Management method, and apparatus

Abstract

The present application provides a management method, and an apparatus. The method comprises: a first network management node determines information about capabilities of artificial intelligence (AI) / machine learning (ML), the information about capabilities of AI / ML comprising one or more of the following: the general capability of AI / ML, the learning capability of AI / ML, the trustworthiness capability of AI / ML, or the processing capability of AI / ML; and the first network management node sends the information about the capabilities of AI / ML to a second network management node. The present application defines the information about capabilities of AI / ML and the acquisition mode for the information about capabilities of AI / ML, and enables the second network management node to indicate the capability requirements of AI / ML to the first network management node, such that the first network management node executes the capabilities corresponding to AI / ML according to the capability requirements of AI / ML, thus improving the network performance.

Description

A management method and device

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on September 27, 2023, with application number 202311269757.6 and application name “A Management Method and Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of artificial intelligence technology, and in particular to a management method and device. Background Art

[0003] Currently, artificial intelligence (AI) / machine learning (ML) technologies and related applications are being increasingly adopted by various industries. The functions of AI / ML are being used in various fields, for example, in management data analytics (MDA) and network data analytical function (NWDAF).

[0004] The 3rd Generation Partnership Project (3GPP) defines the use case of AI / ML capability discovery and mapping, but does not specifically define AI / ML capabilities and AI / ML capability control.

[0005] Summary of the Invention

[0006] This application proposes a management method and device that define AI / ML capability information and a method for obtaining AI / ML capability information, and enables a second network management node to indicate the AI / ML capability requirements to a first network management node, so that the first network management node executes the AI / ML corresponding capabilities according to the AI / ML capability requirements to improve network performance.

[0007] In a first aspect, an embodiment of the present application provides a management method, the method comprising: a first network management node determines capability information of artificial intelligence (AI) machine learning (ML), wherein the capability information of AI / ML comprises one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML; the first network management node sends the capability information of AI / ML to a second network management node.

[0008] In the above method, the capability information of AI / ML may refer to the capability information of ML, or the capability information of AI, or may also refer to the capability information of the AI / ML model, that is, it may refer to the capability information of the AI ​​model, or the capability information of the ML model, wherein the AI / ML model, the AI ​​model, the ML model may also be referred to as an AI / ML entity, an AI entity, an ML entity, and AI / ML may also be AIML, or other terms that can represent AI and ML. The embodiment of the present application defines the capability information of AI / ML and the method for obtaining the AI / ML capability information, so that the second network management node can indicate the capability requirements of AI / ML to the first network management node, so that the first network management node executes the corresponding capabilities of AI / ML according to the capability requirements of AI / ML to improve network performance.

[0009] In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0010] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0011] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0012] In another possible implementation, the first network management node receives a request message from the second network management node, where the request message is used to request capability information of the AI / ML.

[0013] In yet another possible implementation, the first network management node sends updated AI / ML capability information to the second network management node.

[0014] In another possible implementation, the first network management node receives first indication information from the second network management node, where the first indication information is used to indicate a target learning capability, which is one of the learning capabilities of the AI / ML; the first network management node performs model training based on the first indication information to obtain a training result.

[0015] In the above method, the second network management node can send first indication information to the first network management node based on the obtained AI / ML learning capability, indicating the target learning capability. In this way, the second network management node can select appropriate learning methods for different business scenarios, that is, appropriate AI / ML training learning capabilities, so that the first network management node can perform training according to the target learning capability indicated by the first indication information, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirements to the first network management node (for example, different business scenarios are suitable for different learning methods), so that the first network management node executes the corresponding AI / ML capabilities according to the AI / ML capability requirements, for example, the AI / ML learning capability, to improve network performance.

[0016] In another possible implementation, the first network management node receives second indication information from the second network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML; the first network management node performs a trusted assessment of the indicator of the target trusted capability based on the second indication information to obtain a trusted assessment result.

[0017] In the above method, the second network management node can send second indication information to the first network management node based on the obtained trusted capabilities of the AI / ML, indicating the target trusted capability indicator, so that the first network management node performs a trusted assessment of the target trusted capability indicator to obtain a trusted assessment result, thereby improving network performance. In other words, the second network management node indicates the capability requirements of the AI / ML (e.g., the trusted capability requirements of the AI / ML) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities, such as the trusted capabilities of the AI / ML, based on the capability requirements of the AI / ML, thereby improving network performance.

[0018] In another possible implementation, the first network management node receives third indication information from the second network management node, where the third indication information is used to indicate a key performance indicator KPI target or performance measurement target that the model needs to achieve; the first network management node performs model inference based on the third indication information and the reasoning capability of the AI / ML.

[0019] In the above method, by the first network management node receiving the third indication information from the second network management node, the second network management node can make a corresponding request to the first network management node according to the demand, that is, the key performance indicator KPI target or performance measurement target that the model needs to achieve, so that the first network management node performs model reasoning according to the demand of the second network management node, that is, the third indication information, and the determined AI / ML reasoning capability, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirement (for example, the AI / ML reasoning capability requirement) to the first network management node, so that the first network management node executes the corresponding AI / ML capability according to the AI / ML capability requirement, for example, the AI / ML reasoning capability, to improve network performance.

[0020] In another possible implementation, the first network management node receives fourth indication information from the second network management node, where the fourth indication information includes a correspondence between a business scenario identifier and a threshold, where the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; the first network management node deploys a model based on the fourth indication information and the processing capability of the AI / ML.

[0021] In the above method, the first network management node receives the fourth indication information from the second network management node. For example, the second network management node may be an operator, so that the operator can specify the fourth indication information, that is, specify the requirements of the model in a specific scenario, so that the first network management node can deploy the model according to the requirements of the model in the specific scenario. For example, for scenarios with high latency sensitivity such as video games, it is necessary to deploy a highly responsive model to improve network performance and user experience. That is, the second network management node indicates the AI / ML capability requirements (for example, the processing capability requirements of AI / ML) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities according to the AI / ML capability requirements, for example, the processing capability of AI / ML, to improve network performance.

[0022] In another possible implementation, the first network management node sends deployment result information to the second network management node, where the deployment result information includes deployment success or deployment failure.

[0023] In a second aspect, an embodiment of the present application provides a management method, which includes: a second network management node receives artificial intelligence AI / machine learning ML capability information from a first network management node, wherein the AI / ML capability information includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML.

[0024] In the above method, the capability information of AI / ML may refer to the capability information of ML, or the capability information of AI, or may also refer to the capability information of the AI / ML model, that is, it may refer to the capability information of the AI ​​model, or the capability information of the ML model, wherein the AI / ML model, the AI ​​model, the ML model may also be referred to as an AI / ML entity, an AI entity, an ML entity, and AI / ML may also be AIML, or other terms that can represent AI and ML. The embodiment of the present application defines the capability information of AI / ML and the method for obtaining the AI / ML capability information, so that the second network management node can indicate the capability requirements of AI / ML to the first network management node, so that the first network management node executes the corresponding capabilities of AI / ML according to the capability requirements of AI / ML to improve network performance. In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0025] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0026] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0027] In another possible implementation, the second network management node sends a request message to the first network management node, where the request message is used to request capability information of the AI / ML.

[0028] In yet another possible implementation, the second network management node receives updated AI / ML capability information from the first network management node.

[0029] In another possible implementation, the second network management node sends first indication information to the first network management node, where the first indication information is used to indicate a target learning capability, where the target learning capability is one of the learning capabilities of the AI / ML, and the first indication information is used for model training.

[0030] In the above method, the second network management node can send first indication information to the first network management node based on the obtained AI / ML learning capability, indicating the target learning capability. In this way, the second network management node can select appropriate learning methods for different business scenarios, that is, appropriate AI / ML learning capabilities, so that the first network management node can perform training according to the target learning capability indicated by the first indication information, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirements to the first network management node (for example, different business scenarios are suitable for different learning methods), so that the first network management node executes the corresponding AI / ML capabilities according to the AI / ML capability requirements, for example, the AI / ML learning capability, to improve network performance.

[0031] In another possible implementation, the second network management node sends second indication information to the first network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML, and the target trusted capability is used for trusted assessment.

[0032] In the above method, the second network management node can send second indication information to the first network management node based on the obtained trusted capabilities of the AI / ML, indicating the target trusted capabilities, so that the first network management node performs a trusted assessment of the target trusted capabilities indicators to obtain a trusted assessment result, thereby improving network performance. In other words, the second network management node indicates the capability requirements of the AI / ML (e.g., the trusted capability requirements of the AI / ML) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities, such as the trusted capabilities of the AI / ML, based on the capability requirements of the AI / ML, thereby improving network performance.

[0033] In another possible implementation, the second network management node sends third indication information to the first network management node, where the second indication information is used to indicate a key performance indicator KPI target or performance measurement target that the model needs to achieve, and the second indication information is used for model reasoning.

[0034] In the above method, by the first network management node receiving the third indication information from the second network management node, the second network management node can make a corresponding request to the first network management node according to the requirements, that is, the key performance indicator (KPI) target or performance measurement target that the model needs to achieve, so that the first network management node performs model reasoning based on the requirements of the second network management node, that is, the third indication information, and the determined AI / ML reasoning capability, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirements (e.g., the AI / ML reasoning capability requirements) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities, such as the AI / ML reasoning capability, according to the AI / ML capability requirements to improve network performance. In another possible implementation, the second network management node sends a fourth indication information to the first network management node, the fourth indication information including a correspondence between a business scenario identifier and a threshold, the threshold including one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; the fourth indication information is used for model deployment.

[0035] In the above method, the first network management node receives the fourth indication information from the second network management node. For example, the second network management node may be an operator, so that the operator can specify the fourth indication information, that is, specify the requirements of the model in a specific scenario, so that the first network management node can deploy the model according to the requirements of the model in the specific scenario. For example, for scenarios with high latency sensitivity such as video games, it is necessary to deploy a highly responsive model to improve network performance and user experience. That is, the second network management node indicates the capability requirements of AI / ML (for example, the processing capability requirements of AI / ML) to the first network management node, so that the first network management node executes the corresponding capabilities of AI / ML according to the capability requirements of AI / ML, for example, the processing capability of AI / ML, to improve network performance.

[0036] In another possible implementation, the second network management node receives deployment result information from the first network management node, where the deployment result information includes deployment success or deployment failure.

[0037] In a third aspect, an embodiment of the present application provides a management method, which includes: a first network management node determines the general capabilities of artificial intelligence AI / machine learning ML, where the general capabilities of AI / ML include one or more of the following: AI / ML training capabilities, AI / ML testing capabilities, AI / ML reasoning capabilities, or AI / ML simulation capabilities; the first network management node sends the general capabilities of AI / ML to a second network management node.

[0038] In a fourth aspect, an embodiment of the present application provides a management method, which includes: a second network management node receives general capabilities of artificial intelligence AI / machine learning ML from a first network management node, where the general capabilities of AI / ML include one or more of the following: AI / ML training capabilities, AI / ML testing capabilities, AI / ML reasoning capabilities, or AI / ML simulation capabilities.

[0039] In a fifth aspect, an embodiment of the present application provides a management method, which includes: a first network management node determines the learning capability of artificial intelligence AI / machine learning ML, where the learning capability of the AI / ML includes one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the first network management node sends the learning capability of the AI / ML to a second network management node.

[0040] In a sixth aspect, an embodiment of the present application provides a management method, which includes: a second network management node receives the learning capability of artificial intelligence AI / machine learning ML from a first network management node, wherein the learning capability of the AI / ML includes one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning.

[0041] In a seventh aspect, an embodiment of the present application provides a management method, which includes: a first network management node determines the trusted capabilities of artificial intelligence AI / machine learning ML, where the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the first network management node sends the trusted capabilities of the AI / ML to a second network management node.

[0042] In an eighth aspect, an embodiment of the present application provides a management method, which includes: a second network management node receives trusted capabilities of artificial intelligence AI / machine learning ML from a first network management node, where the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability.

[0043] In a ninth aspect, an embodiment of the present application provides a management method, which includes: a first network management node determines the processing capability of artificial intelligence AI / machine learning ML, where the processing capability of AI / ML includes one or more of the following: training processing capability of AI / ML, testing processing capability of AI / ML, reasoning processing capability of AI / ML, or simulation processing capability of AI / ML; the first network management node sends the processing capability of AI / ML to a second network management node.

[0044] In the tenth aspect, an embodiment of the present application provides a management method, which includes: a second network management node receives the processing capability of artificial intelligence AI / machine learning ML from a first network management node, wherein the processing capability of AI / ML includes one or more of the following: training processing capability of AI / ML, testing processing capability of AI / ML, reasoning processing capability of AI / ML, or simulation processing capability of AI / ML.

[0045] In the eleventh aspect, an embodiment of the present application provides a management device, which is a first network management node, including: a processing unit and a transceiver unit, the processing unit is used to determine the capability information of artificial intelligence AI / machine learning ML, and the capability information of AI / ML includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML; the transceiver unit is used to send the capability information of AI / ML to the second network management node.

[0046] In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0047] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0048] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0049] In another possible implementation, the transceiver unit is configured to receive a request message from the second network management node, where the request message is used to request capability information of the AI / ML.

[0050] In another possible implementation, the transceiver unit is configured to send updated AI / ML capability information to the second network management node.

[0051] In another possible implementation, the transceiver unit is used to receive first indication information from the second network management node, where the first indication information is used to indicate a target learning capability, and the target learning capability is one of the learning capabilities of the AI / ML; the processing unit is used to perform model training according to the first indication information to obtain a training result.

[0052] In another possible implementation, the transceiver unit is used to receive second indication information from the second network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML; and the processing unit is used to perform a trusted assessment of the indicator of the target trusted capability to obtain a trusted assessment result.

[0053] In another possible implementation, the transceiver unit is used to receive third indication information from the second network management node, where the third indication information is used to indicate a key performance indicator KPI target or performance measurement target that the model needs to achieve; and the processing unit is used to perform model inference based on the third indication information and the reasoning capability of the AI / ML.

[0054] In another possible implementation, the transceiver unit is used to receive fourth indication information from the second network management node, where the fourth indication information includes a correspondence between a business scenario identifier and a threshold, where the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; and the processing unit is used to deploy a model based on the fourth indication information and the processing capability of the AI / ML.

[0055] In another possible implementation, the transceiver unit is configured to send deployment result information to the second network management node, where the deployment result information includes deployment success or deployment failure.

[0056] Regarding the technical effects brought about by the eleventh aspect or possible implementation methods, reference may be made to the introduction to the technical effects of the first aspect or corresponding implementation methods.

[0057] In the twelfth aspect, an embodiment of the present application provides a management device, which can be a second network management node, including: a processing unit and a transceiver unit, the transceiver unit is used to receive artificial intelligence AI / machine learning ML capability information from the first network management node, and the AI / ML capability information includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML.

[0058] In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0059] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0060] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0061] In another possible implementation, the transceiver unit is configured to send a request message to the first network management node, where the request message is used to request capability information of the AI / ML.

[0062] In another possible implementation, the transceiver unit is configured to receive updated AI / ML capability information from the first network management node.

[0063] In another possible implementation, the transceiver unit is used to send first indication information to the first network management node, where the first indication information is used to indicate a target learning capability, where the target learning capability is one of the learning capabilities of the AI / ML, and the first indication information is used for model training.

[0064] In another possible implementation, the transceiver unit is used to send second indication information to the first network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML, and the target trusted capability is used for trusted assessment.

[0065] In another possible implementation, the transceiver unit is used to send third indication information to the first network management node, the second indication information is used to indicate the key performance indicator KPI target or performance measurement target that the model needs to achieve, and the second indication information is used for model reasoning.

[0066] In another possible implementation, the transceiver unit is used to send fourth indication information to the first network management node, and the fourth indication information includes a correspondence between a business scenario identifier and a threshold, and the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; the fourth indication information is used for model deployment.

[0067] In another possible implementation, the transceiver unit is configured to receive deployment result information from the first network management node, where the deployment result information includes deployment success or deployment failure.

[0068] Regarding the technical effects brought about by the twelfth aspect or possible implementation methods, please refer to the introduction to the technical effects of the second aspect or corresponding implementation methods.

[0069] In the thirteenth aspect, an embodiment of the present application provides a management device, which may be a first network management node. The management device includes at least one processor and a communication interface. The at least one processor calls a computer program or instruction stored in a memory to execute the method described in the above-mentioned first aspect or a possible implementation method of the first aspect.

[0070] In the fourteenth aspect, an embodiment of the present application provides a management device, which may be a second network management node. The management device includes at least one processor and a communication interface. The at least one processor calls a computer program or instruction stored in a memory to execute the method described in the above-mentioned second aspect or a possible implementation method of the second aspect.

[0071] In a fifteenth aspect, an embodiment of the present application provides a chip device, comprising at least one processor, wherein the at least one processor is configured to execute computer programs or instructions to implement the method described in any one of the above aspects.

[0072] In the sixteenth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction runs on a processor, the method described in any one of the above aspects is implemented.

[0073] In the seventeenth aspect, an embodiment of the present application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are run on a computer, the method described in any one of the above aspects is implemented.

[0074] In the eighteenth aspect, an embodiment of the present application provides a communication system, which includes: the device as described in the eleventh aspect and the device as described in the twelfth aspect.

[0075] In the above method, the AI / ML capability information and the method of obtaining the AI / ML capability information are defined through the above method, so that the second network management node indicates the AI / ML capability requirements to the first network management node, so that the first network management node executes the AI / ML corresponding capabilities according to the AI / ML capability requirements to improve network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] FIG1 is a schematic diagram of a service-oriented management architecture provided in an embodiment of the present application;

[0077] FIG2 is a schematic diagram of a management function entity provided in an embodiment of the present application;

[0078] FIG3 is a schematic diagram of a producer and a consumer provided in an embodiment of the present application;

[0079] FIG4 is a schematic diagram of an MnS Consumer-triggered AI / ML model training according to an embodiment of the present application;

[0080] FIG5 is a schematic diagram of an AI / ML capability discovery provided by an embodiment of the present application;

[0081] FIG6 is a flow chart of a management method provided in an embodiment of the present application;

[0082] FIG7 is a flow chart of another management method provided in an embodiment of the present application;

[0083] FIG8 is a schematic diagram of the structure of a management device provided in an embodiment of the present application;

[0084] FIG9 is a schematic structural diagram of another management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of this application.

[0086] References to "one embodiment" or "some embodiments" in this application mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0087] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "plurality" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a, b, and c. Among them, a, b, and c can be single or multiple.

[0088] It is understood that in this application, "indication" can include direct indication, indirect indication, explicit indication, and implicit indication. When describing that a certain indication information is used to indicate A, it can be understood that the indication information carries A, directly indicates A, or indirectly indicates A.

[0089] In this application, the information indicated by the indication information is referred to as the information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc., or the information to be indicated can be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved with the help of the arrangement order of each information agreed in advance (such as specified in the protocol), thereby reducing the indication overhead to a certain extent.

[0090] The information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately. The transmission period and / or transmission timing of these sub-information can be the same or different. The specific transmission method is not limited in this application. The transmission period and / or transmission timing of these sub-information can be predefined, for example, according to a protocol, or can be configured by the transmitting device through sending configuration information to the receiving device.

[0091] It can be understood that "sending" and "receiving" in this application indicate the direction of signal transmission. For example, "sending information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receiving information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface.

[0092] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0093] The communication method provided in the embodiment of the present application can be applied to cellular communication systems related to the third generation partnership project (3GPP), for example, fourth generation (4G) communication systems, such as long term evolution (LTE) communication systems, and can also be applied to fifth generation (5G) communication systems, such as 5G new radio (NR) communication systems, or to various future communication systems, such as sixth generation (6G) communication systems. The method provided in the embodiment of the present application can also be applied to Bluetooth systems, wireless fidelity (WiFi) systems, LoRa systems or Internet of Vehicles systems, communication systems that support the integration of multiple wireless technologies, and device-to-device (D2D) systems. The method provided in the embodiment of the present application can also be applied to satellite communication systems, wherein the satellite communication system can be integrated with the above-mentioned communication system. The wireless communication systems involved in this application also include but are not limited to: narrowband Internet of Things (NB-IoT) system, global system for mobile communications (GSM), enhanced data rate for GSM evolution (EDGE), wideband code division multiple access (WCDMA), code division multiple access 2000 (CDMA2000), or time division-synchronization code division multiple access (TD-SCDMA).

[0094] Please refer to Figure 1, which is a schematic diagram of a service-oriented management architecture provided in an embodiment of the present application. Among them, the service-oriented management architecture includes a business support system (BSS), a cross-domain management function unit (CD-MnF), a domain management function unit (Domain-MnF) and a network element (element). It should be understood that the cross-domain management function unit can be a network management system (NMS), a management service producer (MnS Producer), a management service consumer (MnS Consumer) and other nodes, and the domain management function unit can be an element management system (EMS), a wireless automation engine (MAE), a MnS Producer, a MnS Consumer and other nodes.

[0095] If the management service is a management service provided by a cross-domain management functional unit, the cross-domain management functional unit is the management service producer, and the business support system is the management service consumer.

[0096] If the management service is a management service provided by a domain management functional unit, the domain management functional unit is the management service producer, and the cross-domain management functional unit is the management service consumer.

[0097] When the management service is a management service provided by a network element, the network element is the management service producer and the domain management functional unit is the management service consumer.

[0098] Business support systems (BSSs) are oriented towards communications services and provide functions and management services such as billing, settlement, accounting, customer service, sales, network monitoring, communications service lifecycle management, and business intent translation. BSSs can be either carrier operations systems or vertical industry operations systems (OT systems).

[0099] The cross-domain management function unit, also known as the network management function unit (NMF), can be a network management entity such as an NMS or a network function management service consumer (NFMS_C). The cross-domain management function unit provides one or more of the following management functions or services: network lifecycle management, network deployment, network fault management, network performance management, network configuration management, network assurance, network optimization, and translation of service producer's network intent (intent from communication service provider, Intent-CSP).

[0100] Among them, the network referred to in the above-mentioned management function or management service may include one or more network elements or subnetworks, or it may be a network slice. That is to say, the network management function unit may be a network slice management function unit (network slice management function, NSMF), or a cross-domain management data analytical function unit (management data analytical function, MDAF), or a cross-domain self-organization network function (SON Function), or a cross-domain AIML management function unit (AI / ML management service, AI / ML function) or a cross-domain intent driven management function unit (intent driven management service, IDMS).

[0101] Optionally, in certain deployment scenarios, the cross-domain management functional unit can also provide sub-network lifecycle management, sub-network deployment, sub-network fault management, sub-network performance management, sub-network configuration management, sub-network assurance, sub-network optimization functions, sub-network service producer network intent (Intent-CSP) or sub-network service consumer network intent (Intent from communication service consumer, Intent-CSC) translation, etc. The sub-network here consists of multiple small sub-networks, which can be network slicing sub-networks.

[0102] The domain management function (Domain-MnF) is also called the network management function (NMF) or the network element management function. For example, the domain management function can be a network element management entity such as the MAE, EMS, or the network function management service provider (NFMS_P).

[0103] The domain management functional unit provides one or more of the following functions or management services: lifecycle management of sub-networks or network elements, deployment of sub-networks or network elements, fault management of sub-networks or network elements, performance management of sub-networks or network elements, assurance of sub-networks or network elements, optimization of sub-networks or network elements, and translation of intents from network operators (Intent-NOPs) for sub-networks or network elements. A sub-network here includes one or more network elements. A sub-network may also include sub-networks, i.e., one or more sub-networks form a larger sub-network.

[0104] Optionally, the subnetwork here can also be a network slice subnetwork. The domain management system can be a network slice subnet management function unit (NSSMF), a domain management data analytical function unit (Domain MDAF), a domain self-organization network function (SON Function), a domain intent management function unit Intent Driven MnS, etc.

[0105] The domain management functional units can be classified as follows:

[0106] Based on network type, they can be divided into: radio access network domain management function (RAN-Domain-MnF), core network domain management function (CN-Domain-MnF), transport network domain management function (TN-Domain-MnF), etc. It should be noted that the domain management function unit can also be a domain network management system that can manage one or more of the access network, core network, or transport network.

[0107] According to administrative region classification, it can be divided into: domain management functional units of a certain region, such as Shanghai domain management functional unit, Beijing domain management functional unit, etc.

[0108] Network elements are entities that provide network services, including core network elements and access network elements. Core network elements include: access and mobility management function (AMF), session management function (SMF), policy control function (PCF), network data analytical function (NWDAF), network repository function (NRF), and gateways. Access network elements include: base stations (such as gNBs and eNBs), central unit control plane (CUCP), central unit (CU), distributed unit (DU), and central unit user plane (CUUP).

[0109] Among them, the network element can provide one or more of the following management functions or management services: network element lifecycle management, network element deployment, network element fault management, network element performance management, network element assurance, network element optimization function and network element intent translation, etc.

[0110] Please refer to Figure 2, which is a schematic diagram of a management function entity provided by the present application; Figure 2 is a further explanation of the service-oriented management architecture based on Figure 1. The management function (MnF) is a management entity defined by the 3rd generation partnership project (3GPP), and its externally visible behavior and interface are defined as management services (MnS). In the management architecture of the service, MnF can play the role of MnS Producer or MnS Consumer. For example, as shown in Figure 2, the management service produced by the management service producer played by MnF may have multiple consumers. MnF can consume multiple management services from one or more management service producers. In other words, the management service producer played by MnF can manage multiple management service consumers, and can also obtain management information from multiple management service consumers.

[0111] Exemplarily, the management service producer may be a network element management entity such as MAE or EMS.

[0112] Exemplarily, the management service consumer may be specifically an NMS, etc.

[0113] Management service producers (e.g., MAE, EMS, etc.) and management service consumers (e.g., NMS) can be collectively referred to as network management entities. In other words, a network management entity can refer to a management service producer, a management service consumer, an NMS, a MAE, or an EMS.

[0114] Please refer to Figure 3, which is a schematic diagram of a producer and consumer provided in an embodiment of the present application. The AI / ML model training capability is provided by the MnS Producer to the authorized MnS Consumer through the model training management service. For example, the MnS Producer sends the AI / ML model training capability to the MnS Consumer through the AI / ML model training management service service interface. The AI / ML model training process can include two triggering methods:

[0115] In the first approach, AI / ML model training is triggered by an MnS Consumer. This can be triggered by requests from one or more MnS Consumers. The MnS Consumers may provide source data as training data input, and the MnS Producer returns a response message to notify the MnS Consumers whether the training request was accepted. See Figure 4, which is a schematic diagram of an MnS Consumer-triggered AI / ML model training method provided in an embodiment of the present application. The details are as follows:

[0116] Step 1: MnS Consumer sends an AI / ML model training request message to MnS Producer.

[0117] The interface between MnS Consumer and MnS Producer can provide machine learning model training management services.

[0118] Model training, MLMT MnS) service interface, MnS Consumer sends an AI / ML model training request message to MnS Producer. MnS Consumer can send an AI / ML model training request message to MnS Producer through the MLMT MnS service interface.

[0119] Optionally, the MnS Consumer may indicate the model function or purpose of the AI / ML model, such as coverage problem analysis; the MnS Consumer may also send source data to the MnS Producer, which may be the input of candidate training data.

[0120] Step 2: MnS Producer sends a response message to MnS Consumer.

[0121] The response message is a response to the training request message.

[0122] Step 3: MnS Producer sends the training results to MnS Consumer.

[0123] Before sending training results to the MnS Consumer, the MnS Producer selects training data. Optionally, the MnS Producer can select source data provided by the MnS Consumer as training data. Alternatively, the MnS Producer can select other available data as training data. The MnS Producer uses the training data to train the AI / ML model.

[0124] The training results may include the location of the trained AI / ML model, etc.

[0125] In the second approach, AI / ML model training is triggered by the MnS Producer. For example, AI / ML model training is triggered based on feedback received from consumers, as a result of an AI / ML model performance evaluation, or when new training data describing new network states / events becomes available. The MnS Producer then selects training data. Optionally, the MnS Producer can select source data provided by the MnS Consumer as training data. Alternatively, the MnS Producer can select other available data as training data. The MnS Producer trains the AI / ML model using the training data. The MnS Producer then sends the training results to the MnS Consumer that has subscribed to receive them. The training results may optionally include information such as the location of the trained AI / ML model.

[0126] Please refer to FIG5 , which is a schematic diagram of an AI / ML capability discovery provided by an embodiment of the present application, as follows:

[0127] Step 1: MnS Consumer sends a request message to MnS Producer.

[0128] The request message is used to request AI / ML capabilities.

[0129] Step 2: MnS Producer sends AI / ML capabilities to MnS Consumer.

[0130] As can be seen from Figure 5, the above is an AI / ML capability discovery and mapping use case, but it does not specifically define the AI / ML capabilities in detail. For example, regarding the processing capabilities of AI / ML reasoning functions, different entities that apply ML models to AI / ML reasoning functions may have different reasoning requirements and capabilities. Therefore, the processing capabilities of the target reasoning function are also important for model selection and deployment. For example, when producers perform model selection and deployment, they also need to consider other consumer needs, such as the processing capability requirements (latency sensitivity, available resources) and energy consumption requirements of the reasoning entities applicable to different models. Producers comprehensively select appropriate model deployments based on processing capabilities and requirements. For example, the processing capability of the reasoning entity also affects the consumer's execution model selection and deployment. For example, for reasoning scenarios with high latency sensitivity, such as video game reasoning, it is necessary to select a model with high responsiveness. For example, the available resources for reasoning, such as memory size and CPU conditions, also affect model selection. Therefore, how to obtain these reasoning processing capabilities in this scenario to select the appropriate model deployment to meet the reasoning requirements is a technical problem that people in this field are currently solving. In order to solve the above problems, the embodiments of this application propose the following solutions.

[0131] Based on the service-oriented management architecture provided in Figures 1 and 2, an embodiment of the present application provides a management method. Please refer to Figure 6, which is a flow chart of a management method provided in an embodiment of the present application. The method may include the following steps S601-S603.

[0132] Step S601: The first network management node determines AI / ML capability information.

[0133] The first network management node is a producer, such as an AI / ML producer (AI / ML producer), an AI / ML training producer, an AI / ML reasoning producer, an AI / ML testing producer, an AI / ML simulation producer, etc. The first network management node may be a Domain-MnF, such as an EMS or MAE.

[0134] It should be noted that the AI / ML capability information determined by the first network management node may also be understood as the AI / ML capability information possessed by the first network management node.

[0135] It should also be noted that the AI / ML capability information here may refer to the capability information of ML, or the capability information of AI, or it may refer to the capability information of the AI / ML model, that is, it may refer to the capability information of the AI ​​model, or the capability information of the ML model, and this application does not limit this.

[0136] It should also be noted that the AI / ML model, AI model, and ML model referred to in this application can also be referred to as AI / ML entity, AI entity, and ML entity.

[0137] It should also be noted that the AI / ML referred to in this application may also be AIML, or other terms that can represent AI and ML, and this application is not limited here.

[0138] It should also be noted that the model mentioned in this application refers to the AI / ML model.

[0139] The AI / ML capability information includes one or more of the following: general AI / ML capabilities, AI / ML learning capabilities, AI / ML trust capabilities, or AI / ML processing capabilities. It should be noted that the AI / ML capability information defined in this embodiment of the application can be applied to AI / ML model training triggered by the MnS Consumer in Figure 4 or to AI / ML model training triggered by the MnS Producer, and this embodiment of the application does not limit this.

[0140] Among them, the general capabilities of AI / ML include one or more of the following: AI / ML training capabilities, AI / ML testing capabilities, AI / ML reasoning capabilities, or AI / ML simulation capabilities. AI / ML training capabilities include joint training capabilities, which include the ability to jointly train between models. Optionally, this may refer to the ability to jointly train between two models, or between multiple models, and this is not limited in the embodiments of this application. In one example, the joint training capability may include the ability to jointly train between Model 1 and Model 2. For example, the output of Model 1 serves as the input data for Model 2, and Models 1 and 2 are jointly trained. AI / ML testing capabilities include joint testing capabilities, which include the ability to jointly test between models. Optionally, this may refer to the ability to jointly test between two models, or between multiple models, and this is not limited in the embodiments of this application. In one example, the joint testing capability may include the ability to jointly test between Model A and Model B. AI / ML reasoning capabilities include analytical and decision-making capabilities. For example, analytical capabilities may include network data analysis capabilities and / or management data analysis capabilities. In one example, the management data analysis capability may include coverage analysis, mobility analysis, etc. (specifically, reference may be made to the use case scenario of management data analysis defined in 3GPPTS 28.104, which is not described in detail in this application). In another example, the network data analysis capability may include network data analysis of slice load, network data analysis of quality of service (QoS), etc. (specifically, reference may be made to the use case scenario of network data analysis defined in 3GPP TS 23.288, which is not described in detail in this application). The decision-making capability may include one or more of the following: self-organization network (SON) optimization capability, radio access network intelligence (RANintellgence) optimization capability (specifically, reference may be made to the use case scenario of RAN intelligence defined in 3GPPTR 37.340, which is not described in detail in this application), or centralized energy saving (centralized ES) optimization capability. Among them, self-organizing network optimization can also be referred to as self-optimization function optimization. In an example, the SON optimization capability may include coverage optimization, mobility optimization, or energy saving optimization (for details, please refer to the use case scenario of SON optimization defined in 3GPP TS 28.313, which will not be described in detail in this application).The simulation capability of AI / ML refers to the ability to simulate AI / ML models in specific simulation environments, such as the simulation environment of twin networks and the simulation environment that simulates the existing network, and perform reasoning of AI / ML models to evaluate the reasoning results and their impact on network performance.

[0141] AI / ML learning capabilities include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning. Alternatively, AI / ML learning capabilities can be included in AI / ML training capabilities. This means that AI / ML training capabilities can also include AI / ML learning capabilities, or that AI / ML learning capabilities can be a subset of AI / ML training capabilities. For example, AI / ML models can be trained using federated learning.

[0142] Among them, AI / ML trustworthy capabilities include one or more of the following: explainability, robustness, or fairness. Optionally, AI / ML trustworthy capabilities can be divided into the following four categories: trustworthy capabilities for training, trustworthy capabilities for testing, trustworthy capabilities for simulation, and trustworthy capabilities for reasoning. Taking the example of trustworthy capabilities for training, trustworthy capabilities for training can include one or more of the following: data explainability, data robustness, data fairness, trustworthy capabilities for training, trustworthy capabilities for training, or trustworthy capabilities for training. Trustworthy capabilities for testing can include one or more of the following: data explainability, data robustness, data fairness, trustworthy capabilities for testing, trustworthy capabilities for testing, robustness for testing, or fairness for testing. It should be noted that the data here refers to data used for model training, model testing, model reasoning, or model simulation. Similarly, the specific content of trustworthy capabilities for simulation and reasoning is similar to that of trustworthy capabilities for training and will not be further elaborated here.

[0143] Among them, the processing capability of AI / ML may include: AI / ML training processing capability, AI / ML test processing capability, AI / ML reasoning processing capability or AI / ML simulation processing capability. It should be noted that the training processing capability of AI / ML here refers to the processing capability of the module or entity where the training function of the AI / ML model is located, the testing processing capability of AI / ML refers to the processing capability of the module or entity where the testing function of the AI / ML model is located, the reasoning processing capability of AI / ML refers to the processing capability of the module or entity where the reasoning function of the AI / ML model is located, and the simulation processing capability of AI / ML refers to the processing capability of the module or entity where the simulation function of the AI / ML model is located. Among them, the processing capability may include one or more of the following: dedicated resources, latency sensitivity or execution time. Optionally, dedicated resources may include available memory and central processing unit (CPU). In one example, the training processing capability of AI / ML may include the dedicated resources available for training, for example, available memory for training.

[0144] Optionally, the first network management node may also create and configure an AI / ML capability (AIMLCapabilites) instance. For example, it may be an AI / ML capability information object class (AIMLCapabilities IOC). The specific object class attribute information is shown in Table 1 and Table 2, as shown below:

[0145] Table 1

[0146] Table 2

[0147] Step S602: The first network management node sends AI / ML capability information to the second network management node.

[0148] The second network management node is a consumer, for example, an AI / ML consumer, an AI / ML training consumer, an AI / ML reasoning consumer, an AI / ML testing consumer, an AI / ML simulation consumer, etc. The second network management node may be a CD-MnF, for example, an NMS.

[0149] In one possible implementation, before a first network management node sends AI / ML capability information to a second network management node, the first network management node receives a request message from the second network management node requesting the AI / ML capability information. Specifically, the second network management node may send a capability query message to the first network management node to query the AI / ML capability information. Upon receiving the capability query message, the first network management node sends a capability query result to the second network management node, the capability query result including the AI / ML capability information. It should be noted that the first network management node may send the AI / ML capability information to the second network management node after receiving the request message or capability query message from the second network management node, or the first network management node may send the AI / ML capability information to the second network management node after receiving the control parameters of the AI / ML capability report of the second network management node. Optionally, the control parameter may be a reporting period, a reporting event (the event may be reported when a certain threshold is met, and the threshold may be the performance threshold of the existing network), or the first network management node may actively report, that is, the first network management node may send the AI / ML capability information to the second network management node without receiving a request message or capability query message.

[0150] In another possible implementation, after the first network management node sends the AI / ML capability information to the second network management node, the first network management node may send the updated AI / ML capability information to the second network management node. In other words, after the first network management node updates the AI / ML capability information, it may send the updated AI / ML capability information to the second network management node.

[0151] In another possible implementation, the first network management node receives first indication information from the second network management node, where the first indication information is used to indicate the target learning capability; the first network management node performs model training according to the first indication information to obtain a training result.

[0152] Among them, the target learning capability is one of the learning capabilities of AI / ML. The purpose of model training is to use a specific learning method through a given data set to allow the model to learn the characteristics and rules of the data, so that it can predict or classify unknown data. The training results may include one or more of the following: model identification, model performance, or model location. The learning method refers to the method used in model training. The process in which the first network management node receives the first indication information from the second network management node and performs model training according to the first indication information to obtain the training result can be understood as the second network management node specifying the target learning capability to complete a specific business scenario, and accordingly, the first network management node performs training according to the target learning capability.

[0153] In one example, when the business scenario of the second network management node is an energy-saving optimization scenario, the second network management node sends a first indication message to the first network management node, and the target learning capability indicated by the first indication message is transfer learning. After the first network management node receives the transfer learning indicated by the first indication message, it performs model training according to the transfer learning algorithm to obtain a training result. Optionally, the training result may include whether a specified learning method is used. For example, the training result includes the use of a specified learning method, that is, transfer learning. For example, the training result includes not using a specified learning method. When the training result includes not using a specified learning method, other learning methods used may be optionally reported. In another example, when the business scenario is a mobility optimization scenario, the second network management node sends a first indication message to the first network management node, and the target learning capability indicated by the first indication message is generative learning. After the first network management node receives the generative learning indicated by the first indication message, it performs model training according to the generative learning algorithm to obtain a training result. Optionally, the training result may include whether a specified learning method is used. For example, the training result includes the use of a specified learning method, that is, generative learning. For example, the training result includes not using a specified learning method. When the training result includes not using a specified learning method, other learning methods used may be optionally reported.

[0154] In the above method, the second network management node can send first indication information to the first network management node based on the obtained AI / ML learning capability, indicating the target learning capability. In this way, the second network management node can select appropriate learning methods for different business scenarios, that is, appropriate AI / ML training learning capabilities, so that the first network management node can perform training according to the target learning capability indicated by the first indication information, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirements to the first network management node (for example, different business scenarios are suitable for different learning methods), so that the first network management node executes the corresponding AI / ML capabilities according to the AI / ML capability requirements, for example, the AI / ML learning capability, to improve network performance.

[0155] In yet another possible implementation, the first network management node receives second indication information from the second network management node, and the first network management node performs a trustworthy evaluation of the indicator of the target trusted capability to obtain a trustworthy evaluation result.

[0156] The second indication information is used to indicate an indicator of a target trusted capability, which is one of the trusted capabilities of AI / ML. Accordingly, the first network management node performs a trusted assessment of the indicator of the target trusted capability and reports the trusted assessment result. When the target trusted capability indicated by the second indication information is one of the trusted capabilities for training, the first network management node obtains a trusted assessment result during the model training process or after the model training is completed. The trusted assessment result includes the trustworthiness of the training process or the trustworthiness of the training result. When the target trusted capability indicated by the second indication information is one of the trusted capabilities for testing, the first network management node obtains a trusted assessment result during the model testing process or after the model testing is completed. The trusted assessment result includes the trustworthiness of the model testing process or the trustworthiness of the test result. When the target trusted capability indicated by the second indication information is one of the trusted capabilities for simulation, the first network management node obtains a trusted assessment result during the model simulation process or after the model simulation is completed. The trusted assessment result includes the trustworthiness of the model simulation process or the trustworthiness of the simulation result. When the target trusted capability indicated by the second indication information is one of the trusted capabilities of reasoning, the first network management node obtains a trusted evaluation result during the execution of the model reasoning process or after the model reasoning is completed. The trusted evaluation result includes the credibility of the model reasoning process or the credibility of the reasoning result.

[0157] Among them, optionally, the credibility of the training process or the credibility of the training results can be understood as the interpretability of the training process or the training results, the robustness of the training process or the training results, or the fairness of the training process or the training results. By analogy, the credibility of the test process or the credibility of the test results, the credibility of the simulation process or the credibility of the simulation results, or the credibility of the reasoning process or the credibility of the reasoning results can be similar to the credibility of the training results, which will not be repeated here. Optionally, the credibility evaluation result can be embodied in the form of an indicator, and there is a corresponding relationship between the indicator and the interpretability ability, robustness ability or fairness ability. For example, the indicator corresponding to the interpretability ability can be a specific score value, the indicator corresponding to the robustness ability can be a numerical percentage, and the indicator corresponding to the fairness ability can be a level, which is not limited in the embodiments of the present application.

[0158] In one example, the second network management node sends a second indication message to the first network management node, where the second indication message is used to indicate an indicator of a target trusted capability, where the indicator is a score value, and the target trusted capability is the interpretability capability of the training, that is, the second indication message indicates the score of the interpretability capability of the training. The first network management node receives the target trusted capability and obtains a trusted evaluation result during or after the model training, where the trusted evaluation result includes an interpretability capability of the training result of 90 points (out of 100 points). Optionally, the first network management node sends the trusted evaluation result to the second network management node.

[0159] In the above method, the second network management node can send second indication information to the first network management node based on the obtained trusted capabilities of the AI / ML, indicating the target trusted capabilities, so that the first network management node performs a trusted assessment of the relevant indicators of the target trusted capabilities and reports the trusted assessment results, thereby improving network performance. In other words, the second network management node indicates the capability requirements of the AI / ML (e.g., the trusted capability requirements of the AI / ML) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities, such as the trusted capabilities of the AI / ML, based on the capability requirements of the AI / ML, thereby improving network performance.

[0160] In another possible implementation, the first network management node receives third indication information from the second network management node, and the first network management node performs model inference based on the third indication information and the reasoning capability of AI / ML.

[0161] Among them, the third indication information is used to indicate the key performance indicator (KPI) target and / or performance measurement (PM) target that the model needs to achieve (for details, please refer to the relevant indicators defined in TS 28.554 and TS 28.552 in 3GPP, which will not be repeated in this application), or the target of a specific business scenario. Optionally, the KPI can be a category, such as coverage, capacity, voice, switching, etc. For example, coverage may include the ratio of coverage holes, the ratio of weak coverage, the ratio of over-coverage, the ratio of cross-area coverage, or the ratio of uplink and downlink imbalance; capacity includes user throughput, the number of users in the cell, etc.; voice includes QoS flow-related measurements; switching includes switching success rate, switching failure probability, premature switching ratio, or late switching ratio, etc. Optionally, the target of a specific business scenario may include one or more of the following: energy saving target, load target or coverage optimization target. For example, the energy saving target may include the power consumption of the base station. The load target may include the utilization rate of the uplink and downlink physical resource blocks (PRBs), the number of radio resource connections (RRCs), and the coverage optimization target may include the satisfaction rate of the reference signal received power (RSRP), the satisfaction rate of the reference signal received quality (RSRQ), the satisfaction rate of the signal to interference plus noise ratio (SINR), the switching performance, or the average cell throughput, etc. Optionally, the first network management node performs model reasoning based on the third indication information and the reasoning capability of the AI / ML to obtain a reasoning result, and the reasoning result may include the reasoning capability of the AI / ML, and may also include a correspondence between the third indication information and the reasoning capability of the AI / ML. The reasoning capability of the AI / ML here may be the reasoning capability of the AI / ML selected by the first network management node according to the third indication information. For example, the third indication information indicates that the KPI that the model needs to achieve is coverage threshold 1 and switching threshold 2, and the first network management node determines that the selected AI / ML reasoning capability includes coverage optimization and mobility optimization based on the third indication information.

[0162] In one example, the third indication information is KPI1, KPI1 refers to the coverage threshold 1 reached, and the reasoning capabilities of AI / ML selected by the first network management node are coverage optimization and load optimization, wherein coverage optimization and load optimization belong to SON optimization in the reasoning capabilities of AI / ML. The first network management node performs model reasoning based on the third indication information KPI1 and the reasoning capabilities of AI / ML to obtain an inference result. Optionally, the inference result can be as shown in Table 3, where the inference result includes the correspondence between the third indication information and the reasoning capabilities of AI / ML, that is, the inference result includes KPI1, coverage optimization and load optimization.

[0163] Table 3

[0164] In the above method, by the first network management node receiving the third indication information from the second network management node, the second network management node can make a corresponding request to the first network management node according to the demand, that is, the key performance indicator KPI target or performance measurement target that the model needs to achieve, so that the first network management node performs model reasoning according to the demand of the second network management node, that is, the third indication information, and the determined AI / ML reasoning capability, thereby improving network performance. That is, the second network management node indicates the AI / ML capability requirement (for example, the AI / ML reasoning capability requirement) to the first network management node, so that the first network management node executes the corresponding AI / ML capability according to the AI / ML capability requirement, for example, the AI / ML reasoning capability, to improve network performance.

[0165] In another possible implementation, the first network management node receives fourth indication information from the second network management node, and the first network management node deploys the model according to the fourth indication information and the processing capability of AI / ML.

[0166] The fourth indication information includes a correspondence between a business scenario identifier and a threshold value. Specifically, the fourth indication information includes the business scenario identifier and the threshold value. The threshold value includes one or more of the following: a latency sensitivity threshold value, an execution time threshold value, or a threshold value for available dedicated resources. Optionally, the fourth indication information may directly indicate a correspondence between the business scenario and the threshold value. Specifically, the fourth indication information includes the business scenario and the threshold value. For example, the business scenario may be indicated in a string format. For details, see Table 4.

[0167] Table 4

[0168] Optionally, the thresholds of the same indicator in different business scenarios may be different. In one example, for example, when the business scenario is an energy-saving optimization scenario, the threshold of delay sensitivity is threshold 1, and when the business scenario is a coverage optimization scenario, the threshold of delay sensitivity is threshold 4, where threshold 1 and threshold 4 may be different.

[0169] After the first network management node deploys the model according to the fourth indication information and the processing capability of AI / ML, the first network management node may send deployment result information to the second network management node.

[0170] The deployment result information includes deployment success or deployment failure. When deployment is successful, the first network management node may determine the model's energy consumption and resource usage information, and optionally, send the model's energy consumption and resource usage information to the second network management node. When deployment fails, the first network management node determines the reason for the deployment failure, which may optionally be insufficient computing energy, insufficient energy storage, or energy consumption issues. Optionally, the first network management node sends the reason for the deployment failure to the second network management node.

[0171] In one example, the fourth indication information is a business scenario identified as identification 1, and its requirements for training processing capabilities are: delay sensitivity is threshold 1, execution time is threshold 2, and available dedicated resources are threshold 3. The processing capability of AI / ML includes training processing capabilities, and the training processing capabilities include: delay sensitivity is threshold 7, execution time is threshold 8, and the threshold of dedicated resources for training is threshold 9, wherein threshold 1 is greater than threshold 7, threshold 2 is greater than threshold 8, and threshold 3 is less than threshold 9, that is, the training processing capability of AI / ML can meet the requirements of the business scenario, that is, the business scenario corresponding to business scenario identification 1 has requirements for training processing capabilities. The first network management node deploys the model according to the fourth indication information and the processing capability of AI / ML, and sends deployment result information to the second network management node, and the deployment result information includes successful deployment.

[0172] In the above method, the first network management node receives the fourth indication information from the second network management node. For example, the second network management node may be an operator, so that the operator can specify the fourth indication information, that is, specify the requirements of the model in a specific scenario, so that the first network management node can deploy the model according to the requirements of the model in the specific scenario. For example, for scenarios with high latency sensitivity such as video games, it is necessary to deploy a highly responsive model. For example, the requirements for the processing capabilities of the inference entities applicable to different models (latency sensitivity, available resources) and energy consumption requirements. The first network management node comprehensively selects the appropriate model deployment based on the processing capabilities and requirements, thereby improving network performance and user experience. That is, the second network management node indicates the AI / ML capability requirements (for example, the processing capability requirements of AI / ML) to the first network management node, so that the first network management node executes the corresponding AI / ML capabilities according to the AI / ML capability requirements, for example, the processing capability of AI / ML, to improve network performance.

[0173] Optionally, before the second network management node sends the first indication information, the second indication information, the third indication information, or the fourth indication information to the first network management node, the second network management node may define an AI / ML capability control (AIMLCapabilitesControl) object instance, such as an AI / ML capability control object class (AIMLcapabilitescontrolinformation object class, AIMLCapabilitesControl IOC), wherein AIMLCapabilitesControl may include one or more of the following: the first indication information, the second indication information, the third indication information, or the fourth indication information. Accordingly, after the first network management node receives the first indication information, the second indication information, the third indication information, or the fourth indication information, it may create an AIMLCapabilitesControl object instance based on the first indication information, the second indication information, the third indication information, or the fourth indication information, and configure the first indication information, the second indication information, the third indication information, or the fourth indication information to the AIMLCapabilitesControl object instance.

[0174] Step S603: The second network management node receives the AI / ML capability information from the first network management node.

[0175] The second network management node has read access to the AI / ML capability information in the AI / ML capability information object class, but cannot write access to it. That is, the second network management node cannot modify the AI / ML capability information.

[0176] In the above method, the AI / ML capability information and the method of obtaining the AI / ML capability information are defined through the above method, so that the second network management node indicates the AI / ML capability requirements to the first network management node, so that the first network management node executes the AI / ML corresponding capabilities according to the AI / ML capability requirements to improve network performance.

[0177] Optionally, please refer to FIG7 , which is a flowchart of another management method provided in an embodiment of the present application, as follows:

[0178] Step S701: The second network management node sends a request message to the first network management node.

[0179] The request message is used to request the capability information of the AI / ML. The capability information of the AI / ML can refer to the relevant description in step S601 and will not be repeated here.

[0180] Step S702: The first network management node receives a request message from the second network management node.

[0181] Before receiving the request message from the second network management node, the first network management node may create and configure an AI / ML capability (AIMLCapabilites) instance. For example, it may be an AI / ML capability information object class (AIMLCapabilities IOC), as described above.

[0182] Step S703: The first network management node sends AI / ML capability information to the second network management node.

[0183] For details, please refer to step S602, which will not be repeated here.

[0184] Step S704: The second network management node receives the AI / ML capability information from the first network management node.

[0185] Step S705: the second network management node sends the first indication information, the second indication information, the third indication information or the fourth indication information to the first network management node.

[0186] It should be noted that this step is optional.

[0187] For explanations of the first indication information, the second indication information, the third indication information, or the fourth indication information, please refer to the description in step S602. Before the second network management node sends the first indication information, the second indication information, the third indication information, or the fourth indication information to the first network management node, the second network management node may define an AI / ML capability control (AIMLCapabilitesControl) object instance. For details, please refer to the description in step S602.

[0188] Step S706: The first network management node receives the first indication information, the second indication information, the third indication information or the fourth indication information from the second network management node.

[0189] It should be noted that this step is optional.

[0190] Step S707: the first network management node performs relevant processing according to the first indication information, the second indication information, the third indication information or the fourth indication information.

[0191] It should be noted that this step is optional. For example, the first network management node may perform model training according to the first indication information to obtain a training result. For details, please refer to the relevant description in step S602.

[0192] For example, the first network management node performs a trustworthy evaluation on the indicator of the target trustworthy capability to obtain a trustworthy evaluation result. For details, please refer to the relevant description in step S602.

[0193] For example, the first network management node performs model inference according to the third indication information and the reasoning capability of AI / ML. For details, please refer to the relevant description in step S602.

[0194] For example, the first network management node deploys the model according to the fourth indication information and the processing capability of AI / ML. For details, please refer to the relevant description in step S602.

[0195] In the above method, the capability information of AI / ML and the method for obtaining the capability information of AI / ML are defined in the above manner, so that the second network management node can indicate the capability requirements of AI / ML to the first network management node, thereby enabling the first network management node to execute the corresponding capabilities of AI / ML according to the capability requirements of AI / ML to improve network performance.

[0196] The above describes in detail the method of the embodiment of the present application, and the following provides an apparatus of the embodiment of the present application.

[0197] Please refer to Figure 8, which is a schematic diagram of the structure of a management device 800 provided in an embodiment of the present application. The management device 800 can be a first network management node or a chip in the first network management node. The management device 800 can include a processing unit 801 and a transceiver unit 802. The details of each unit are as follows:

[0198] The processing unit 801 is used to determine the capability information of artificial intelligence AI / machine learning ML, where the capability information of AI / ML includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML; the transceiver unit 802 is used to send the capability information of AI / ML to the second network management node.

[0199] In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0200] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0201] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0202] In another possible implementation, the transceiver unit 802 is configured to receive a request message from the second network management node, where the request message is used to request capability information of the AI / ML.

[0203] In another possible implementation, the transceiver unit 802 is configured to send updated AI / ML capability information to the second network management node.

[0204] In another possible implementation, the transceiver unit 802 is used to receive first indication information from the second network management node, where the first indication information is used to indicate a target learning capability, which is one of the learning capabilities of the AI / ML; the processing unit 801 is used to perform model training according to the first indication information to obtain a training result.

[0205] In another possible implementation, the transceiver unit 802 is used to receive second indication information from the second network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML; the processing unit 801 is used to perform a trusted assessment of the indicator of the target trusted capability to obtain a trusted assessment result.

[0206] In another possible implementation, the transceiver unit 802 is used to receive third indication information from the second network management node, where the third indication information is used to indicate a key performance indicator KPI target or performance measurement target that the model needs to achieve; and the processing unit 801 is used to perform model reasoning based on the third indication information and the reasoning capability of the AI / ML.

[0207] In another possible implementation, the transceiver unit 802 is used to receive fourth indication information from the second network management node, where the fourth indication information includes a correspondence between a business scenario identifier and a threshold, where the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; and the processing unit 801 is used to deploy a model based on the fourth indication information and the processing capability of the AI / ML.

[0208] In another possible implementation, the transceiver unit 802 is configured to send deployment result information to the second network management node, where the deployment result information includes deployment success or deployment failure.

[0209] Optionally, the management device 800 may be a second network management node or a chip in the second network management node. The specific units are as follows:

[0210] The transceiver unit 802 is used to receive artificial intelligence AI / machine learning ML capability information from the first network management node, where the AI / ML capability information includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML.

[0211] In one possible implementation, the general capabilities of the AI / ML include one or more of the following: AI / ML training capability, AI / ML testing capability, AI / ML reasoning capability, or AI / ML simulation capability; the learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; the trusted capabilities of the AI / ML include one or more of the following: explainability capability, robustness capability, or fairness capability; the processing capabilities of the AI / ML include one or more of the following: AI / ML training processing capability, AI / ML testing processing capability, AI / ML reasoning processing capability, or AI / ML simulation processing capability.

[0212] In another possible implementation, the training capability of the AI / ML includes a joint training capability, which includes the ability to jointly train models; the testing capability of the AI / ML includes a joint testing capability, which includes the ability to jointly test models; the reasoning capability of the AI / ML includes an analysis capability and a decision-making capability, which includes a management data analysis capability and / or a network data analysis capability; the decision-making capability includes a self-optimization function SON optimization capability and / or a radio access network intelligence RANintellgence optimization capability; the simulation capability of the AI / ML includes support for a simulation environment.

[0213] In another possible implementation, the explainability capability includes one or more of the following: explainability capability of training, explainability capability of testing, explainability capability of reasoning, or explainability capability of simulation; the robustness capability includes one or more of the following: robustness capability of training, robustness capability of testing, robustness capability of reasoning, or robustness capability of simulation; the fairness capability includes one or more of the following: fairness capability of training, fairness capability of testing, fairness capability of reasoning, or fairness capability of simulation.

[0214] In another possible implementation, the transceiver unit 802 is configured to send a request message to the first network management node, where the request message is used to request capability information of the AI / ML.

[0215] In another possible implementation, the transceiver unit 802 is configured to receive updated AI / ML capability information from the first network management node.

[0216] In another possible implementation, the transceiver unit 802 is used to send first indication information to the first network management node, where the first indication information is used to indicate a target learning capability, where the target learning capability is one of the learning capabilities of the AI / ML, and the first indication information is used for model training.

[0217] In another possible implementation, the transceiver unit 802 is used to send second indication information to the first network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML, and the target trusted capability is used for trusted assessment.

[0218] In another possible implementation, the transceiver unit 802 is used to send a third indication message to the first network management node, where the second indication message is used to indicate a key performance indicator KPI target or performance measurement target that the model needs to achieve, and the second indication message is used for model reasoning.

[0219] In another possible implementation, the transceiver unit 802 is used to send fourth indication information to the first network management node, and the fourth indication information includes a correspondence between a business scenario identifier and a threshold, and the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; the fourth indication information is used for model deployment.

[0220] In another possible implementation, the transceiver unit 802 is configured to receive deployment result information from the first network management node, where the deployment result information includes deployment success or deployment failure.

[0221] It should be noted that the implementation and beneficial effects of each unit may also correspond to the corresponding description of the method embodiment shown in FIG. 6 or FIG. 7 .

[0222] Please refer to Figure 9, which is a schematic diagram of the structure of another management device 900 provided in an embodiment of the present application. The management device 900 includes at least one processor 901 and a communication interface 903, and optionally also includes a memory 902. The processor 901, memory 902, and communication interface 903 are interconnected via a bus 904. Optionally, the processor 901 and memory 902 can be integrated together.

[0223] The memory 902 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM). The memory 902 is used for storing computer programs and data. The communication interface 903 is used to receive and send data.

[0224] The processor 901 may be one or more central processing units (CPUs). In the case where the processor 901 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

[0225] The processor 901 in the management device 900 is used to read the computer program or instructions stored in the memory 902 to implement the functions of the above-mentioned processing unit, and the communication interface 903 in the management device 900 is used to implement the functions of the above-mentioned transceiver unit.

[0226] An embodiment of the present application also provides a chip device, which includes at least one processor, and the at least one processor is used to call a computer program or instruction stored in a memory so that the processor executes the method provided in the embodiment shown in Figure 6 or Figure 7 above.

[0227] In one possible implementation, the input of the chip device corresponds to the receiving operation in any one of the embodiments shown in FIG. 6 or FIG. 7 , and the output of the chip device corresponds to the sending operation in any one of the embodiments shown in FIG. 6 or FIG. 7 .

[0228] Optionally, the processor is coupled to the memory via an interface.

[0229] Optionally, the chip device further includes a memory, in which computer program instructions are stored.

[0230] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program or instruction. When the computer program or instruction runs on a processor, the method executed by the first network management node or the second network management node in the above method embodiment is implemented.

[0231] An embodiment of the present application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed on a processor, the method performed by the first network management node or the second network management node in the above method embodiment is implemented.

[0232] The present application also provides a communication system including the first network management node and the second network management node described in the above embodiment. The first network management node is configured to execute some or all of the operations performed by the first network management node in the above method embodiment, and the second network management node is configured to execute some or all of the operations performed by the second network management node in the above method embodiment.

[0233] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0234] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk, a mobile hard disk, a CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a base station or a terminal. Of course, the processor and the storage medium can also exist in a base station or a terminal as discrete components.

[0235] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are performed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable device. The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video disk; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

[0236] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0237] In the description of this application, words such as "first", "second", "S601", or "S602" are only used to distinguish the description and facilitate the context. Different sequence numbers themselves do not have specific technical meanings and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying the order of execution of operations. The execution order of each process should be determined by its function and internal logic.

[0238] In this application, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. A and B can be singular or plural. Additionally, the character " / " in this document indicates that the related objects are in an "or" relationship.

[0239] In this application, "transmission" may include the following three situations: sending of data, receiving of data, or sending of data and receiving of data. In this application, "data" may include business data and / or signaling data.

[0240] In this application, the terms "comprise" or "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process / method comprising a series of steps, or a system / product / apparatus comprising a series of units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes / methods / products / apparatus.

[0241] In the description of this application, unless otherwise specified, the number of nouns refers to "singular or plural," that is, "one or more." "At least one" means one or more. "Including at least one of the following: A, B, C" means that it may include A, or include B, or include C, or include A and B, or include A and C, or include B and C, or include A, B, and C. A, B, and C can be single or plural.

Claims

1. A management method, characterized in that: include: The first network management node determines capability information of the artificial intelligence AI / machine learning ML, where the capability information of the AI / ML includes one or more of the following: general capability of the AI / ML, learning capability of the AI / ML, trust capability of the AI / ML, or processing capability of the AI / ML; The first network management node sends the AI / ML capability information to the second network management node.

2. The method according to claim 1, characterized in that The general capabilities of AI / ML include one or more of the following: training capabilities of AI / ML, testing capabilities of AI / ML, reasoning capabilities of AI / ML, or simulation capabilities of AI / ML; The learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; The trustworthy capabilities of the AI / ML include one or more of the following: explainability, robustness, or fairness; The processing capability of the AI / ML includes one or more of the following: the training processing capability of the AI / ML, the testing processing capability of the AI / ML, the reasoning processing capability of the AI / ML, or the simulation processing capability of the AI / ML.

3. The method according to claim 2, characterized in that The training capability of the AI / ML includes a joint training capability, and the joint training capability includes a capability of joint training between models; The AI / ML testing capability includes a joint testing capability, and the joint testing capability includes a capability for joint testing between models; The reasoning capability of the AI / ML includes analysis capability and decision-making capability. The analysis capability includes management data analysis capability and / or network data analysis capability. The decision-making capability includes self-optimization function SON optimization capability and / or radio access network intelligence RANintellgence optimization capability. The simulation capability of the AI / ML includes support for a simulation environment.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The first network management node receives a request message from the second network management node, where the request message is used to request capability information of the AI / ML.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: The first network management node sends updated AI / ML capability information to the second network management node.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The first network management node receives first indication information from the second network management node, where the first indication information is used to indicate a target learning capability, where the target learning capability is one of the learning capabilities of the AI / ML; The first network management node performs model training according to the first indication information to obtain a training result.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: The first network management node receives second indication information from the second network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML; The first network management node performs a trustworthy evaluation of an indicator of the target trustworthy capability to obtain a trustworthy evaluation result.

8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: The first network management node receives third indication information from the second network management node, where the third indication information is used to indicate a key performance indicator KPI target or a performance measurement target that the model needs to achieve; The first network management node performs model reasoning according to the third indication information and the reasoning capability of the AI / ML.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: The first network management node receives fourth indication information from the second network management node, where the fourth indication information includes a correspondence between a service scenario identifier and a threshold, where the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; The first network management node deploys a model according to the fourth indication information and the processing capability of the AI / ML.

10. A management method, characterized in that: include: The second network management node receives artificial intelligence AI / machine learning ML capability information from the first network management node, where the AI / ML capability information includes one or more of the following: general capabilities of AI / ML, learning capabilities of AI / ML, trusted capabilities of AI / ML, or processing capabilities of AI / ML.

11. The method according to claim 10, characterized in that The general capabilities of AI / ML include one or more of the following: training capabilities of AI / ML, testing capabilities of AI / ML, reasoning capabilities of AI / ML, or simulation capabilities of AI / ML; The learning capabilities of the AI / ML include one or more of the following: federated learning, transfer learning, generative learning, or reinforcement learning; The trustworthy capabilities of the AI / ML include one or more of the following: explainability, robustness, or fairness; The processing capability of the AI / ML includes one or more of the following: the training processing capability of the AI / ML, the testing processing capability of the AI / ML, the reasoning processing capability of the AI / ML, or the simulation processing capability of the AI / ML.

12. The method according to claim 10 or 11, characterized in that: The training capability of the AI / ML includes a joint training capability, and the joint training capability includes a capability of joint training between models; The AI / ML testing capability includes a joint testing capability, and the joint testing capability includes a capability for joint testing between models; The reasoning capability of the AI / ML includes analysis capability and decision-making capability. The analysis capability includes management data analysis capability and / or network data analysis capability. The decision-making capability includes self-optimization function SON optimization capability and / or radio access network intelligence RANintellgence optimization capability. The simulation capability of the AI / ML includes support for a simulation environment.

13. The method according to any one of claims 10 to 12, characterized in that: The method further comprises: The second network management node sends a request message to the first network management node, where the request message is used to request capability information of the AI / ML.

14. The method according to any one of claims 10 to 13, characterized in that: The method further comprises: The second network management node receives updated AI / ML capability information from the first network management node.

15. The method according to any one of claims 10 to 14, characterized in that: The method further comprises: The second network management node sends first indication information to the first network management node, where the first indication information is used to indicate a target learning capability, where the target learning capability is one of the learning capabilities of the AI / ML, and the first indication information is used for model training.

16. The method according to any one of claims 10 to 15, characterized in that: The method further comprises: The second network management node sends second indication information to the first network management node, where the second indication information is used to indicate an indicator of a target trusted capability, where the target trusted capability is one of the trusted capabilities of the AI / ML, and the target trusted capability is used for trusted assessment.

17. The method according to any one of claims 10 to 16, characterized in that: The method further comprises: The second network management node sends third indication information to the first network management node, where the third indication information is used to indicate a key performance indicator KPI target or a performance measurement target that the model needs to achieve, and the second indication information is used for model reasoning.

18. The method according to any one of claims 10 to 17, characterized in that: The method further comprises: The second network management node sends fourth indication information to the first network management node, and the fourth indication information includes the correspondence between the business scenario identifier and the threshold, and the threshold includes one or more of the following: a delay sensitivity threshold, an execution time threshold, or a threshold of available dedicated resources; the fourth indication information is used for model deployment.

19. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, which, when executed on a processor, implements the method according to any one of claims 1 to 9.

20. A computer program product, characterized in that The computer program product includes a computer program or instructions, and when the computer program or instructions are executed on a computer, the method according to any one of claims 1 to 9 is implemented.

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