Communication method, apparatus, storage medium, and program product

Abstract

This application provides a communication method, apparatus, storage medium, and program product applied to a first application entity. The method includes: the first application entity sending a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by a second application entity to determine a model and / or algorithm matching the task. The first application entity sends the task assistance information to the second application entity, and the second application entity can determine the model and / or algorithm matching the task based on the task assistance information. This can effectively improve the processing capability and accuracy of the generated scheme of the second application entity (such as a target agent) for the current task.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method, apparatus, storage medium, and program product. Background Technology

[0002] Future communication systems need to support new business scenarios such as the integration of artificial intelligence (AI) with communication and the fusion of sensing and communication, including smart cities, digital healthcare, and smart factories. Different business scenarios have different performance requirements; therefore, communication systems need strong customization capabilities to integrate end-to-end network functions, application functions, and communication, computing, and data resources to build end-to-end application networks for service targets (tenants / users / applications, etc.). An application network is a logical network composed of a series of network functions, application functions, and communication, computing, and data resources. The future number of application networks will be large, and each application network involves the flexible assembly of multiple functions and multi-dimensional resources, with complex parameter configurations. This presents significant challenges to the design and management of application networks. Traditional predefined processes based on human expert experience are insufficient to address this problem.

[0003] AI-based agents, with their powerful intent understanding, reasoning abilities, and capacity for interaction and self-evolution, are considered an effective way to solve the aforementioned problems. Since it's typically difficult for a single agent to perform all the work, future core networks will likely employ multiple agents. How these agents choose the appropriate model / algorithm when collaborating to complete tasks is a problem that needs to be solved. Summary of the Invention

[0004] This application discloses a communication method, device, storage medium, and program product that can improve the agent's ability to select a suitable model / algorithm.

[0005] In a first aspect, embodiments of this application provide a communication method. This method is applied to a first application entity. The method may include: the first application entity sending a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by a second application entity to determine a model and / or algorithm matching the task.

[0006] In this embodiment of the application, the first application entity sends task assistance information to the second application entity so that the second application entity can determine the model and / or algorithm that matches the task based on the task assistance information. This can effectively improve the target application entity's ability to process the current task and the accuracy of the generated scheme.

[0007] An application entity can be an agent instance. Alternatively, an application entity can also be an instance generated based on a model / algorithm application framework, which can be a large model or a small model, etc.

[0008] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a model and / or algorithm matching threshold with the task.

[0009] This can help the second application entity select a matching model and / or algorithm.

[0010] In one possible implementation, the first application entity also receives model and / or algorithm selection information in response to the first task assignment request.

[0011] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

[0012] In one possible implementation, the first application entity also obtains task-related auxiliary information from a knowledge base based on the task.

[0013] In one possible implementation, the knowledge base is either a local knowledge base or a third-party knowledge base.

[0014] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0015] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0016] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0017] Secondly, embodiments of this application provide a communication method. This method is applied to a second application entity. The method may include: the second application entity receiving a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by the second application entity to determine a model and / or algorithm matching the task; and the second application entity sending model and / or algorithm selection information based on the task assistance information in response to the first task allocation request.

[0018] In this embodiment of the application, the second application entity can determine the model and / or algorithm that matches the task based on the task auxiliary information, which can effectively improve the target application entity's ability to process the current task and the accuracy of the generated scheme.

[0019] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information. The second application entity further determines the target model and / or algorithm based on this task-aided information.

[0020] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a model and / or algorithm matching threshold with the task.

[0021] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0022] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0023] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0024] In one possible implementation, the second application entity also sends a second task allocation request to the third application entity, wherein the third application entity and the second application entity are application entities from different domains.

[0025] In one possible implementation, the third application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0026] In one possible implementation, the second application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0027] In one possible implementation, the third application entity and the second application entity are application entities in the same layer, such as the business layer, service layer, or resource layer.

[0028] Thirdly, this application provides a communication device that has the functions of the first aspect above. For example, the communication device includes modules, units, or means that perform the operations involved in the first aspect above. These modules, units, or means can be implemented by software, hardware, or a combination of software and hardware.

[0029] In one implementation, the communication device includes: a communication module for sending a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by a second application entity to determine a model and / or algorithm matching the task.

[0030] In one possible implementation, the communication module is also configured to receive model and / or algorithm selection information in response to the first task assignment request.

[0031] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a matching threshold between the model and / or algorithm and the task.

[0032] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

[0033] In one possible implementation, a processing module is also included, which is used to obtain task-related auxiliary information from the knowledge base based on the task.

[0034] In one possible implementation, the knowledge base is either a local knowledge base or a third-party knowledge base.

[0035] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0036] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0037] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0038] Fourthly, this application also provides a communication device that has the functions of the second aspect above. For example, the communication device includes modules, units, or means that perform the operations involved in the second aspect above. These modules, units, or means can be implemented by software, hardware, or a combination of software and hardware.

[0039] In one implementation, the communication device includes: a communication module for receiving a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by the second application entity to determine a model and / or algorithm matching the task;

[0040] The communication module is also used to send model and / or algorithm selection information based on the task auxiliary information in response to the first task assignment request.

[0041] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

[0042] In one possible implementation, a processing module is also included for determining the target model and / or algorithm based on the task assistance information.

[0043] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a matching threshold between the model and / or algorithm and the task.

[0044] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0045] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0046] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0047] In one possible implementation, the communication module is further configured to send a second task allocation request to a third application entity, wherein the third application entity and the second application entity are application entities from different domains.

[0048] In one possible implementation, the third application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0049] In one possible implementation, the second application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0050] In one possible implementation, the third application entity and the second application entity are application entities in the same layer, such as the business layer, service layer, or resource layer.

[0051] Fifthly, this application provides a communication device including a processor, which is configured to execute a computer program or computer-executable instructions stored in a memory, and / or cause the device to perform a method provided in any of the possible embodiments of the first to second aspects via logic circuitry.

[0052] One possible implementation also includes memory. Alternatively, the memory and processor can be integrated together.

[0053] One possible implementation also includes an interface circuit.

[0054] In one possible implementation, the device is a chip or chip system.

[0055] In a sixth aspect, this application provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the method provided in any of the possible embodiments of the first to second aspects.

[0056] In a seventh aspect, this application provides a computer program product that, when run on a computer, causes the computer to perform the method provided in any of the possible implementations of the first to second aspects.

[0057] It is understood that the apparatus described in the third aspect, the apparatus described in the fourth aspect, the apparatus described in the fifth aspect, the computer storage medium described in the sixth aspect, or the computer program product described in the seventh aspect are all used to perform the method provided in any of the first or second aspects. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here. Attached Figure Description

[0058] The accompanying drawings used in the embodiments of this application are described below.

[0059] Figure 1 This is a schematic diagram of a communication system provided in an embodiment of this application;

[0060] Figures 2a-3 This is a schematic structural diagram of the system architecture applicable to the embodiments of this application;

[0061] Figure 4 This is a flowchart illustrating a communication method provided in an embodiment of this application;

[0062] Figure 5 This is a schematic diagram of another communication method provided in an embodiment of this application;

[0063] Figure 6 This is a schematic diagram of yet another communication method provided in an embodiment of this application;

[0064] Figure 7 This is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0065] Figure 8 This is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0066] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0067] The technical solution of this application can be applied to various network function virtualization (NFV) systems. This system can use standard formal language to describe current operator service technology solutions, network construction solutions, and network operation and maintenance methods as patterns and strategies, and implement the technical solutions and construction solutions based on these patterns and strategies. For example, the technical solution of this application can be applied to one or more of the following systems: wireless intent-driven network (wIDN) systems, experiential networked intelligence (ENI) systems, intent-driven management service (IDMS) systems, or open network automation platform (ONAP) systems, etc.

[0068] To facilitate understanding of the embodiments of this application, some terms involved in the embodiments of this application will be explained first.

[0069] 1. Network management and network management services

[0070] Network management refers to the management of network resources, including but not limited to monitoring, controlling, and recording the performance and usage of network resources, and issuing management action groups to network resources (such as devices in the network) based on the detected network conditions to ensure the effective operation of the network. For example, network management can include at least one of the following: monitoring, testing, configuring, analyzing, evaluating, or controlling network resources. Network management can also include timely reporting and handling of network failures, and coordinating and maintaining the efficient operation of the network system. Network resources are the objects of network management; they can also be called network objects or managed entities. For example, network resources can be base station equipment, routers, switches, core network equipment, etc.

[0071] Network management service: refers to a service that provides network management functions. The entity that produces this service usually provides network management functions to the entity that consumes the service through a network interface (e.g., a service-based interface).

[0072] 2. Large Model

[0073] Large models are used to provide large-scale model services. A large model refers to a neural network model containing an extremely large number of parameters. Large models can also be called foundation models. Large models play a crucial role in many fields and applications, with common applications including natural language processing, computer vision, speech recognition and synthesis, recommender systems, financial risk control, intelligent dialogue systems, games, artificial intelligence (AI), and healthcare. In the future, large models will evolve towards multimodal models, which can handle data from multiple modalities; for example, multimodal large models can process natural language as well as text, images, or videos.

[0074] Large language models (LLMs) are a type of large model that is a deep learning model trained on large amounts of text data. LLMs can generate natural language text or understand the meaning of language text. LLMs can handle various natural language tasks, such as text classification, question answering, and dialogue.

[0075] 3. Large-scale model application framework

[0076] The Large Model Application Framework is a framework for developing large model applications. Based on components such as model, prompt, and memory, it provides capabilities such as prompt templates, model orchestration, large model services, and security isolation, helping developers achieve a simple, secure, and reliable large model application building experience.

[0077] 4. Framework and application paradigms of AI agents

[0078] An AI agent can be an instance of an intelligent agent in a communication device. In this application, the AI ​​agent instance can be generated by a large model application framework based on a large model.

[0079] An AI agent framework can include the following key components:

[0080] 1) Brain: Based on a large model, it provides memory and decision-making abilities;

[0081] 2) Perception: Responsible for receiving external input, perceiving information from the environment, and providing it to the brain;

[0082] 3) Action: Responsible for executing the action instructions given by the brain, including generating text, calling tools, and controlling embodied devices. Embodied devices typically refer to devices with physical form and sensory capabilities, enabling them to communicate and interact naturally with humans.

[0083] Application paradigms for AI agents can include:

[0084] 1) Machine-attended, i.e., single agent, can handle simple tasks;

[0085] 2) Human-machine co-driving, also known as agent-human interaction, can handle complex tasks;

[0086] 3) Swarm intelligence, namely agent-agent interaction, can handle cross-team collaborative tasks.

[0087] In the embodiments of this application, the AI ​​agent can also be simply referred to as agent.

[0088] 5. Interaction and collaboration between AI agents

[0089] In a multi-agent architecture based on AI agents, there is interaction and collaboration between AI agents. The following example, using a wireless base station outage troubleshooting process, provides a simple description of the interaction and collaboration between AI agents.

[0090] Wireless base station outages involve multiple domains, such as the wireless domain, core network domain (e.g., cloud core network), data communication domain, optical domain, and environmental domain. The network management system for the wireless domain and / or core network domain can be a mobile bandwidth automation engine (MAE), while the network management system for the data communication and / or optical domain can be a network cloud engine (NCE). Through the autonomous collaboration between the MAE agent (i.e., the wireless agent) and the NCE agent (i.e., the transmission agent), the location and repair of base station outages can be completed. For example, if a batch of base station outage alarms occur in a certain district of a city, maintenance personnel can issue a task, requesting the MAE agent and NCE agent to assist in troubleshooting and resolving the issue. Upon receiving the task, the MAE agent checks the environmental monitoring system and, through routine troubleshooting, identifies a list of base stations with transmission failures. The MAE agent can then provide this list to the NCE agent for collaborative troubleshooting. After receiving the list, the NCE agent can perform a site-by-site investigation of each base station, generating work orders for each faulty base station to repair. If base stations without transmission failures are identified, the NCE agent can provide a list of these base stations to the MAE agent for detailed investigation. The MAE agent can then perform a detailed investigation of these base stations, generating work orders for each faulty base station and conducting on-site investigations of the non-transmission failure base stations. After repairing the transmission failure base stations, the NCE agent can notify the MAE agent that all transmission failure base stations have been repaired, and the MAE agent confirms the repair result. Once the agent confirms that the base station with the transmission failure has been repaired, and all base stations without the transmission failure have also been repaired, the process of locating and repairing the base station outage failure ends.

[0091] It should be noted that the aforementioned "MAE agent" or "NCE agent" can refer to an agent within the MAE or NCE that possesses the function or capability of troubleshooting wireless base station faults. The MAE or NCE may also have agents with other functions or capabilities; that is, different agents can be set up within the MAE or NCE for different functions or capabilities. Alternatively, the aforementioned "MAE agent" or "NCE agent" can also refer to an agent representing the MAE or NCE. This agent, in addition to possessing the function or capability of troubleshooting wireless base station faults, may also possess other functions or capabilities. In other words, the MAE or NCE has only one agent representing itself, and this agent may possess one or more functions or capabilities.

[0092] 6. Application Entities

[0093] An application entity can be an agent instance. Alternatively, an application entity can also be an instance generated based on a model / algorithm application framework, which can be a large model or a small model, etc.

[0094] The relevant terms used in the embodiments of this application have been described above. The following is a description of the terms used in conjunction with the embodiments of this application. Figures 1 to 2b The system architecture applicable to the embodiments of this application is described.

[0095] Figure 1 A possible, non-limiting system schematic diagram is shown. For example... Figure 1 As shown, the communication system 1000 includes a radio access network (RAN) 100 and a core network (CN) 200. Optionally, it also includes an Internet 300. The RAN 100 includes at least one RAN node (e.g., Figure 1 110a and 110b (collectively referred to as 110) and at least one terminal (such as Figure 1 RAN100, denoted as RAN100, comprises RAN nodes 120a-120j, collectively referred to as RAN120. RAN100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment. Figure 1 (Not shown in the image). Terminal 120 is connected to RAN node 110 wirelessly. RAN node 110 is connected to core network 200 wirelessly or via wired connection. The core network equipment in core network 200 and RAN node 110 in RAN 100 can be different physical devices, or they can be the same physical device integrating core network logical functions and radio access network logical functions.

[0096] RAN 100 can be a cellular system related to the 3rd Generation Partnership Project (3GPP), such as 4G, 5G mobile communication systems, or evolutionary systems beyond 5G (e.g., 6th generation, 6G mobile communication systems). RAN 100 can also be an open radio access network (O-RAN or ORAN), a cloud radio access network (CRAN), or a wireless-fidelity (Wi-Fi) system based on the IEEE 802.11 standard. RAN 100 can also be a communication system that integrates two or more of the above systems.

[0097] RAN node 110, sometimes also referred to as access network equipment, RAN entity, or access node, constitutes part of the communication system and assists terminals in achieving wireless access. Multiple RAN nodes 110 in the communication system 1000 can be of the same type or different types. In some scenarios, the roles of RAN node 110 and terminal 120 are relative, for example... Figure 1 Network element 120i can be a helicopter or a drone, and it can be configured as a mobile base station. For terminals 120j that access RAN 100 through network element 120i, network element 120i is a base station; however, for base station 110a, network element 120i is a terminal. RAN node 110 and terminal 120 are sometimes referred to as communication devices, for example... Figure 1 Network elements 110a and 110b can be understood as communication devices with base station functions, while network elements 120a-120j can be understood as communication devices with terminal functions.

[0098] In one possible scenario, a RAN node can be a base station, an evolved NodeB (eNodeB), an access point (AP), a transmission reception point (TRP), a next-generation NodeB (gNB), a next-generation base station in a 6G mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. A RAN node can also be a macro base station (such as...) Figure 1 110a), micro base stations or indoor stations (such as Figure 1 In CRAN scenarios, RAN nodes can be 110b), relay nodes or donor nodes, or wireless controllers. Optionally, RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, in vehicle-to-everything (V2X) technology, the access network equipment can be a roadside unit (RSU).

[0099] In another possible scenario, multiple RAN nodes assist the terminal in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be central units (CUs), distributed units (DUs), CU-control plane (CPs), CU-user plane (UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0100] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules.

[0101] A terminal can also be called a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR) devices, augmented reality (AR) devices, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, light UEs, reduced-capability UEs (REDCAP UEs), point-of-sale (POS) machines, customer-premises equipment (CPE), etc. The terminal can also be a vehicle device, such as a complete vehicle device, an in-vehicle module, an in-vehicle chip, an on-board unit (OBU), or a telematics box (T-BOX). The embodiments of this application do not limit the device form of the terminal.

[0102] Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0103] For the network elements in the ORAN system and their corresponding protocol layer functions, please refer to Table 1:

[0104] Table 1

[0105] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PDCP-C O-CU-UP SDAP+PDCP-U O-DU RLC+MAC+PHY-high O-RU PHY-low

[0106] Base stations and terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the base stations and terminals.

[0107] The roles of base stations and terminals can be relative, for example, Figure 1 The helicopter or drone 120i can be configured as a mobile base station. For terminals 120j accessing the wireless access network 100 via 120i, terminal 120i is a base station; however, for base station 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Of course, 110a and 120i can also communicate via a base station-to-base station interface protocol; in this case, 120i is also a base station relative to 110a. Therefore, both base stations and terminals can be collectively referred to as communication devices. Figure 1 The 110a and 110b in the text can be referred to as communication devices with base station functions. Figure 1 The 120a-120j in the text can be referred to as communication devices with terminal functions.

[0108] In this embodiment, the base station is also referred to as an access network device. The apparatus used to implement the functions of the access network device can be the access network device itself; it can also be any apparatus capable of supporting the access network device in implementing these functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the access network device or used in conjunction with the access network device. In this embodiment, the example of an access network device being used to implement the functions of the access network device is used only and does not constitute a limitation on the solutions of this embodiment.

[0109] It is understood that this application can be applied between access network equipment and terminals.

[0110] It should be understood that Figure 1 The number and type of devices in the communication system shown are for illustrative purposes only. This application is not limited to this. In actual applications, the communication system may include more terminals, more access network devices, and other network elements, such as core network devices and / or network elements used to implement artificial intelligence functions.

[0111] It is understood that all or part of the functions implemented by one or more of the terminals, access network devices, core network devices, or network elements used to implement artificial intelligence functions can be virtualized, that is, implemented through one or more of dedicated or general-purpose processors and corresponding software modules. Among these, the transmit and receive functions of the terminals and access network devices, which involve air interface transmission, can be implemented in hardware. Core network devices, such as operation administration and maintenance (OAM) network elements, can also be virtualized. Optionally, one or more of the functions of the virtualized terminals, access network devices, core network devices, or network elements used to implement artificial intelligence functions can be implemented by cloud devices, such as cloud devices in over-the-top (OTT) systems. It is understood that, for example, the agent described below can be deployed on the aforementioned network elements used to implement artificial intelligence functions, or the agent described below can be applied to the aforementioned terminals, access network devices, core network devices, etc.

[0112] like Figure 2a The diagram shown is a schematic structural diagram of a system architecture applicable to embodiments of this application. The communication system includes a management service producer (MnS producer) and a management service consumer (MnS consumer). The management service consumer initiates the interaction messages between large model agents. The management service producer, as the receiver of the interaction messages between large model agents, performs corresponding operations (e.g., updating agent capability information) based on the received requests. The aforementioned management service producer and / or management service consumer can be configured in a network management system (NMS), equipment management system (EMS), or network element (NE), such as a management function / entity / management entity / network device / network element, etc., and this solution does not limit this.

[0113] For example, refer to Figure 2bThe system architecture shown includes multiple network management service production entities (such as network management service production entities 210 and 220). The entity providing network management services is called a network management service production entity. A network management service production entity may also be called a network management service provider, network management provider, management service provider (MnS provider), intent provider, intent handler, network management service production network element, intent management service provider, intent service provider, or network management service producer (MnSproducer), etc. In future communication systems, network management service production entities may have other names, which are not specifically limited in this application. For ease of description, the network management service production entity will be simply referred to as a production entity below.

[0114] The capabilities or functions of a production entity can be deployed on a network element, which is called a network management service production network element. The capabilities or functions of a production entity can also be deployed on other devices; this application does not limit this. A production entity can be a management function, a management function entity, a management entity, a network device, or a network element, etc., and this application does not limit this. For ease of description, this application uses a production entity as an example, but all can be replaced with other devices that have deployed the capabilities or functions of a production entity.

[0115] A production entity may include a large model application framework. This framework can be a large model application functional module within the production entity, serving as a module for using large model instances and providing functions such as agent instance generation and local agent configuration information storage. Agent instances generated by the production entity's large model application framework based on the large model can run within the production entity. Agent instances within the production entity can interact with agent instances in production entities of other domains to achieve business objectives. In this application, an agent instance can also be simply referred to as an agent. As the initiator and receiver of messages exchanged between agents, the production entity can perform corresponding operations based on the received messages, such as sending or updating agent capability information as described below. It should be noted that the above describes the agent instances contained within the production entity. It is understood that the aforementioned production entity may also include instances generated based on the model / algorithm application framework, etc.

[0116] It is understandable that consumer entities (such as management service consumers) may also include the aforementioned large model application framework, as well as model / algorithm application frameworks, etc. This solution does not impose any restrictions on this.

[0117] The aforementioned entities can be network components within hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). It is understood that these entities can be implemented by a single device or by multiple devices working together. Furthermore, these entities can also be functional modules within a system, such as a network management system (NMS) or equipment management system (EMS), or functional modules within a device, such as one or more functional modules within network equipment (NE), which can be access network equipment or core network equipment, etc. As an example, production entities can be deployed within different EMSs. As another example, production entities can be deployed within different NEs.

[0118] The embodiments of this application can be applied to message interaction between agents in different domains.

[0119] The following is combined Figure 3 This paper introduces the architecture using the example of a Network Management System (NMS) as the Managed Service Consumer (MnS consumer) and an Equipment Management System (EMS) as the Managed Service Producer. Figure 3 As shown, the implementation of this architecture may include the following processes (1)-(4), as detailed below:

[0120] (1) The agent instance (agent_NMS) in NMS initiates a "base station outage fault handling" task. The message type can be, for example, "task allocation," requesting the EMS's coordinating agent (agent_coordinate) to generate a workflow. Simultaneously, the agent message (AgentMessage) in the request message provides task assistance information to help the coordinating agent select a suitable model / algorithm instance. This task assistance information can come from a knowledge base, such as a local knowledge base or an independent, general-purpose knowledge base, such as a third-party knowledge base.

[0121] (2) The collaborative agent verifies the task execution effect of the target instance in the model / algorithm list based on the task auxiliary information, and selects and sets the appropriate model / algorithm.

[0122] (3) The collaborating agent generates a workflow based on the capability information of the local agents, and sends tasks (such as AgentMessage) to other agent instances based on the workflow. For example, the other agent instance is agent2.

[0123] (4) The collaborative agent sends a response message to the agent instance (agentNMS) in the NMS, returning the results after model / algorithm selection verification. This result may include information such as matching degree and matching result.

[0124] Among them, the large model application framework is the internal large model application function module of MnS producer. As the module for using large model instances, it provides the functions of generating intelligent agent instances and storing agent local configuration information.

[0125] The following section introduces the definition of the information model (including AgentEntity and AgentMessage) related to AgentMessage.

[0126] Enhanced AgentMessage model: Add information to the agent message to assist the target agent in selecting large model / algorithm instances; add a list of model / algorithm instances to the agent entity model.

[0127] (1) Enhanced Agent Entity Structure: Added information on the list of model / algorithm instances.

[0128] It includes a model list: a list of model information that the agent can use;

[0129] It also includes an algorithm list: a list of algorithms that the agent can use.

[0130] By adding information to the list of model / algorithm instances, the agent entity structure can be enhanced.

[0131] The list of examples of the above models / algorithms can be found in Table 2.

[0132] Table 2

[0133] Serial Number Attributename Supportqualifier Isreadable Iswritable Isinvariant Isnotifyable 1 modelList CM T T F T 2 algorithmList CM T T F T

[0134] (2) Enhanced AgentMessage Structure

[0135] For example, it includes needTaskSupport: adding an auxiliary setting mechanism for "agent mounting model / algorithm" in the task delivery messages for interaction between agents (providing auxiliary setting switches and a list of auxiliary information);

[0136] It also includes taskSupportKnowledge: which can be retrieved from the knowledge base based on the task scenario and used to store information to assist the target agent in selecting model / algorithm instances.

[0137] For example, see Table 3.

[0138] Table 3

[0139] Serial Number Attributename Supportqualifier Isreadable Iswritable Isinvariant Isnotifyable 1 needTaskSupport CM T T T T 2 taskSupportKnowledge CM T T T T

[0140] (3) TaskSupportKnowledge Structure Definition

[0141] Provided by the task message initiator agent, after needTaskSupport is set to True:

[0142] inputExamTask: Stores reference task test case information;

[0143] outputExpected: Stores the expected output information;

[0144] matchingDegree: Indicates the degree of matching, assisting the target agent in selecting a suitable large model / algorithm instance (e.g., selecting a suitable model or algorithm for the task based on actual internal execution accuracy) and completing the configuration refresh.

[0145] For example, see Table 4.

[0146] Table 4

[0147] Serial Number Attributename Supportqualifier Isreadable Iswritable Isinvariant Isnotifyable 1 inputExamTask M T T T T 2 outputExpected M T T T T 3 matchingDegree M T T T T

[0148] (4) Response: After receiving the matching and selection of the background model / algorithm from the agent, the response returns the selection result to the message initiating agent:

[0149] Match Status: Indicates the matching status of the model / algorithm instance, such as matched or unmatched;

[0150] MatchingDegree: The degree of match between the selected instances;

[0151] MatchedInfo: Information about the matched model / algorithm instance.

[0152] For example, see Table 5.

[0153] Table 5

[0154] Serial Number Attributename Supportqualifier Isreadable Iswritable Isinvariant Isnotifyable 1 matchStatus M T T T T 2 matchingDegree M T T T T 3 matchedInfo M T T F T

[0155] The external interface is described below.

[0156] Define the large model registration / discovery service interface: Based on the service-oriented interface defined in the 3GPP standard, extend and adapt intelligent agent message sending. Table 6 below is a comparison table between general services and large model related information models.

[0157] Table 6

[0158]

[0159] The architecture of the embodiments of this application has been described above. The methods of the embodiments of this application will be described in detail below.

[0160] Reference Figure 4 The diagram shown is a flowchart illustrating a communication method provided in an embodiment of this application. Optionally, this method can be applied to the aforementioned communication system, for example... Figure 1 The communication system shown. (As shown) Figure 4 The communication method shown may include steps 401-402. Steps 401-402 are as follows:

[0161] 401. The first application entity sends a first task allocation request to the second application entity. The first task allocation request includes task assistance information, which is used by the second application entity to determine a model and / or algorithm that matches the task. Accordingly, the second application entity receives the first task allocation request.

[0162] The first application entity can be the aforementioned agent instance. Alternatively, the first application entity can be an instance generated based on a model / algorithm application framework, where the model can be a large model, a small model, etc. The second application entity can be the aforementioned agent instance. Alternatively, the second application entity can be an instance generated based on a model / algorithm application framework, etc.

[0163] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a model and / or algorithm matching threshold for the task. The reference task use case information is used by the second application entity to determine the model and / or algorithm that matches the task. The model and / or algorithm matching threshold for the task can be, for example, 80%, 90%, etc. That is, the second application entity determines a suitable model and / or algorithm based on one or more of the reference task use case information, expected output information, and the model and / or algorithm matching threshold for the task.

[0164] In one possible implementation, the first application entity retrieves task-related auxiliary information from a knowledge base based on the task. Optionally, the knowledge base can be a local knowledge base, or it can be a third-party knowledge base, etc.

[0165] 402. The second application entity sends model and / or algorithm selection information based on the task assistance information in response to the first task allocation request. Accordingly, the first application entity receives the model and / or algorithm selection information.

[0166] The model and / or algorithm selection information may include one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information. For example, the model and / or algorithm matching status information may include "matched" or "not matched." The target model and / or algorithm information may be, for example, relevant information about the target model and / or algorithm, such as parameters and type.

[0167] In one possible implementation, the second application entity determines the target model and / or algorithm based on the task assistance information. Then, the second application entity sends model and / or algorithm selection information.

[0168] In one possible implementation, the first application entity and the second application entity are application entities at different layers. These different layers could be, for example, the business layer, service layer, and resource layer in a communication network architecture. Of course, other layers are also possible, and this solution does not limit this. The business layer is used for communication domain business management. The service layer is used for communication domain operation and maintenance. The resource layer is used for communication domain resource management. For example, the first application entity can be an application entity at the business layer, service layer, or resource layer. The second application entity can also be an application entity at the business layer, service layer, or resource layer. For instance, the first application entity can be an application entity at the service layer, and the second application entity can be an application entity at the resource layer.

[0169] In one possible implementation, the second application entity further sends a second task allocation request to the third application entity, wherein the third application entity and the second application entity are application entities in different domains. The second task allocation request may be related to a subtask of the aforementioned task, or it may be related to other tasks; this solution does not impose any limitations on this. For example, the second application entity and the third application entity are application entities in the same layer but different domains. These different domains may be, for example, a wireless domain, a cloud core domain, an Internet Protocol (IP) domain, an optical access domain, an optical transmission domain, etc. In one possible implementation, the third application entity and the second application entity are application entities in the same layer, such as the service layer, the business layer, or the resource layer. For example, both the third application entity and the second application entity are service layer application entities, or both are resource layer application entities. Optionally, the third application entity is an application entity in any of the following resource layer domains: wireless domain, cloud core domain, IP domain, optical access domain, and optical transmission domain. The second application entity can be an application entity from any of the following resource layer domains: wireless domain, cloud core domain, IP domain, optical access domain, and optical transport domain. For example, the third application entity can be an application entity from the wireless domain of the resource layer. The second application entity can be an application entity from the IP domain of the resource layer.

[0170] In this embodiment of the application, the first application entity sends task assistance information to the second application entity. The second application entity can determine the model and / or algorithm that matches the task based on the task assistance information. This can effectively improve the target agent's (i.e., the second application entity) processing ability for the current task and the accuracy of the generated scheme.

[0171] Reference Figure 5 The image shows another communication method provided in an embodiment of this application. This example illustrates a model / algorithm instance-assisted selection among multiple Agents in a cross-layer scenario. Figure 5 As shown, the communication method may include steps 501-510, as follows:

[0172] 501. The AI ​​agent management of the MnS producer (Producer1) obtains task assistance information corresponding to the above task scenarios from the knowledge base.

[0173] For example, the AI ​​agent management of MnS Producer1 (the operator's NMS) retrieves task assistance information from a knowledge base based on the task scenario. This knowledge base can come from a local knowledge base or use a third-party independent general knowledge base, etc.

[0174] Optionally, the task auxiliary information includes reference task use case information and expected output information. Further, the task auxiliary information also includes a task matching threshold.

[0175] 502. The AI ​​agent management of MnS Producer1 sends a task assignment request 1, which includes the aforementioned task assistance information.

[0176] The AI ​​agent management of MnS Producer1 sends an agent task allocation request to the AI ​​agent management of MnS Producer2. Optionally, in conjunction with the above... Figure 3 As introduced, MnS Producer1's AI agent management is based on the general service interface createMOI, which constructs agent message instances and provides relevant task assistance information to trigger the target collaborative agent to set up models / algorithms.

[0177] 503. MnS Producer2's AI agent management determines the target model / algorithm based on task auxiliary information and generates the corresponding workflow.

[0178] 504. MnS Producer2's AI agent management returns response message 1, which includes workflow and model / algorithm selection information.

[0179] 505. The AI ​​agent management of MnS Producer1 sends task assignment request 2, which does not include the aforementioned task assistance information.

[0180] Task assignment request 2 is used to instruct the AI ​​agent of MnS Producer2 to manage and execute tasks.

[0181] In this process, MnS Producer1 and MnS Producer2 interact across layers to allocate tasks to agents; these tasks do not provide auxiliary information. In other words, MnS Producer1 and MnS Producer2 reside on different layers. Optionally, in conjunction with the aforementioned... Figure 3 As mentioned in the introduction, the needTaskSupport attribute is set to False. That is, target Agent1 (i.e., the AI ​​agent management of MnSProducer2 below) does not need to be configured with a model / algorithm.

[0182] 506. MnS Producer2's AI agent manages and executes tasks.

[0183] 507. The AI ​​agent management of MnS Producer2 returns response message 2, which includes the results of the task execution.

[0184] 508. The AI ​​agent management of MnS Producer1 sends a task assignment request 3 to the AI ​​agent management of MnS Producer3. The request 3 includes task assistance information.

[0185] MnS Producer1 and MnS Producer 3 interact across layers to allocate tasks to agents and provide relevant task assistance information. Optionally, if the needTaskSupport attribute is set to True, the request 3 will include the corresponding task assistance information to trigger the target agent 2 (i.e., the AI ​​agent management of MnS Producer 3 below) to perform model / algorithm settings.

[0186] 509. MnS Producer3 manages and optimizes AI intelligent agents / algorithms and executes tasks.

[0187] 510. MnS Producer3's AI agent management returns response message 3, which includes model / algorithm selection information.

[0188] This example provides a solution that supports cross-layer agent message sending. In MnS Producer2, agent1 does not require model / algorithm instance setup, while in MnS Producer3, agent2 does. This enables collaboration between agents. Compared to existing technologies where agent instances are associated with pre-configured models / algorithms after creation and the agent processes assigned tasks based on the capabilities of those models / algorithms, this solution introduces a mechanism to assist agents in selecting model / algorithm instances within the agent message collaboration protocol. In cross-layer scenarios, this effectively improves the target agent's ability to handle current tasks and the accuracy of generated solutions.

[0189] Reference Figure 6 The image shows another communication method provided in an embodiment of this application. This example illustrates a model / algorithm instance-assisted selection among multiple agents in a cross-domain scenario. Figure 6 As shown, the communication method may include steps 601-610, as follows:

[0190] 601. MnS Producer1's AI agent management retrieves task assistance information corresponding to the above task scenarios from the knowledge base.

[0191] For example, the AI ​​agent management of MnS Producer1 (the operator's NMS) retrieves task assistance information from a knowledge base based on the task scenario. This knowledge base can come from a local knowledge base or use a third-party independent general knowledge base, etc.

[0192] Optionally, the task auxiliary information includes reference task use case information and expected output information. Further, the task auxiliary information also includes a task matching threshold.

[0193] 602. The AI ​​agent management of MnS Producer1 sends a task assignment request 1, which includes the aforementioned task assistance information.

[0194] The AI ​​agent management of MnS Producer1 sends an agent task allocation request to the AI ​​agent management of MnS Producer2. For example, the AI ​​agent management of MnS Producer1 constructs an agent message instance based on the general service interface createMOI, providing relevant task assistance information to trigger the target collaborative agent to set up the model / algorithm.

[0195] 603. MnS Producer2's AI agent management determines the target model / algorithm based on task auxiliary information and generates the corresponding workflow.

[0196] 604. MnS Producer2's AI agent management returns response message 1, which includes workflow and model / algorithm selection information.

[0197] 605. The AI ​​agent management of MnS Producer2 sends a task assignment request 2 to the AI ​​agent management of MnS Producer3. Request 2 does not include the aforementioned task assistance information.

[0198] Task assignment request 2 is used to instruct the AI ​​agent of MnS Producer3 to manage and execute tasks.

[0199] In this process, MnS Producer2 and MnS Producer3 interact across domains to allocate agent tasks, which do not provide auxiliary information. MnS Producer2 and MnS Producer3 reside in the same layer but different domains. That is, target agent2-1 (i.e., the AI ​​agent management of MnS Producer3) does not require model / algorithm settings.

[0200] 606. MnS Producer3's AI agent manages and executes tasks.

[0201] 607. MnS Producer3's AI agent management returns response message 2, which includes the results of the task execution.

[0202] 608. The AI ​​agent management of MnS Producer2 sends a task assignment request 3 to the AI ​​agent management of MnS Producer3. The request 3 includes task assistance information.

[0203] MnS Producer2 and MnS Producer3 interact across domains to allocate tasks to agents and provide relevant task assistance information. Optionally, if the needTaskSupport attribute is set to True, the request 3 will include the corresponding task assistance information to trigger the target agent 2-2 (i.e., the AI ​​agent management of MnS Producer3) to perform model / algorithm settings.

[0204] 609. MnS Producer3's AI agent manages and optimizes models / algorithms, and executes tasks.

[0205] 610. MnS Producer3's AI agent management returns response message 3, which includes model / algorithm selection information.

[0206] This example demonstrates a solution that supports cross-domain agent message sending. In MnS Producer3, agent2-1 does not require model / algorithm instance setup, while agent2-2 does. Compared to existing technologies, this approach associates an agent instance with a pre-configured model / algorithm upon creation and uses the capabilities provided by that model / algorithm to handle tasks assigned to that agent. By introducing a mechanism to assist the agent in selecting model / algorithm instances within the agent message collaboration protocol, this effectively improves the target agent's ability to handle current tasks and the accuracy of generated solutions in cross-domain scenarios.

[0207] It should be noted that, in the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions between the various 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 relationship.

[0208] The methods of the embodiments of this application have been described in detail above, and the apparatus of the embodiments of this application is provided below. It is understood that the division of multiple units or modules in the various apparatus embodiments of this application is only a logical division based on function and is not intended to limit the specific structure of the apparatus. In specific implementations, some functional modules may be subdivided into more smaller functional modules, and some functional modules may be combined into a single functional module. However, regardless of whether these functional modules are subdivided or combined, the general flow executed by the apparatus is the same. For example, some apparatuses include a receiving unit and a transmitting unit. In some designs, the transmitting unit and the receiving unit can also be integrated into a communication unit, which can implement the functions implemented by the receiving unit and the transmitting unit. Typically, each unit corresponds to its own program code (or program instructions). When the program code corresponding to each unit runs on the processor, it causes the unit to be controlled by the processing unit to execute the corresponding flow and thus achieve the corresponding function.

[0209] This application also provides an apparatus for implementing any of the above methods. For example, a communication apparatus is provided that includes a module (or means) for implementing the steps performed by the first application entity or the second application entity in any of the above methods.

[0210] For example, refer to Figure 7 The diagram shown is a structural schematic of a communication device provided in an embodiment of this application. This communication device is used to implement the aforementioned communication method, for example... Figure 4 The communication method shown.

[0211] like Figure 7 As shown, the device may include a communication module 701, as detailed below:

[0212] The communication module 701 is used to send a first task allocation request, which includes task auxiliary information, which is used by a second application entity to determine a model and / or algorithm that matches the task.

[0213] In one possible implementation, the communication module 701 is further configured to receive model and / or algorithm selection information in response to the first task assignment request.

[0214] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a matching threshold between the model and / or algorithm and the task.

[0215] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

[0216] In one possible implementation, a processing module is also included, which is used to obtain task-related auxiliary information from the knowledge base based on the task.

[0217] In one possible implementation, the knowledge base is either a local knowledge base or a third-party knowledge base.

[0218] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0219] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0220] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0221] For a description of each of the above modules, please refer to the description in the foregoing embodiments, which will not be repeated here.

[0222] For example, refer to Figure 7 The diagram shown is a structural schematic of a communication device provided in an embodiment of this application. This communication device is used to implement the aforementioned communication method, for example... Figure 4 The communication method shown.

[0223] like Figure 7 As shown, the device may include a communication module 701, as detailed below:

[0224] The communication module 701 is used to receive a first task allocation request, the first task allocation request including task auxiliary information, the task auxiliary information being used by the second application entity to determine a model and / or algorithm that matches the task;

[0225] The communication module 701 is also used to send model and / or algorithm selection information based on the task auxiliary information in response to the first task allocation request.

[0226] In one possible implementation, the model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

[0227] In one possible implementation, a processing module is also included for determining the target model and / or algorithm based on the task assistance information.

[0228] In one possible implementation, the task-aiding information includes one or more of the following: reference task use case information, expected output information, and a matching threshold between the model and / or algorithm and the task.

[0229] In one possible implementation, the first application entity and the second application entity are application entities at different layers.

[0230] In one possible implementation, the first application entity is an application entity of the business layer, service layer, or resource layer.

[0231] In one possible implementation, the second application entity is an application entity of the business layer, service layer, or resource layer.

[0232] In one possible implementation, the communication module 701 is further configured to send a second task allocation request to a third application entity, wherein the third application entity and the second application entity are application entities from different domains.

[0233] In one possible implementation, the third application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0234] In one possible implementation, the second application entity is an application entity of any one of the following in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

[0235] In one possible implementation, the third application entity and the second application entity are application entities in the same layer, such as the business layer, service layer, or resource layer.

[0236] For a description of each of the above modules, please refer to the description in the foregoing embodiments, which will not be repeated here.

[0237] It should be understood that the division of modules in the above devices is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, modules in a communication device can be implemented by a processor calling software; for example, a communication device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of each module in the device. The processor can be, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the modules in the device can be implemented as hardware circuits. The functionality of some or all units can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functionality of some or all of the above units is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD), such as a field-programmable gate array (FPGA), which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the above units. All modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0238] Reference Figure 8 The diagram shown is a hardware structure schematic of another communication device provided in an embodiment of this application. Figure 8 The communication device 800 shown includes one or more processors 801 (one processor is illustrated in the figure).

[0239] Processor 801 is a circuit with signal processing capabilities. In one implementation, processor 801 can be a circuit with instruction read and execute capabilities, such as a central processing unit (CPU), microprocessor, graphics processing unit (GPU) (which can be understood as a type of microprocessor), or digital signal processor (DSP). In another implementation, processor 801 can implement certain functions through the logical relationships of hardware circuits. These logical relationships of hardware circuits are fixed or reconfigurable. For example, processor 801 can be a hardware circuit implemented as an ASIC or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the processor loading instructions to implement the functions of some or all of the above modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a type of ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), or deep learning processing unit (DPU). The processor 801 is used to execute related programs to implement the functions required by the units in the communication device of this application embodiment, or to execute the communication method of this application method embodiment.

[0240] Optionally, the communication device 800 may also include a memory (e.g., memory 803, memory 804, memory 805) (shown as dashed lines in the figure). This memory is used to store instructions executed by the processor 801, or to store input data required by the processor 801 to execute instructions, or to store data generated after the processor 801 executes instructions.

[0241] Optionally, the memory may be located within the one or more processors (e.g., memory 803), or outside the one or more processors (e.g., memory 804, memory 805), or may include a storage portion located within the one or more processors and a storage portion located outside the one or more processors.

[0242] In this embodiment, the memory (e.g., memory 803, memory 804, memory 805) may include, but is not limited to, cache, read-only memory (ROM), random access memory (RAM), synchronous dynamic random access memory (SDRAM), hard disk drive (HDD) or solid-state drive (SSD), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), etc. Memory is any other medium capable of carrying or storing desired program code having an instruction or data structure form and accessible by a computer, but is not limited thereto. The memory in this embodiment may also be a circuit or any other device capable of implementing storage functions for storing computer programs or instructions, and / or data.

[0243] Optionally, the communication device 800 may also include a communication interface 802 (shown as a dashed line in the figure). The processor 801 and the communication interface 802 are coupled to each other. The communication interface 802 may be a transceiver or interface circuit, bus, module, or other type of communication interface.

[0244] The memory can store programs. When the program stored in the memory is executed by the processor 801, the processor 801 and the communication interface 802 are used to execute the various steps of the communication method of the embodiments of this application.

[0245] As can be seen, each module in the above device can be one or more processors (or processing circuits) configured to implement the above methods, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms or a portion of the processing circuits in these processors.

[0246] Furthermore, the modules in the above devices can be integrated in whole or in part, or they can be implemented independently. In one implementation, these modules are integrated together as a system-on-a-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or for implementing the functions of the modules of the device. The at least one processor may be of different types, such as CPU and FPGA, CPU and artificial intelligence processor, CPU and GPU, etc.

[0247] It should be noted that, although Figure 8 The illustrated device 800 only shows the memory, processor, and communication interface. However, those skilled in the art should understand that in specific implementations, device 800 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that device 800 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that device 800 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 8 All the devices shown.

[0248] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of any of the above methods.

[0249] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to perform one or more steps of any of the methods described above.

[0250] It is understood that in this application, "instruction" can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information to indicate A, it can be understood that the instruction information carries A, directly indicates A, or indirectly indicates A. In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementation, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index, or indirectly indicating the information to be instructed by indicating other information, wherein there is an association between the other information and the information to be instructed. It is also possible to indicate only a part of the information to be instructed, while the other parts of the information to be instructed are known or agreed upon in advance. For example, the instruction of specific information can also be achieved by using the arrangement order of various information in advance (e.g., as specified by a protocol), thereby reducing the instruction overhead to a certain extent. The information to be instructed can be sent as a whole or divided into multiple sub-information to be sent separately, and the sending period and / or sending time of these sub-information can be the same or different. This application does not limit the specific sending method. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the transmitting device by sending configuration information to the receiving device.

[0251] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0252] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed between each other may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0253] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0254] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid state disks (SSDs).

[0255] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A communication method, characterized in that, Applied to a first application entity, the method includes: A first task allocation request is sent, the first task allocation request including task assistance information, the task assistance information being used by a second application entity to determine a model and / or algorithm that matches the task.

2. The method according to claim 1, characterized in that, The task assistance information includes one or more of the following: reference task use case information, expected output information, and model and / or algorithm matching threshold.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Receive model and / or algorithm selection information in response to the first task assignment request.

4. The method according to claim 3, characterized in that, The model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Based on the task, obtain the task auxiliary information from the knowledge base.

6. The method according to claim 5, characterized in that, The knowledge base is either a local knowledge base or a third-party knowledge base.

7. The method according to any one of claims 1 to 6, characterized in that, The first application entity and the second application entity are application entities at different layers.

8. The method according to claim 7, characterized in that, The first application entity is an application entity of the business layer, service layer, or resource layer.

9. The method according to claim 7, characterized in that, The second application entity is an application entity in the business layer, service layer, or resource layer.

10. A communication method, characterized in that, Applied to a second application entity, the method includes: Receive a first task allocation request, the first task allocation request including task assistance information, the task assistance information being used by the second application entity to determine a model and / or algorithm that matches the task; Based on the task assistance information, model and / or algorithm selection information is sent in response to the first task allocation request.

11. The method according to claim 10, characterized in that, The model and / or algorithm selection information includes one or more of the following: model and / or algorithm matching status information, model and / or algorithm matching degree, and target model and / or algorithm information. The method further includes: The target model and / or algorithm are determined based on the task assistance information.

12. The method according to claim 10 or 11, characterized in that, The task assistance information includes one or more of the following: reference task use case information, expected output information, and model and / or algorithm matching threshold.

13. The method according to any one of claims 10 to 12, characterized in that, The first application entity and the second application entity are application entities at different layers.

14. The method according to claim 13, characterized in that, The first application entity is an application entity of the business layer, service layer, or resource layer.

15. The method according to claim 13, characterized in that, The second application entity is an application entity in the business layer, service layer, or resource layer.

16. The method according to any one of claims 10 to 12, characterized in that, The method further includes: Send a second task allocation request to a third application entity, wherein the third application entity and the second application entity are application entities from different domains.

17. The method according to claim 16, characterized in that, The third application entity is any one of the application entities in the resource layer's wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

18. The method according to claim 16, characterized in that, The second application entity is any one of the following application entities in the resource layer: wireless domain, cloud core domain, Internet protocol domain, optical access domain, and optical transmission domain.

19. The method according to any one of claims 16 to 18, characterized in that, The third application entity and the second application entity are application entities in the same layer, such as the business layer, service layer, or resource layer.

20. A communication device, characterized in that, Includes modules for implementing the method as described in any one of claims 1-19.

21. A communication device, characterized in that, The apparatus includes a processor and a memory for storing program code, and the processor for calling the program code to perform the method as described in any one of claims 1-19.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the method as described in any one of claims 1-19 is performed.

23. A computer program product, characterized in that, The computer program product includes relevant program instructions, which, when executed, cause the method as described in any one of claims 1-19 to be implemented.

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