a training data update message to the first node, the training data update message carries the encrypted third training data AI model, or the training data update message carries the address corresponding to the encrypted third training data.

[0070] In a fifth aspect, a communication device is provided, which is configured to execute the method of any possible implementation of the first and third aspects. Specifically, the device may include units and / or modules, such as a transceiver unit and / or a processing unit, configured to execute the method of any possible implementation of the first and third aspects.

[0071] In one implementation, the device is a first node. When the device is a communication device, the communication unit may be a transceiver or an input / output interface; the processing unit may be at least one processor. Alternatively, the transceiver may be a transceiver circuit. Alternatively, the input / output interface may be an input / output circuit.

[0072] In another implementation, the device is a chip, chip system, or circuit for the first node. When the device is a chip, chip system, or circuit for a communication device, the communication unit may be an input / output interface, interface circuit, output circuit, input circuit, pin, or related circuit on the chip, chip system, or circuit; and the processing unit may be at least one processor, processing circuit, or logic circuit.

[0073] In a sixth aspect, a communication device is provided, which is configured to execute the method of any possible implementation of the second aspect or the fourth aspect. Specifically, the device may include units and / or modules, such as a transceiver unit and / or a processing unit, configured to execute the method of any possible implementation of the second aspect or the fourth aspect.

[0074] In one implementation, the device is a second node. When the device is a communication device, the communication unit may be a transceiver or an input / output interface; the processing unit may be at least one processor. Alternatively, the transceiver may be a transceiver circuit. Alternatively, the input / output interface may be an input / output circuit.

[0075] In another implementation, the device is a chip, chip system, or circuit for the second node. When the device is a chip, chip system, or circuit for a communication device, the communication unit may be an input / output interface, interface circuit, output circuit, input circuit, pin, or related circuit on the chip, chip system, or circuit; and the processing unit may be at least one processor, processing circuit, or logic circuit.

[0076] In a seventh aspect, a communication device is provided, comprising: at least one processor configured to execute a computer program or instruction stored in a memory to perform the method of any possible implementation of any of the first and third aspects. Optionally, the device further comprises a memory configured to store the computer program or instruction. Optionally, the device further comprises a communication interface, through which the processor reads the computer program or instruction stored in the memory.

[0077] In one implementation, the device is a first node.

[0078] In another implementation, the device is a chip, a chip system, or a circuit for the first node.

[0079] In an eighth aspect, a communication device is provided, comprising: at least one processor configured to execute a computer program or instruction stored in a memory to perform the method of any possible implementation of any of the second and fourth aspects. Optionally, the device further comprises a memory configured to store the computer program or instruction. Optionally, the device further comprises a communication interface, and the processor reads the computer program or instruction stored in the memory through the communication interface.

[0080] In one implementation, the device is a second node.

[0081] In another implementation, the device is a chip, a chip system, or a circuit for the second node.

[0082] In a ninth aspect, the present application provides a processor, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method of any possible implementation of any one of the first to fourth aspects.

[0083] In a specific implementation, the processor may be one or more chips, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, or various logic circuits. The input signal received by the input circuit may be, for example, but not limited to, received and input by a transceiver, and the signal output by the output circuit may be, for example, but not limited to, output to and transmitted by a transmitter. The input circuit and the output circuit may be the same circuit, which functions as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation of the processor and various circuits.

[0084] For the operations such as sending and acquiring / receiving involved in the processor, unless otherwise specified, or if they do not conflict with their actual functions or internal logic in the relevant descriptions, they can be understood as processor output, reception, input and other operations, and can also be understood as sending and receiving operations performed by the radio frequency circuit and antenna. This application does not limit this.

[0085] In a tenth aspect, a processing device is provided, comprising a processor and a memory. The processor is configured to read instructions stored in the memory, receive signals via a transceiver, and transmit signals via a transmitter, to execute the method of any possible implementation of any one of aspects 1 to 4.

[0086] Optionally, there are one or more processors and one or more memories.

[0087] Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

[0088] In the specific implementation process, the memory can be a non-transitory memory, such as a read-only memory (ROM), which can be integrated with the processor on the same chip or can be set on different chips. The embodiments of the present application do not limit the type of memory and the setting method of the memory and the processor.

[0089] It should be understood that related data interaction processes, such as sending indication information, can be the process of outputting indication information from the processor, and receiving capability information can be the process of receiving input capability information from the processor. Specifically, data output by the processor can be output to the transmitter, and input data received by the processor can be received from the transceiver. The transmitter and transceiver can be collectively referred to as a transceiver.

[0090] The processing device in the ninth aspect may be one or more chips. The processor in the processing device may be implemented in hardware or software. When implemented in hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor implemented by reading software code stored in a memory, which may be integrated into the processor or located independently of the processor.

[0091] In an eleventh aspect, a computer-readable storage medium is provided, which stores a program code for execution by a device, wherein the program code includes a method for executing any possible implementation of the first to fourth aspects above.

[0092] In a twelfth aspect, a computer program product comprising instructions is provided, which, when run on a computer, enables the computer to execute the method in any possible implementation of the first to fourth aspects above.

[0093] In the thirteenth aspect, a chip system is provided, comprising a processor for calling and running a computer program from a memory, so that a device equipped with the chip system executes the methods in each implementation of any one of the first to fourth aspects above.

[0094] In a fourteenth aspect, a communication system is provided, comprising the first node and the second node. The first node is configured to perform any possible implementation method of any of the first and third aspects, and the second node is configured to perform any possible implementation method of any of the second and fourth aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] FIG1 is a schematic structural diagram of a communication system.

[0096] FIG2 is a schematic diagram of a CU-DU architecture provided in this application.

[0097] FIG3 is a schematic diagram of a neuron structure.

[0098] FIG4 is a schematic diagram of the layer relationship of a neural network.

[0099] Figure 5 is a schematic diagram of the framework for training and reasoning of an AI model provided in this application.

[0100] Figure 6 is a schematic diagram of an AI application framework in NR applicable to this application.

[0101] FIG7 is a schematic flowchart of a method 700 for transmitting an artificial intelligence model provided in the present application.

[0102] FIG8 is a schematic flowchart of a method 800 for transmitting training data provided in this application.

[0103] FIG9 is a schematic block diagram of a communication device 900 provided in this application.

[0104] FIG10 is a schematic block diagram of a communication device 1000 provided in this application. DETAILED DESCRIPTION

[0105] The technical solution in this application will be described below with reference to the accompanying drawings.

[0106] The technology provided in this application can be applied to various communication systems. For example, the communication system can be a fourth-generation (4G) communication system (such as a long-term evolution (LTE) system), a fifth-generation (5G) communication system, a world-wide interoperability for microwave access (WiMAX) or a wireless local area network (WLAN) system, a satellite communication system, a future communication system such as a sixth-generation (6G) mobile communication system, or a fusion system of multiple systems. Among them, the 5G communication system can also be called a new radio (NR) system. Satellite communication system, future communication system such as a sixth-generation (6G) mobile communication system, or a fusion system of multiple systems.

[0107] A device in a communication system can send a signal to another device or receive a signal from another device, wherein the signal may include information, signaling or data, etc. Among them, the device can also be replaced by an entity, a network entity, a communication device, a communication module, a node, a communication node, etc. For example, the communication system may include at least one terminal device and at least one network device. For another example, the communication system may include a training 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 access network device. In addition, it can be understood that if the communication system includes multiple terminal devices, the multiple terminal devices can also send signals to each other, that is, the signal sending network element and the signal receiving network element can both be terminal devices. It can be understood that the terminal device in the present application can be replaced by the first device, and the network device can be replaced by the second device, and the two perform the corresponding communication methods in the present disclosure.

[0108] The method provided in the embodiment of the present application can be applied to wireless communication systems such as 5G, 6G, and satellite communications. Referring to Figure 1, Figure 1 is a simplified schematic diagram of a wireless communication system provided in an embodiment of the present application. As shown in Figure 1, the wireless communication system includes a wireless access network 100 (an example of a network device). The wireless access network 100 can be a next-generation (e.g., 6G or higher) wireless access network, or a traditional (e.g., 5G, 4G, 3G, or 2G) wireless access network. One or more communication devices (120a-120j, collectively referred to as 120) can be connected to each other or to one or more network devices (110a, 110b, collectively referred to as 110) in the wireless access network 100. Optionally, Figure 1 is only a schematic diagram, and the wireless communication system may also include other devices, such as core network devices, wireless relay devices, and / or wireless backhaul devices, which are not shown in Figure 1.

[0109] Optionally, in actual applications, the wireless communication system may include multiple network devices (e.g., access network devices) at the same time, or may include multiple communication devices at the same time. A network device may serve one or more communication devices at the same time. A communication device may also access one or more network devices at the same time. The embodiments of the present application do not limit the number of communication devices and network devices included in the wireless communication system.

[0110] The network device may be an entity on the network side for transmitting or receiving signals. The network device may be an access device for a communication device to access the wireless communication system in a wireless manner, for example, the network device may be a base station. The base station can broadly cover the following various names, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), access network equipment in open radio access network (O-RAN), relay station, access point, transmission point (TRP), transmitting point (TP), master station MeNB, secondary station SeNB, multi-standard radio (MSR) node, home base station, network controller, access node, radio node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), centralized unit control plane (CU-CP) node, centralized unit user plane (CU-UP) node, positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The network device can also refer to a communication module, a modem or a chip for being arranged in the aforementioned device or apparatus. The network device can also be a mobile switching center and a device to device (Device-to-Device, D2D), vehicle outreach (vehicle-to-everything, V2X), a device that performs the base station function in machine to machine (machine-to-machine, M2M) communications, a network side device in a 6G network, a device that performs the base station function in a future communication system, etc. The network device can support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form adopted by the network device.

[0111] In a network structure, the network device may also refer to a centralized unit (CU) or a distributed unit (DU), or the network device may also be composed of a CU and a DU. CU and DU can be understood as a division of the base station from a logical function perspective. Among them, the CU and DU can be physically separated or deployed together, and the embodiments of the present application do not specifically limit this. A CU can be connected to a DU, or multiple DUs can share a CU, which can save costs and facilitate network expansion. The division of CU and DU can be based on the protocol stack. One possible way is to deploy the Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP) and Packet Data Convergence Protocol (PDCP) layers in the CU, and the remaining Radio Link Control (RLC) layer, Media Access Control (MAC) layer and physical layer in the DU. The present application does not limit the above-mentioned protocol stack segmentation method, and other segmentation methods may be used. For details, please refer to the technical research report (TR) 38.801v14.0.0.

[0112] Figure 2 shows a schematic diagram of a CU-DU architecture. As shown in Figure 2, the CU and DU are connected via the F1 interface. The CU represents the gNB and is connected to the core network via the Ng interface. In the embodiments of the present application, the network device may also refer to a centralized unit control plane (CU-CP) node or a centralized unit user plane (CU-UP) node, or the network device may be a CU-CP and a CU-UP. The CU-CP is responsible for control plane functions, primarily including RRC and PDCP-C. PDCP-C is primarily responsible for encryption and decryption, integrity protection, and data transmission of control plane data. The CU-UP is responsible for user plane functions, primarily including SDAP and PDCP-U. SDAP is primarily responsible for processing core network data and mapping flows to bearers. PDCP-U is primarily responsible for encryption and decryption, integrity protection, header compression, sequence number maintenance, and data transmission of the data plane. The CU-CP and CU-UP are connected via the E1 interface. The CU-CP represents the gNB and is connected to the core network via the Ng interface. It is connected to the DU via F1-C (control plane). The CU-UP is connected to the DU via F1-U (user plane). Of course, another possible implementation is that PDCP-C is also in CU-UP.

[0113] The access network device mentioned in the embodiments of the present application may be a device including a CU, or a DU, or a device including a CU and a DU, or a device including a control plane CU node (CU-CP node) and a user plane CU node (CU-UP node) and a DU node.

[0114] Network devices can be fixed or mobile. For example, base stations 110a, 110b (examples of network devices) are stationary and are responsible for wireless transmission and reception in one or more cells from the communication device 120. The helicopter or drone 120i shown in Figure 1 can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station 120i. In other examples, the helicopter or drone (120i) can be configured to serve as a communication device that communicates with the base station 110b.

[0115] In the present application, the communication device used to implement the above network functions can be, for example, an access network device, or a network device with some functions of the access network, or a device that can support the implementation of the access network function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. The device can be installed in the access network device or used in combination with the access network device.

[0116] In this application, a communication device can be an entity on the user side for receiving or transmitting signals, such as a mobile phone. The communication device can be used to connect people, objects and machines. The communication device can communicate with one or more core networks through network equipment. The communication device includes a handheld device with wireless connection function, other processing equipment connected to a wireless modem, or a vehicle-mounted device. The communication device can be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device. The communication device 120 can be widely used in various scenarios, such as cellular communication, device-to-device D2D, vehicle-to-everything V2X, end-to-end P2P, machine-to-machine M2M, machine-type communication MTC, Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc. Some examples of the communication device 120 include: 3GPP standard user equipment (UE), fixed equipment, mobile equipment, handheld equipment, wearable equipment, cellular phones, smart phones, Session Initiation Protocol (SIP) phones, laptops, personal computers, smart books, vehicles, satellites, Global Positioning System (GPS) equipment, target tracking equipment, drones, helicopters, aircraft, ships, remote control equipment, smart home equipment, industrial equipment, personal communication service (PCS) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), wireless network cameras, tablet computers, handheld computers, mobile internet devices (MIDs), wearable devices such as smart watches, virtual reality (VR) equipment, augmented reality (AR) equipment, wireless terminals in industrial control, terminals in vehicle networking systems, wireless terminals in self-driving cars, wireless terminals in smart grids, wireless terminals in transportation safety, and smart cities. The communication device 120 may be a wireless device in various scenarios described above or a device configured for use with a wireless device, such as a communication module, modem, or chip in the aforementioned devices.A communication device may also be referred to as a terminal, terminal equipment, user equipment (UE), mobile station (MS), mobile terminal (MT), etc. A communication device may also be a communication device in a future wireless communication system. A communication device may be used in a dedicated network device or a general-purpose device. The embodiments of this application do not limit the specific technology and specific device form used by the communication device.

[0117] Alternatively, a communication device can function as a base station. For example, a UE can function as a dispatching entity, providing sidelink signals between UEs in V2X, D2D, or P2P scenarios. As shown in Figure 1 , a cell phone 120a and a car 120b communicate with each other using sidelink signals. Cell phone 120a and smart home device 120e communicate without relaying the communication signals through base station 110b.

[0118] In this application, a communication device for implementing the functions of a communication device may be a terminal device, a terminal device having some of the functions of the above communication device, or a device capable of supporting the functions of the above communication device, such as a chip system, which can be installed in the terminal device or used in conjunction with the terminal device. In this application, a chip system may be composed of a chip, or may include a chip and other discrete devices.

[0119] Optionally, a wireless communication system is typically composed of cells, with base stations providing cell management and communication services to multiple mobile stations (MS) in the cell. The base station includes a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU can be placed in different locations, for example: the RRU is remote and placed in an area with high traffic volume, while the BBU is placed in a central computer room. The BBU and RRU can also be placed in the same computer room. The BBU and RRU can also be different components under the same rack. Optionally, a cell can correspond to a carrier or component carrier.

[0120] It should be understood that the number and type of each device in the communication system shown in Figure 1 are for illustration only, and the present application is not limited to this. In actual applications, the communication system may also include more terminal devices, more network devices, and other network elements, such as core network devices, and / or network elements for implementing artificial intelligence functions.

[0121] The wireless access network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on the water; and can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the wireless access network equipment and terminal equipment.

[0122] The embodiments of the present application can be applied to downlink signal transmission, uplink signal transmission, and device-to-device (D2D) signal transmission. For downlink signal transmission, the transmitting device is a wireless access network device, and the corresponding receiving device is a terminal device. For uplink signal transmission, the transmitting device is a terminal device, and the corresponding receiving device is a wireless access network device. For D2D signal transmission, the transmitting device is a terminal device, and the corresponding receiving device is also a terminal device. The embodiments of the present application do not limit the direction of signal transmission.

[0123] The wireless access network device and the terminal device, as well as the terminal device and the terminal device, can communicate through the licensed spectrum (licensed spectrum), can also communicate through the unlicensed spectrum (unlicensed spectrum), or can also communicate through the licensed spectrum and the unlicensed spectrum at the same time. The wireless access network device and the terminal device, as well as the terminal device and the terminal device, can communicate through the spectrum below 6 gigahertz (GHz), can also communicate through the spectrum above 6G, and can also communicate using the spectrum below 6G and the spectrum above 6G at the same time. The embodiments of the present application do not limit the spectrum resources used between the wireless access network device and the terminal device.

[0124] To facilitate understanding of the technical solutions provided by this application, the following first briefly introduces the professional terms involved in this application. It should be understood that this introduction does not limit this application.

[0125] 1. AI Model

[0126] An AI model is a concrete implementation of AI technology. It represents the mapping relationship 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 machine learning (ML) models.

[0127] Depending on the specific methods and / or technologies used to implement artificial intelligence, AI models may also be referred to as machine learning models, deep learning models, or reinforcement learning models. Machine learning is a method for implementing artificial intelligence. Its goal is to design and analyze algorithms (also known as "models") that allow computers to automatically "learn." The designed algorithms are called "machine learning models." Machine learning models are a type of algorithm that automatically analyzes data to obtain patterns and uses these patterns to make predictions about unknown data. There are many different types of machine learning models. Depending on whether model training requires the labels corresponding to the training data, machine learning models can be divided into supervised learning models and unsupervised learning models. The following mainly introduces "supervised learning models."

[0128] 2. Deep neural network (DNN)

[0129] DNNs are a specific implementation of machine learning. 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, DNN-based deep learning communication systems can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0130] The idea of ​​DNN comes from the neuron structure of the brain. Each neuron performs a weighted sum operation on its input value and generates an output through a nonlinear function, as shown in Figure 3. Specifically, suppose the input of the neuron is x = [x0,…,x n ], and the weight corresponding to the input is d=[d0,…,d n ], the bias of the weighted sum is b, and the form of the nonlinear function can be diversified. For example, if the nonlinear function is max{0,x}, the effect of the execution of a neuron can be At this time, the weight d=[d0,…,d n ] and bias b can be understood as parameters of the model.

[0131] DNNs typically have a multi-layered structure, with each layer containing multiple neurons. The input layer processes the values ​​received by the neurons and then passes them to the intermediate hidden layer. Similarly, the hidden layer passes the computational results to the final output layer, generating the DNN's final output, as shown in Figure 4. DNNs typically have more than one hidden layer, which directly impacts the DNN's ability to extract information and fit functions. Increasing the number of hidden layers or increasing the width of each layer can improve the DNN's function fitting capabilities. The weighted values ​​in each neuron are the parameters of the DNN network model. These model parameters are optimized through training, enabling the DNN network to extract data features and express mapping relationships.

[0132] 3. AI model training and inference

[0133] Before any AI model can be used to solve a specific technical problem, it must be trained. As shown in Figure 5, AI model training involves using a specified initial model to calculate training data. Based on the calculation results, the parameters in the initial model are adjusted using a specific method, allowing the model to gradually learn certain patterns and acquire specific functions. After training, a stable AI model can be used for inference. AI model inference is the process of using a trained AI model to calculate input data and obtain predicted inference results.

[0134] During the training phase, it is first necessary to build a training set for the deep learning model based on the goal. The training set includes multiple training data, each of which is set with a label. The label of the training data is the correct answer to the specific question of the training data. The label can represent the goal of training the deep learning model using the training data.

[0135] When training a deep learning model, training data can be input in batches into the deep learning model after parameter initialization. The deep learning model performs calculations (i.e., "inference") on the training data to obtain prediction results for the training data. The prediction results obtained through inference and the labels corresponding to the training data are used as data for calculating the loss according to the loss function. The loss function is used to calculate the gap (i.e., "loss value") between the model's prediction results for the training data and the labels of the training data during the model training phase. The loss function can be implemented using different mathematical functions. Common loss function expressions include: mean square error loss function, logarithmic loss function, least squares method, etc. Model training is an iterative process. Each iteration performs inference on different training data and calculates the loss value. The goal of multiple iterations is to continuously update the parameters of the deep learning model and find the parameter configuration that minimizes or stabilizes the loss value of the loss function.

[0136] 4. Training data and inference data

[0137] "Training data" is used to train AI models. The training data may include the input of the AI ​​model, or the input and target output of the AI ​​model. The training data includes one or more training data. The training data may be a training sample input to the AI ​​model, or the target output of the AI ​​model. The target output may also be referred to as a "label" or a "label sample." Training data is an important part of machine learning. Model training is essentially learning certain features from the training data so that the output of the AI ​​model is as close as possible to the target output, for example, making the difference between the output of the AI ​​model and the target output as small as possible. The composition and selection of the training data set can, to a certain extent, determine the performance of the trained AI model. The performance of the model can be measured, for example, by "loss value" and "inference accuracy."

[0138] In addition, during the training process of an AI model (such as a neural network), a loss function can be defined. The loss function describes the gap or difference between the output value of the AI ​​model and the target output value. This application does not limit the specific form of the loss function. The training process of the AI ​​model is a process of adjusting the model parameters of the AI ​​model so that the value of the loss function is less than the threshold, or the value of the loss function meets the target requirements. For example, the AI ​​model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers, width, weights of neurons, or parameters in the activation function of neurons.

[0139] Inference data can be used as input to a trained AI model for inference. During the inference process, the inference data is input into the AI ​​model, and the corresponding output is the inference result.

[0140] Figure 6 is a diagram of an AI application framework in NR applicable to the present application. As shown in Figure 6, data collection 610 can store data input from gNBs, gNB-CUs, gNB-DUs, UEs, or other management entities as a database for AI model training and data analysis and inference. Model training 620 analyzes the training data provided by data collection 610 to generate an optimal AI model. Model inference 630 uses the AI ​​model to provide reasonable AI-based predictions about network operation based on the data provided by data collection 610, or to guide the network to make policy adjustments. The relevant policy adjustments are uniformly planned by action entity 640 (actor) and sent to multiple network entities for execution. At the same time, after the relevant policies are applied, the specific performance of the network is again input into the database for storage.

[0141] Currently, Release 18 (R18) of the RAN3 standard specifies that artificial intelligence (AI) or machine learning (ML) models be trained and inferred separately in different network entities (e.g., gNB and Operation Administration and Maintenance (OAM) elements). For example, specific scenarios include the following: AI training and inference in the gNB, or AI training in the OAM and inference in the gNB. Currently, R18 does not involve the transmission of AI models across network entities. However, the cross-node transmission of models has the following benefits: (1) Improving the accuracy of AI prediction information or decision-making. For example, if the accuracy of the AI ​​model of some nodes is too low, high-precision inference information can be obtained by transmitting the high-precision AI model of other nodes across nodes. (2) Nodes without AI model training resources can obtain AI models. For example, if the computing power and / or storage resources of some nodes are insufficient and cannot train AI models, they can obtain trained AI models by transmitting the AI ​​models of other nodes across nodes. (3) Reducing the resource consumption of nodes. For example, in the scenario where the same manufacturer (or manufacturers that have signed an agreement) can share models, it is not necessary for all nodes to train AI models. Some nodes with low computing power can obtain AI models of specific accuracy by transmitting the AI ​​models of other nodes across nodes. However, in the application scenario of transmitting AI models across network entities, when different network entities belong to different manufacturers, the cross-node transmission of AI models may involve privacy issues related to AI models. For example, AI models trained by individual manufacturers cannot be exposed to other manufacturers. Another example is that the training data used by each AI model may also contain user privacy data and cannot be exposed to other manufacturers. Therefore, how to achieve cross-network entity transmission of AI models has become a technical problem that needs to be solved.

[0142] In view of this, the present application provides a method for transmitting an AI model, in which a first node can request a second node for pre-configuration information of an AI model that the second node can provide and a manufacturer identifier corresponding to the second node, and the first node carries its own corresponding manufacturer identifier in the request message. The first node can determine the AI ​​model that needs to be requested from the second node based on the pre-configuration information of the AI ​​model, and the second node can determine whether it belongs to the same manufacturer as the first node based on the manufacturer identifier corresponding to the first node, thereby determining how to subsequently send the AI ​​model required by the first node. The method provided in the present application can realize the transmission of AI models across network entities and avoid the privacy issues of AI models.

[0143] It should be noted that the “AI model” in this application can also be replaced by the “ML model”. For the convenience of description, the following unified description is “AI model”.

[0144] FIG7 is a schematic flow chart of a method 700 for transmitting an AI model provided by the present application. As shown in FIG7 , the method includes:

[0145] 710. The first node sends a first request message to the second node. The first request message is used to request the pre-configuration information of the AI ​​model that the second node can provide and the manufacturer identifier corresponding to the second node. The first request message carries the manufacturer identifier corresponding to the first node.

[0146] Correspondingly, the second node receives the first request message from the first node.

[0147] In this application, the first node and the second node can be any of the following nodes: gNB, gNB-CU, gNB-DU, UE, 5GC, OAM, etc.

[0148] In one possible implementation, the first node and the second node may be different nodes from the same manufacturer; in another possible implementation, the first node and the second node may be nodes from different manufacturers. It can also be understood that in this application, the first node and the second node are not limited.

[0149] In this application, for example, when the first node is operating manufacturer #1, the manufacturer identifier corresponding to the first node can be understood as the vendor ID of operating manufacturer #1, and the manufacturer identifier can be used by the second node to determine whether it is the same manufacturer or a different manufacturer as the first node. In addition, the first node also requests the manufacturer identifier corresponding to the second node from the second node, and the pre-configuration information of the AI ​​model provided by the second node can be associated with the manufacturer identifier of the second node. Alternatively, it can be understood that the first node will determine that the received pre-configuration information of the AI ​​model is sent by the manufacturer corresponding to the second node.

[0150] In one possible implementation, the first request message also carries pre-configuration information of the AI ​​model that the first node can provide to the second node. The pre-configuration information of the AI ​​model is used by the second node to subsequently determine the AI ​​model to be requested, where the AI ​​model to be requested is one or more AI models that the first node can provide.

[0151] In one possible implementation, the pre-configuration information of the AI ​​model includes a third association relationship. The "third association relationship" can also be understood as an association relationship between at least the following two items: the model identifier corresponding to the AI ​​model, the model algorithm corresponding to the AI ​​model, the model algorithm identifier corresponding to the AI ​​model algorithm, the business corresponding to the AI ​​model, the business identifier corresponding to the AI ​​model business, the purpose corresponding to the AI ​​model, the purpose identifier corresponding to the purpose, the reasoning information corresponding to the AI ​​model, the model information corresponding to the AI ​​model, and the cell identifier (cell ID).

[0152] Exemplarily, the third association relationship includes one or more of the following association relationships: the association relationship between the model identifier, model algorithm, and model algorithm identifier corresponding to the AI ​​model; the association relationship between the model identifier, business, and business identifier corresponding to the AI ​​model; the association relationship between the model identifier, purpose, and purpose identifier corresponding to the AI ​​model; the association relationship between the model identifier, model information, and model reasoning information corresponding to the AI ​​model; the association relationship between the model identifier corresponding to the AI ​​model and the reasoning information corresponding to the AI ​​model, and the association relationship between the model identifier corresponding to the AI ​​model and the model information.

[0153] In this application, model information is used to characterize one or more of the following aspects of the AI ​​model: the input of the AI ​​model, the output of the AI ​​model, the number of layers of the AI ​​model, and the dimension of the AI ​​model.

[0154] The “AI model algorithms” mentioned in this application may include, for example: decision tree algorithm, random forest algorithm, logistic regression algorithm, naive Bayes algorithm, K-nearest neighbor algorithm, K-means algorithm, Adaboost algorithm, neural network algorithm, Markov algorithm, etc.

[0155] The "AI model services" mentioned in this application can be, for example, application scenarios designed in the 3rd Generation Partnership Project (3GPP), such as: channel state information reference signal (CSI-RS) feedback enhanced services, beam scanning enhanced services, positioning enhanced services, network energy saving services, load balancing services, mobility optimization services, and any other use cases that may be expanded in the future, etc.

[0156] The "purposes corresponding to the AI ​​model" mentioned in this application can also be understood as the "business corresponding to the AI ​​model." In other words, the "purposes," "businesses," and "scenarios" corresponding to the AI ​​model in this application can be used interchangeably.

[0157] The "inference information corresponding to the AI ​​model" mentioned in this application can be understood as the reasoning results of the model, which can be prediction information, prediction strategies, etc.

[0158] Illustratively, each of the above association relationships may be presented in the form of a table, as shown in Tables 1 to 5 below.

[0159] Table 1

[0160] Table 2

[0161] Table 3

[0162] Table 4

[0163] Form 5

[0164] Exemplarily, the pre-configuration information of the AI ​​model supported by the first node and provided to the second node may also include multiple association relationships, as shown in Table 6 and Table 7 below.

[0165] Table 6

[0166] Form 7

[0167] It can be seen that in this application, the first node sends the pre-configuration information of its own AI model to the second node, so that the second node can determine the AI ​​model that needs to be requested from the first node subsequently based on the association relationship in the pre-configuration information of the AI ​​model.

[0168] It should be noted that, in this application, the above-mentioned "association relationship" can also be replaced by "correspondence relationship", "mapping relationship", etc.

[0169] Optionally, the pre-configuration information of the AI ​​model provided by the first node to the second node may also include one or more of the following: information on the AI ​​model scrambling methods supported by the first node, information on the AI ​​model update methods supported by the first node, and information on the AI ​​model transmission methods supported by the first node. Information on the AI ​​model scrambling methods includes: an association between an AI model scrambling method identifier and a corresponding scrambling method and / or scrambling level; information on the AI ​​model update method includes: an association between an AI model update method identifier and a corresponding update method; and information on the AI ​​model transmission method includes: an association between an AI model transmission method identifier and a corresponding transmission method. Each of the above information is introduced below.

[0170] Taking the above-mentioned AI model scrambling method information as an example, for example, the scrambling method may include one or more of the following: differential privacy based on input perturbations / intermediate parameter perturbations, distributed multi-party secure computation, homomorphic encryption, and noise power. For example, taking differential privacy as an example, it may be the standard deviation σ of the Gaussian distribution when adding Gaussian noise. For another example, the scrambling level may include level one, level two, or level three. For example, assume that the scrambling method associated with scrambling method identifier #1 of AI model #1 is a homomorphic encryption method with a scrambling level of level one; for example, the scrambling method associated with scrambling method identifier #2 of AI model #1 is a differential privacy method based on input perturbations with a scrambling level of level two; for another example, the scrambling method associated with scrambling method identifier #3 of AI model is a noise power-based method. For another example, the scrambling level associated with scrambling method identifier #4 of AI model is level three. In this application, the specific indication method for the scrambling method and / or scrambling level associated with the scrambling method identifier is not limited, and those skilled in the art can flexibly design the indication method.

[0171] In the present application, "AI model update mode" is used to indicate the update mode of the received AI model that the first node can support. For example, the model update mode can be the "model retransmission" mode or the "weight update" mode. Among them, "model retransmission" means that the requested node re-sends the updated AI model, and "weight update" means that the requested node only needs to transmit the weight parameters that need to be updated in the model. Exemplarily, it is assumed that the AI ​​model update mode corresponding to the AI ​​model update mode identifier #1 is the model retransmission mode, and it is assumed that the AI ​​model update mode corresponding to the AI ​​model update mode identifier #2 is the weight update mode. Specifically, the specific implementation method of the "AI model update mode" can be understood with reference to the above-mentioned "AI model scrambling mode information", which will not be repeated here.

[0172] In this application, "AI model transmission mode" is used to indicate the AI ​​model transmission mode that the first node can support. Depending on the difference between the requesting node and the requested node entity, possible transmission modes are: UP-based transmission, CP-based transmission, Internet protocol channel (IP tunnel)-based transmission, forwarding by 5GC, etc. Optionally, the decision conditions for AI model transmission can also be carried. For example, when the AI ​​model is small, you can choose to transmit the AI ​​model based on CP, when the AI ​​model is large, you can choose to transmit the AI ​​model based on UP, when the AI ​​model is non-air interface transmission, the AI ​​model is transmitted based on IP tunnel, and when the AI ​​model is large and 5GC has the latest AI model, the AI ​​model is forwarded based on 5GC. Specifically, the specific implementation method of the "AI model transmission mode" can be understood with reference to the above-mentioned "AI model scrambling method information", which will not be repeated here.

[0173] 720. The second node sends a first response message to the first node. The first response message carries pre-configuration information of the AI ​​model that the second node can provide and the manufacturer identifier corresponding to the second node.

[0174] Correspondingly, the first node receives the first response message.

[0175] The pre-configuration information of the AI ​​model is used by the first node to determine the first AI model to be requested, where the first AI model is one or more AI models that the second node can provide.

[0176] In one possible implementation, the pre-configuration information of the AI ​​model that the second node can provide includes a first association relationship, which is an association relationship between at least two of the following: a model identifier corresponding to the AI ​​model, a model algorithm corresponding to the AI ​​model, a model algorithm identifier corresponding to the AI ​​model algorithm, a business corresponding to the AI ​​model, a business identifier corresponding to the AI ​​model business, a purpose corresponding to the AI ​​model, a purpose identifier corresponding to the purpose, reasoning information corresponding to the AI ​​model, and model information corresponding to the AI ​​model.

[0177] Exemplarily, the first association relationship includes one or more of the following association relationships: the association relationship between the model identifier, model algorithm, and model algorithm identifier corresponding to the AI ​​model; the association relationship between the model identifier, business, and business identifier corresponding to the AI ​​model; the association relationship between the model identifier, purpose, and purpose identifier corresponding to the AI ​​model; the association relationship between the model identifier, reasoning information, and model information corresponding to the AI ​​model; the association relationship between the model identifier corresponding to the AI ​​model and the reasoning information corresponding to the AI ​​model; and the association relationship between the model identifier corresponding to the AI ​​model and the model information.

[0178] Among them, the model information can be used to characterize the characteristics of the AI ​​model, for example, it can be one or more of the following: the input of the AI ​​model, the output of the AI ​​model, the number of layers of the AI ​​model, and the dimension of the AI ​​model.

[0179] For example, the above-mentioned various association relationships can be understood by referring to Tables 1 to 7 above, and no further examples are given one by one.

[0180] Optionally, the pre-configuration information of the AI ​​model that the second node can provide also includes one or more of the following: information on the AI ​​model scrambling method supported by the second node, information on the AI ​​model update method supported by the second node, and information on the AI ​​model transmission method supported by the second node.

[0181] Assume that the second node is a node of vendor #2, and the vendor identifier corresponding to the second node is vendor ID #2.

[0182] In this application, if the second node cannot transmit AI pre-configuration information to the first node, the second node can provide feedback to the first node indicating the reason. For example, the second node does not agree to transmit the AI ​​model, or the second node does not accept the scrambling method of the AI ​​model, or the second node does not support the model transmission method, etc.

[0183] In this application, the specific signaling of step 710 and step 720 can be a new process or a reused legacy process. In this application, the name and implementation method of the specific signaling of step 710 and step 720 are not limited. For example, the specific signaling of step 710 can be "XN SETUP" signaling, and the specific signaling of step 720 can be "NG-RAN NODE CONFIGURATION" signaling.

[0184] Based on the above steps 710 to 720, in this application, the first node can request the pre-configuration information of the AI ​​model of the second node through a request-response mechanism, so that the first node can determine the AI ​​model that needs to be requested from the second node in the future based on the pre-configuration information of the AI ​​model that the second node can provide. In addition, in this application, the first node carries its own manufacturer identifier in the request message, so that the second node can determine whether it belongs to the same manufacturer as the first node through the manufacturer identifier of the first node, and thus determine how to send the AI ​​model when sending the AI ​​model to the first node in the future, thereby ensuring that there will be no privacy issues with the AI ​​model.

[0185] 730. The first node sends a second request message to the second node. The second request message is used to request the first AI model. The second request message carries first indication information. The first indication information is used to indicate pre-configuration information of the first AI model.

[0186] The corresponding second node receives the second request message from the first node.

[0187] In the present application, the pre-configuration information of the first AI model may indicate the requested AI model to the second node.

[0188] Exemplarily, the second request message may be an AI model request message.

[0189] In one possible implementation, the pre-configuration information of the first AI model includes one or more of the following: a model identifier corresponding to the first AI model, model information corresponding to the first AI model, a model algorithm identifier corresponding to the first AI model, a service identifier corresponding to the first AI model, a usage identifier corresponding to the first AI model, an AI model scrambling method identifier supported by the first node, an AI model update method identifier supported by the first node, an AI model transmission method identifier supported by the first node, a cell identifier, a reason for requesting the first AI model, and a model accuracy corresponding to the first AI model.

[0190] The "reason for requesting the first AI model" can be, for example, that the first node requires a high-precision AI model, or that certain AI models cannot be trained on the first node, etc. The "corresponding model accuracy of the first AI model" indicates the first node's accuracy requirement for the requested model, such as a model confidence greater than 0.9, a training step count greater than 1000, or an AI model accuracy greater than 0.95.

[0191] Exemplarily, the second request message may carry AI model identifier #1 and AI model identifier #2. Exemplarily, the second request message may carry model algorithm identifier #m1, model algorithm identifier #m2, service #S1, service #S2, and AI model identifier #1, AI model identifier #2.

[0192] Optionally, the second request message also carries one or more of the following: the period for the second node to report the first AI model, the message identifier corresponding to the second request message, and the message type corresponding to the second request message.

[0193] Among them, the "reporting period of the second response message" means that the first node instructs the second node to regularly report the updated AI model to the first node. The "message identifier corresponding to the second request message" can be used to associate subsequent steps. For example, after the first node subsequently receives a response message from the second node, if the response message also carries the message identifier, the first node can determine that the response message is a reply message to the second request message. For example, the message identifier can be Measurement ID#1. In this application, the "message identifier corresponding to the second request message" can also be replaced with the identifier corresponding to the process of steps 730 to 770.

[0194] "The message type corresponding to the second request message" may, for example, include the request message type for starting to send the requested AI model (start type), the request message type for stopping sending the requested AI model (stop type), the request message type for adding the requested AI model (add type), the request message type for deleting the requested AI model (del type), and the request message type for updating the requested AI model (revise type).

[0195] 740. The second node sends a second response message to the first node. The second response message carries second indication information, and the second indication information is used to indicate pre-configuration information of the second AI model.

[0196] The pre-configuration information of the second AI model is information that the second node supports sending to the first node in the pre-configuration information of the first AI model, and the second AI model is one or more AI models in the first AI model.

[0197] Correspondingly, the first node receives a second response message from the second node.

[0198] Exemplarily, the second response message may be an AI model response message (AI model request response).

[0199] In this application, the pre-configured information of the second AI model includes one or more of the following: a model identifier corresponding to the second AI model, model information corresponding to the second AI model, a model algorithm identifier corresponding to the second AI model, a service identifier corresponding to the second AI model, and model accuracy feedback information corresponding to the second AI model. The "model accuracy feedback information" indicates that the second node supports the model accuracy of the second AI model sent to the first node.

[0200] Optionally, the second response message also carries one or more of the following: reporting cycle feedback information, a message identifier corresponding to the second request message (for example, Measurement ID#1), and failure cause indication information. The "reporting cycle feedback information" is used to indicate the AI ​​model reporting cycle supported by the second node. For example, the second node may indicate a new reporting cycle for the first node to select. The specific implementation form may be a time list, a time range, a time identifier, etc., without limitation. The "failure cause indication information" is used to indicate the identifier and reason why the second node does not support the first AI model sent to the first node. For example, it may be because the model accuracy of the AI ​​model that the second node can provide to the first node does not meet the requirements of the first node, or because privacy is not allowed.

[0201] In one possible implementation, if the second node cannot send any AI model requested by the first node to the first node, the second node may send an AI model failure message to the first node in response to the second request message sent by the first node. For example, the AI ​​model failure message may include a message identifier and a failure cause. The AI ​​model failure message may be used to indicate the identifier and cause of the AI ​​model that cannot be provided, for example, because the accuracy does not meet the requirements or because privacy is not allowed.

[0202] Optionally, the method further includes step 750, where the first node determines fourth indication information based on the second indication information.

[0203] The fourth indication information is used to indicate pre-configuration information of the third AI model, wherein the pre-configuration information of the third AI model is information determined by the first node to request from the second node in the pre-configuration information of the second AI model, and the third AI model is one or more AI models in the second AI model.

[0204] Exemplarily, the first node may confirm the pre-configuration information of the AI ​​model that ultimately needs to be requested from the second node based on the second indication information sent by the second node.

[0205] Exemplarily, the first node may determine a new reporting period based on the second indication information of the second node. If the second node does not modify the reporting period, the first node may not report, or use 1 bit to indicate that the reporting period has not been modified.

[0206] Optionally, the method further includes step 760, where the first node sends an AI model confirmation message to the second node, and the AI ​​model confirmation message carries fourth indication information.

[0207] Exemplarily, the AI ​​model confirmation message may be an AI model request acknowledge.

[0208] Correspondingly, the second node receives the AI ​​model confirmation message.

[0209] Optionally, the AI ​​model confirmation message also includes a message identifier (eg, Measurement ID#1).

[0210] 770. The first node receives an AI model update message from the second node.

[0211] Exemplarily, the AI ​​model update message may be AI model update.

[0212] In one possible implementation, if steps 750 and 760 are not performed, the AI ​​model update message carries the scrambled second AI model, or the AI ​​model update message carries the address corresponding to the scrambled second AI model, or the AI ​​model update message carries the second AI model, or the AI ​​model update message carries the address corresponding to the second AI model.

[0213] In another possible implementation, if steps 750 and 760 are performed, the AI ​​model update message carries the scrambled third AI model, or the AI ​​model update message carries the address corresponding to the scrambled third AI model, or the AI ​​model update message carries the third AI model, or the AI ​​model update message carries the address corresponding to the third AI model.

[0214] Exemplarily, if the transmission mode of the AI ​​model is IP tunnel, the second node may only transmit the address of the AI ​​model.

[0215] Optionally, the AI ​​model update message also carries a model scrambling method identifier and a message identifier (for example, Measurement ID#1).

[0216] It should be noted that the above steps 730 to 770 are mainly for the first node to request the second node for the AI ​​model required by the first node itself. Optionally, since the first node can also provide the pre-configuration information of its AI model to the second node in step 710, in subsequent steps, the second node can also request the AI ​​model required by itself from the first node. The specific implementation method can be understood with reference to steps 730 to 770. It is only necessary to regard the first node in steps 730 to 770 as the "second node" and the second node as the "first node" for understanding.

[0217] Based on the above steps 730 to 770, in the present application, the first node and the second node can first negotiate the pre-configuration information of the AI ​​model to be requested based on the pre-configuration information of the AI ​​model previously interacted, so that the second node can subsequently transmit the AI ​​model that meets the first node's own needs to the first node in an appropriate manner, and can avoid the privacy issues of the AI ​​model.

[0218] In addition, the present application also provides a method 800 for transmitting training data. The description of each information in the steps of method 800 in the present application can be understood with reference to the corresponding steps of method 700 in FIG7 . The following mainly describes the differences between method 800 and method 700. As shown in FIG8 , method 800 includes:

[0219] 810. The first node sends a first request message to the second node. The first request message is used to request pre-configuration information of training data that the second node can provide and the manufacturer identifier corresponding to the second node. The first request message carries the manufacturer identifier corresponding to the first node.

[0220] Correspondingly, the second node receives the first request message from the first node.

[0221] In one possible implementation, the first request message also carries pre-configuration information for providing training data by the first node, wherein the pre-configuration information for the training data includes a fourth association relationship, which is an association relationship between at least two of the following: a data identifier corresponding to the training data, a model algorithm corresponding to the training data, a model algorithm identifier corresponding to the model algorithm, a service corresponding to the training data, a service identifier corresponding to the training data service, a purpose corresponding to the training data, and a purpose identifier corresponding to the purpose. Exemplarily, the fourth association relationship includes at least one of the following association relationships: an association relationship between the data identifier corresponding to the training data, the model algorithm, and the algorithm identifier; an association relationship between the data identifier corresponding to the training data, the service, and the service identifier; and an association relationship between the data identifier corresponding to the training data, the purpose, and the purpose identifier.

[0222] Optionally, the first request message further carries one or more of the following: information about training data scrambling methods supported by the first node, information about training data update methods supported by the first node, and information about training data transmission methods supported by the first node. The information about the training data scrambling method includes: an association between a training data scrambling method identifier and a corresponding scrambling method and / or scrambling level; the information about the training data update method includes: an association between a training data update method identifier and a corresponding update method; and the information about the training data transmission method includes: an association between a training data transmission method identifier and a corresponding transmission method.

[0223] Among them, the "scrambling method of training data" can be understood with reference to the "scrambling method of AI model" in step 710. The "updating method of training data" may include, for example, resending the updated training data, or only sending the updated training data which is different from the original training data. The "transmission method of training data" may include, for example, UP-based transmission, CP-based transmission, IP tunnel-based transmission, forwarding by 5GC, etc. Optionally, the judgment conditions for the transmission of training data may also be carried, such as transmitting the training data based on CP when the training data is small, transmitting the training data based on UP when the training data is large, transmitting the training data based on IP tunnel when the training data is not air interfaced, and obtaining the training data based on 5GC forwarding when the training data is large and 5GC has the latest training data.

[0224] Specifically, the meaning of each information in 810 and the implementation method of the associated information can be understood by referring to step 710 in the above method 700, and will not be described in detail.

[0225] 820. The second node sends a first response message to the first node. The first response message carries pre-configuration information of training data that the second node can provide and a manufacturer identifier corresponding to the second node.

[0226] The pre-configured information of the training data is used by the first node to determine the first training data to be requested, and the first training data is part or all of the training data that can be provided by the second node.

[0227] Correspondingly, the first node receives the first response message from the second node.

[0228] In the present application, the pre-configuration information of the training data that the second node can provide may include a second association relationship. The second association relationship is an association relationship between at least two of the following: a data identifier corresponding to the training data, a model algorithm corresponding to the training data, a model algorithm identifier corresponding to the model algorithm, a service corresponding to the training data, a service identifier corresponding to the training data service, a purpose corresponding to the training data, and a purpose identifier corresponding to the purpose. Exemplarily, the second association relationship includes at least one of the following association relationships: an association relationship between the data identifier, the model algorithm, and the algorithm identifier corresponding to the training data; an association relationship between the data identifier, the service, and the service identifier corresponding to the training data; and an association relationship between the data identifier, the purpose, and the purpose identifier corresponding to the training data.

[0229] Optionally, the first response message further carries one or more of the following: information about training data scrambling methods supported by the second node, information about training data update methods supported by the second node, and information about training data transmission methods supported by the second node. The information about the training data scrambling method includes: an association between a training data scrambling method identifier and a corresponding scrambling method and / or scrambling level; the information about the training data update method includes: an association between a training data update method identifier and a corresponding update method; and the information about the training data transmission method includes: an association between a training data transmission method identifier and a corresponding transmission method.

[0230] Specifically, the meaning of each information in 820 and the implementation method of the associated information can be understood by referring to step 720 in the above method 700, and will not be described in detail.

[0231] 830. The first node sends a second request message to the second node. The second request message is used to request the first training data. The second request message carries first indication information. The first indication information is used to indicate pre-configuration information of the first training data.

[0232] Correspondingly, the second node receives the second request message from the first node.

[0233] Exemplarily, the second request message may be an AI data request (AI data request) message.

[0234] In one possible implementation, the pre-configuration information of the first training data includes one or more of the following: a model identifier corresponding to the first training data, a model algorithm identifier corresponding to the first training data, a service identifier corresponding to the first training data, a usage identifier corresponding to the first training data, a reason for requesting the first training data, a model accuracy corresponding to the first training data, a training data scrambling method identifier supported by the first node, a training data update method identifier supported by the first node, and a training data transmission method identifier supported by the first node.

[0235] The "reason for requesting the first AI training data" can be, for example, that the first node requires high-precision AI training data, or that the first node cannot generate the AI ​​training data. The "model accuracy corresponding to the first training data" indicates the first node's accuracy requirement for the requested training data, such as training data with a model confidence greater than 0.9, training data with a number of training steps greater than 1000, or training data with an AI use case accuracy greater than 0.95.

[0236] Optionally, the second request message further carries one or more of the following: a reporting period of training data, a message identifier corresponding to the second request message, and a message type corresponding to the second request message.

[0237] Among them, the "reporting period of the second response message" means that the first node instructs the second node to regularly report the updated training data to the first node. The "message identifier corresponding to the second request message" can be used to associate subsequent steps. For example, after the first node subsequently receives a response message from the second node, if the response message also carries the message identifier, the first node can determine that the response message is a reply message to the second request message. For example, the message identifier can be Measurement ID#2. In this application, the "message identifier corresponding to the second request message" can also be replaced with the identifier corresponding to the process of steps 830 to 870.

[0238] The "message type corresponding to the second request message" may include, for example, a request message type for starting to send training data (start type), a request message type for stopping sending training data (stop type), a request message type for adding requested training data (add type), a request message type for deleting requested training data (del type), and a request message type for updating requested training data (revise type). Specifically, the meaning and implementation of each information in step 830 can be understood with reference to step 730 in method 700 and will not be described in detail here.

[0239] 840. The first node receives a second response message from the second node. The second response message carries second indication information, and the second indication information is used to indicate pre-configuration information of second training data.

[0240] The pre-configuration information of the second training data is information in the pre-configuration information of the first training data that the second node supports sending to the first node, and the second training data is part or all of the first training data.

[0241] Exemplarily, the second response message may be an AI data response message (AI data request response).

[0242] In one possible implementation, the pre-configuration information of the second training data includes one or more of the following: a model identifier corresponding to the second training data, a model algorithm identifier corresponding to the second training data, a service identifier corresponding to the second training data, and model accuracy feedback information corresponding to the second training data, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second training data sent to the first node.

[0243] Optionally, the second response message further carries one or more of the following: reporting period feedback information, a message identifier corresponding to the second response message, and failure cause indication information. The reporting period feedback information is used to indicate the reporting period of the second response message supported by the second node; the message identifier is used to associate with the first request message; and the failure cause indication information is used to indicate the identifier and reason why the second node does not support the first training data sent to the first node, for example, because the accuracy of the training data does not meet the requirements, or because privacy is not allowed.

[0244] Specifically, the meaning and implementation of each information in step 840 can be understood by referring to step 740 in method 700, and will not be described in detail.

[0245] Optionally, the method further includes step 850, where the first node determines fourth indication information according to the second indication information, where the fourth indication information is used to indicate pre-configuration information of the third training data.

[0246] The pre-configuration information of the third training data is information determined by the first node to request from the second node in the pre-configuration information of the second training data, and the third training data is part or all of the second training data.

[0247] Optionally, the method further includes step 860, where the first node sends a training data confirmation message to the second node, where the training data confirmation message carries fourth indication information.

[0248] Exemplarily, the “training data confirmation message” may be an AI data request acknowledgement message.

[0249] Step 870: The first node receives a training data update message from the second node.

[0250] Exemplarily, the “training data update message” may be an AI data update message.

[0251] In a possible implementation, if steps 850 and 860 are not performed, the training data update message carries the scrambled second training data, or the training data update message carries the address corresponding to the scrambled second training data.

[0252] In one possible implementation, if steps 850 and 860 are performed, the training data update message carries the scrambled third training data AI model, or the training data update message carries the address corresponding to the scrambled third training data.

[0253] It should be noted that the above steps 830 to 870 are mainly for the first node to request the second node for the training data required by the first node itself. Optionally, since the first node can also provide the pre-configuration information of its training data to the second node in step 810, in subsequent steps, the second node can also request the first node for the training data required by itself. The specific implementation method can be understood with reference to steps 830 to 870. It is only necessary to regard the first node in steps 830 to 870 as the "second node" and the second node as the "first node" for understanding.

[0254] Based on the above steps 830 to 870, in the present application, the first node and the second node can first negotiate the pre-configuration information of the training data to be requested based on the pre-configuration information of the training data previously interacted, so that the second node can subsequently transmit the training data that meets the first node's own needs to the first node in an appropriate manner, and can avoid privacy issues of the training data.

[0255] It should be understood that the examples in methods 700 and 800 in the embodiments of the present application are merely intended to facilitate understanding of the embodiments of the present application by those skilled in the art, and are not intended to limit the embodiments of the present application to the specific scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or variations based on the examples in methods 700 and 800, and such modifications or variations also fall within the scope of the embodiments of the present application.

[0256] It can also be understood that some optional features in the various embodiments of the present application may not depend on other features in certain scenarios, and may also be combined with other features in certain scenarios, without limitation.

[0257] It is also understood that the various embodiments described in this application may be independent solutions or combined according to internal logic, and all of these solutions fall within the scope of protection of this application. In addition, the explanations or descriptions of various terms appearing in the embodiments may refer to or explain each other in the various embodiments, without limitation.

[0258] It should be understood that the term "and / or" in this document simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0259] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of interaction between each node. It is understandable that each node, such as a terminal device, a network device, includes a hardware structure and / or software module corresponding to the execution of each function in order to implement the above functions. Those skilled in the art should be aware that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner 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 to be beyond the scope of this application.

[0260] In the embodiment of the present application, the terminal device and the network device can be divided into functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation. The following is an example of dividing each functional module according to each function.

[0261] FIG9 is a schematic block diagram of a communication device 900 according to an embodiment of the present application. As shown in the figure, the device 900 may include a transceiver unit 910 and a processing unit 920 .

[0262] In one possible design, the device 900 may be the first node in the above method embodiment, or may be a chip for implementing the functions of the first node in the above method embodiment. It should be understood that the device 900 may correspond to the first node in the method 700 or the method 800 according to the embodiment of the present application, and the device 900 may perform the steps corresponding to the first node in the method 700 or the method 800 according to the embodiment of the present application.

[0263] In a possible implementation manner, the transceiver unit is configured to send a first request message to the second node, and the transceiver unit is configured to receive a first response message from the second node.

[0264] In a possible implementation manner, the transceiver unit is configured to send a second request message to the second node, and the transceiver unit is configured to receive a second response message from the second node.

[0265] In one possible implementation, the processing unit is used to determine fourth indication information based on the second indication information; the transceiver unit is used to send an AI model confirmation message to the second node, and the AI ​​model confirmation message carries the fourth indication information, or the transceiver unit is used to send an AI training data confirmation message to the second node, and the training data confirmation message carries the fourth indication information.

[0266] In one possible implementation, the transceiver unit is used to receive an AI model update message or a training data update message from the second node.

[0267] In one possible design, the device 900 may be the second node in the above method embodiment, or may be a chip for implementing the functions of the second node in the above method embodiment. It should be understood that the device 900 may correspond to the second node in methods 700 and 800 according to the embodiments of the present application, and the device 900 may perform the steps corresponding to the second node in methods 700 and 800 according to the embodiments of the present application.

[0268] In a possible implementation, the transceiver unit is configured to receive a first request message from the first node; and the transceiver unit is further configured to send a first response message to the first node.

[0269] In a possible implementation, the transceiver unit is configured to receive a second request message from the first node, and the transceiver unit is further configured to send a second response message to the first node.

[0270] In one possible implementation, the transceiver unit is used to receive an AI model confirmation message from the first node, or a training data confirmation message.

[0271] In one possible implementation, the transceiver unit is used to send an AI model update request message, or a training data update request message, to the first node.

[0272] It should also be understood that the device 900 here is embodied in the form of a functional unit. The term "unit" here can refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a merging logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art will understand that the device 900 can be specifically the first node or the second node in the above-mentioned embodiment, and can be used to execute the various processes and / or steps corresponding to the first node or the second node in the above-mentioned method embodiments. To avoid repetition, they will not be described here.

[0273] The apparatus 900 of each of the above-mentioned solutions has the function of implementing the corresponding steps performed by the first node or the second node in the above-mentioned method. The functions can be implemented by hardware, or by hardware executing corresponding software implementations. The hardware or software includes one or more modules corresponding to the above-mentioned functions; for example, the transceiver unit can be replaced by a transceiver (for example, the sending unit in the transceiver unit can be replaced by a transmitter, and the receiving unit in the transceiver unit can be replaced by a receiver), and other units, such as the processing unit, can be replaced by a processor to respectively perform the sending and receiving operations and related processing operations in each method embodiment.

[0274] In addition, the transceiver unit 910 may also be a transceiver circuit (for example, may include a receiving circuit and a sending circuit), and the processing unit may be a processing circuit.

[0275] It should be noted that the device in Figure 9 can be the first node or second receiving order in the aforementioned embodiments, or it can be a chip or chip system, such as a system on chip (SoC). The transceiver unit can be an input / output circuit or a communication interface; the processing unit can be a processor, microprocessor, or integrated circuit integrated on the chip. This is not limited here.

[0276] Figure 10 is a schematic block diagram of a communication device 1000 provided in an embodiment of the present application. As shown in the figure, the device 1000 includes: at least one processor 1020. The processor 1020 is coupled to a memory and is configured to execute instructions stored in the memory to transmit and / or receive signals. Optionally, the device 1000 also includes a memory 1030 for storing instructions. Optionally, the device 1000 also includes a transceiver 1010, and the processor 1020 controls the transceiver 1010 to transmit and / or receive signals.

[0277] It should be understood that the processor 1020 and memory 1030 may be combined into one processing device, and the processor 1020 is configured to execute program codes stored in the memory 1030 to implement the above functions. In specific implementations, the memory 1030 may also be integrated into the processor 1020 or independent of the processor 1020.

[0278] It should also be understood that the transceiver 1010 may include a transceiver (or receiver) and a transmitter (or transmitter). The transceiver may further include an antenna, and the number of antennas may be one or more. The transceiver 1010 may also be a communication interface or interface circuit.

[0279] Specifically, the transceiver 1010 in the device 1000 may correspond to the transceiver unit 110 in the device 1000 , and the processor 220 in the device 1000 may correspond to the processing unit 120 in the device 1000 .

[0280] As a solution, the device 1000 is used to implement the operations performed by the first node in each of the above method embodiments.

[0281] For example, the processor 1020 is configured to execute the computer program or instructions stored in the memory 1030 to implement the operations related to the first node in each of the above method embodiments, such as the method performed by the first node in any of the embodiments shown in method 700 and method 800.

[0282] As another solution, the apparatus 1000 is configured to implement the operations performed by the second node in each of the above method embodiments. For example, the processor 1020 is configured to execute a computer program or instruction stored in the memory 1030 to implement the relevant operations of the second node in each of the above method embodiments. For example, the method performed by the second node in any of the embodiments shown in methods 700 and 800.

[0283] It should be understood that the specific process of each transceiver and processor executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0284] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0285] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0286] It is 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 RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous-link DRAM (SLDRAM), and direct RAM-bus 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.

[0287] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which stores computer program code. When the computer program code runs on a computer, the computer executes the method executed by a terminal device or a network device in any one of the embodiments of method 700 and method 800.

[0288] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable medium, which stores program code. When the program code runs on a computer, the computer executes the method performed by the first node or the second node in the above embodiment.

[0289] According to the method provided in the embodiment of the present application, the present application also provides a communication system, which includes a first node and a second node. The terminal device is configured to execute the steps corresponding to the first node in the above methods 700 and 800, and the second node is configured to execute the steps corresponding to the second node in the above methods 700 and 800.

[0290] The explanation of the relevant contents and beneficial effects of any of the above-mentioned devices can be referred to the corresponding method embodiments provided above, which will not be repeated here.

[0291] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it 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. When the computer instructions are loaded and 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 via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. 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 includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0292] In each of the above-mentioned device embodiments, the corresponding modules or units perform the corresponding steps. For example, the transceiver unit (transceiver) performs the receiving or sending steps in the method embodiments, and other steps except sending and receiving can be performed by the processing unit (processor). The functions of the specific units can be referred to in the corresponding method embodiments. There can be one or more processors.

[0293] As used in this specification, the terms "component," "module," "system," and the like are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. By way of illustration, both an application running on a computing device and a computing device can be a component. One or more components can reside in a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures stored thereon. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component on a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0294] 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.

[0295] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0296] 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0297] The units described as separate components may or may not be physically separate, and the components shown 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0298] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0299] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0300] It should be understood that references to "embodiments" throughout this specification mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, various embodiments throughout this specification do not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0301] It should also be understood that the ordinal numbers "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the size, content, order, timing, priority, or importance of the multiple objects. For example, the first PDSCH and the second PDSCH can be the same physical channel or different physical channels, and such names do not indicate a difference in the amount of information, content, priority, or importance of the two physical channels.

[0302] It should also be understood that, in this application, "at least one" means one or more, and "plurality" means two or more. "At least one item" or similar expressions refers to one or more items, that is, any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c means: a, b, c, a and b, a and c, b and c, or a, b, and c.

[0303] It should also be understood that in each embodiment of the present application, "A corresponds to B" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B based solely on A, and B can also be determined based on A and / or other information.

[0304] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for transmitting an artificial intelligence model, characterized in that: include: The first node sends a first request message to the second node, where the first request message is used to request pre-configuration information of an artificial intelligence AI model that can be provided by the second node and a manufacturer identifier corresponding to the second node, and the first request message carries the manufacturer identifier corresponding to the first node; The first node receives a first response message from the second node, the first response message carrying pre-configuration information of the AI ​​model that can be provided by the second node and the manufacturer identifier corresponding to the second node, the pre-configuration information of the AI ​​model includes a first association relationship, the first association relationship is an association relationship between at least two of the following: a model identifier corresponding to the AI ​​model, a model algorithm corresponding to the AI ​​model, a model algorithm identifier corresponding to the AI ​​model algorithm, a business corresponding to the AI ​​model, a business identifier corresponding to the AI ​​model business, a purpose corresponding to the AI ​​model, a purpose identifier corresponding to the purpose, reasoning information corresponding to the AI ​​model, and model information corresponding to the AI ​​model, the first association relationship is used by the first node to determine a first AI model to be requested, wherein the first AI model is one or more of the AI ​​models that can be provided by the second node.

2. The method according to claim 1, characterized in that The pre-configuration information of the AI ​​model includes one or more of the following: Information about the AI ​​model scrambling method supported by the second node, wherein the information about the AI ​​model scrambling method includes: an association relationship between the AI ​​model scrambling method identifier and a corresponding scrambling method and / or scrambling level; Information about the AI ​​model update method supported by the second node, wherein the information about the model update method includes: an association relationship between an update method identifier of the AI ​​model and a corresponding update method; Information on the AI ​​model transmission mode supported by the second node, wherein the information on the AI ​​model transmission mode includes: an association relationship between the transmission mode identifier of the AI ​​model and the corresponding transmission mode.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: The first node sends a second request message to the second node, where the second request message is used to request the first AI model, and the second request message carries first indication information, where the first indication information is used to indicate pre-configuration information of the first AI model; The first node receives a second response message from the second node, where the second response message carries second indication information, where the second indication information is used to indicate pre-configuration information of a second AI model, wherein the pre-configuration information of the second AI model is information in the pre-configuration information of the first AI model that the second node supports sending to the first node, and the second AI model is one or more AI models in the first AI model.

4. The method according to claim 3, characterized in that The first request message also carries pre-configuration information of the AI ​​model provided by the first node to the second node, and the pre-configuration information of the AI ​​model provided by the first node to the second node includes one or more of the following: Information about the AI ​​model scrambling method supported by the first node, wherein the information about the AI ​​model scrambling method includes: an association relationship between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; Information about the AI ​​model update method supported by the first node, wherein the information about the AI ​​model update method includes: an association relationship between an update method identifier of the AI ​​model and a corresponding update method; Information about the AI ​​model transmission mode supported by the first node, wherein the information about the AI ​​model transmission mode includes: an association relationship between the transmission mode identifier of the AI ​​model and the corresponding transmission mode.

5. The method according to claim 4, characterized in that The pre-configuration information of the first AI model includes one or more of the following: a model identifier corresponding to the first AI model, model information corresponding to the first AI model, a model algorithm identifier corresponding to the first AI model, a service identifier corresponding to the first AI model, a usage identifier corresponding to the first AI model, an AI model scrambling method identifier supported by the first node, an AI model update method identifier supported by the first node, an AI model transmission method identifier supported by the first node, a reason for requesting the first AI model, and a model accuracy corresponding to the first AI model.

6. The method according to any one of claims 3 to 5, characterized in that The second request message further carries one or more of the following: The period for the second node to report the AI ​​model; a message identifier corresponding to the second request message, wherein the message identifier is used to associate with the second response message; The message type corresponding to the second request message.

7. The method according to any one of claims 3 to 6, characterized in that The pre-configuration information of the second AI model includes one or more of the following: the model identifier corresponding to the second AI model, the model information corresponding to the second AI model, the model algorithm identifier corresponding to the second AI model, the service identifier corresponding to the second AI model, and the model accuracy feedback information corresponding to the second AI model, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second AI model sent to the first node.

8. The method according to any one of claims 3 to 7, characterized in that The second response message further carries one or more of the following: Reporting cycle feedback information, where the reporting cycle feedback information is used to indicate an AI model reporting cycle supported by the second node; the message identifier; Failure reason indication information, where the failure reason indication information is used to indicate an identifier and a reason why the second node does not support the first AI model sent to the first node.

9. The method according to any one of claims 3 to 8, characterized in that The method further comprises: The first node receives an AI model update message from the second node, where the AI ​​model update message carries the encrypted second AI model, or the AI ​​model update message carries an address corresponding to the encrypted second AI model, or the AI ​​model update message carries the second AI model, or the AI ​​model update message carries the address corresponding to the second AI model.

10. The method according to any one of claims 3 to 8, characterized in that The method further comprises: The first node determines fourth indication information according to the second indication information, where the fourth indication information is used to indicate pre-configuration information of a third AI model, wherein the pre-configuration information of the third AI model is information determined by the first node to request from the second node in the pre-configuration information of the second AI model, and the third AI model is one or more AI models in the second AI model; The first node sends an AI model confirmation message to the second node, where the AI ​​model confirmation message carries the fourth indication information.

11. The method according to claim 10, characterized in that The method further comprises: The first node receives an AI model update message from the second node, where the AI ​​model update message carries the encrypted third AI model, or the AI ​​model update message carries an address corresponding to the encrypted third AI model, or the AI ​​model update message carries the third AI model, or the AI ​​model update message carries the address corresponding to the third AI model.

12. A method for transmitting an artificial intelligence model, characterized in that: include: The second node receives a first request message from the first node, where the first request message is used to request pre-configuration information of an artificial intelligence AI model that can be provided by the second node and a manufacturer identifier corresponding to the second node, and the first request message carries the manufacturer identifier corresponding to the first node; The second node sends a first response message to the first node, the first response message carrying pre-configuration information of the AI ​​model that can be provided by the second node and the manufacturer identifier corresponding to the second node, the pre-configuration information of the AI ​​model includes a first association relationship, the first association relationship is an association relationship between at least two of the following: a model identifier corresponding to the AI ​​model, a model algorithm corresponding to the AI ​​model, a model algorithm identifier corresponding to the AI ​​model algorithm, a business corresponding to the AI ​​model, a business identifier corresponding to the AI ​​model business, a purpose corresponding to the AI ​​model, a purpose identifier corresponding to the purpose, reasoning information corresponding to the AI ​​model, and model information corresponding to the AI ​​model, the first association relationship is used by the first node to determine the first AI model to be requested, wherein the first AI model is one or more of the AI ​​models that can be provided by the second node.

13. The method according to claim 12, characterized in that The pre-configuration information of the AI ​​model includes one or more of the following: Information about the AI ​​model scrambling method supported by the second node, wherein the information about the AI ​​model scrambling method includes: an association relationship between the AI ​​model scrambling method identifier and a corresponding scrambling method and / or scrambling level; Information about the AI ​​model update method supported by the second node, wherein the information about the model update method includes: an association relationship between an update method identifier of the AI ​​model and a corresponding update method; Information on the AI ​​model transmission mode supported by the second node, wherein the information on the AI ​​model transmission mode includes: an association relationship between the transmission mode identifier of the AI ​​model and the corresponding transmission mode.

14. The method according to claim 12 or 13, characterized in that The method further comprises: The second node receives a second request message from the first node, where the second request message is used to request the first AI model, and the second request message carries first indication information, where the first indication information is used to indicate pre-configuration information of the first AI model; The second node sends a second response message to the first node, the second response message carrying second indication information, the second indication information being used to indicate pre-configuration information of a second AI model, wherein the pre-configuration information of the second AI model is information in the pre-configuration information of the first AI model that the second node supports sending to the first node, and the second AI model is one or more AI models in the first AI model.

15. The method according to claim 14, characterized in that The first request message also carries pre-configuration information of the AI ​​model provided by the first node to the second node, and the pre-configuration information of the AI ​​model provided by the first node to the second node includes one or more of the following: Information about the AI ​​model scrambling method supported by the first node, wherein the information about the AI ​​model scrambling method includes: an association relationship between the AI ​​model scrambling method identifier and the corresponding scrambling method and / or scrambling level; Information about the AI ​​model update method supported by the first node, wherein the information about the AI ​​model update method includes: an association relationship between an update method identifier of the AI ​​model and a corresponding update method; Information about the AI ​​model transmission mode supported by the first node, wherein the information about the AI ​​model transmission mode includes: an association relationship between the transmission mode identifier of the AI ​​model and the corresponding transmission mode.

16. The method according to claim 15, characterized in that The pre-configuration information of the first AI model includes one or more of the following: a model identifier corresponding to the first AI model, model information corresponding to the first AI model, a model algorithm identifier corresponding to the first AI model, a service identifier corresponding to the first AI model, a usage identifier corresponding to the first AI model, an AI model scrambling method identifier supported by the first node, an AI model update method identifier supported by the first node, an AI model transmission method identifier supported by the first node, a reason for requesting the first AI model, and a model accuracy corresponding to the first AI model.

17. The method according to any one of claims 14 to 16, characterized in that The second request message further carries one or more of the following: The period for the second node to report the AI ​​model; a message identifier corresponding to the second request message, wherein the message identifier is used to associate with the second response message; The message type corresponding to the second request message.

18. The method according to any one of claims 14 to 17, characterized in that The pre-configuration information of the second AI model includes one or more of the following: the model identifier corresponding to the second AI model, the model information corresponding to the second AI model, the model algorithm identifier corresponding to the second AI model, the service identifier corresponding to the second AI model, and the model accuracy feedback information corresponding to the second AI model, wherein the model accuracy feedback information is used to indicate that the second node supports the model accuracy corresponding to the second AI model sent to the first node.

19. The method according to any one of claims 14 to 18, characterized in that The second response message further carries one or more of the following: Reporting cycle feedback information, where the reporting cycle feedback information is used to indicate an AI model reporting cycle supported by the second node; the message identifier; Failure reason indication information, where the failure reason indication information is used to indicate an identifier and a reason why the second node does not support the first AI model sent to the first node.

20. The method according to any one of claims 14 to 19, characterized in that The method further comprises: The second node sends an AI model update message to the first node, where the AI ​​model update message carries the encrypted second AI model, or the AI ​​model update message carries the encrypted address corresponding to the second AI model, or the AI ​​model update message carries the second AI model, or the AI ​​model update message carries the address corresponding to the second AI model.

21. The method according to any one of claims 14 to 19, characterized in that The method further comprises: The second node receives an AI model confirmation message from the first node, where the AI ​​model confirmation message carries the fourth indication information, where the fourth indication information is used to indicate pre-configuration information of a third AI model, wherein the pre-configuration information of the third AI model is information determined by the first node to request from the second node in the pre-configuration information of the second AI model, and the third AI model is one or more AI models in the second AI model.

22. The method according to claim 21, characterized in that The method further comprises: The AI ​​of the second node receives a model update message from the first node, where the AI ​​model update message carries the encrypted third AI model, or the AI ​​model update message carries the encrypted address corresponding to the third AI model, or the AI ​​model update message carries the third AI model, or the AI ​​model update message carries the address corresponding to the third AI model.

23. A communication device, characterized in that: Used to implement the method according to any one of claims 1 to 11.

24. A communication device, characterized in that: The communication device comprises a processor and a memory, wherein the memory is used to store a computer program or instructions, and the processor is used to execute the computer program or instructions in the memory, so that the method according to any one of claims 1 to 11 is executed.

25. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 11.

26. A computer program product, characterized in that The computer program product comprises means for executing the method according to any one of claims 1 to 11.

Images