Edge resource management method and device and edge resource management agent

Abstract

The invention provides an edge resource management method and device and an edge resource management agent, and relates to the technical field of computers, in particular to the fields of artificial intelligence, cloud computing and the like. According to the specific implementation scheme, resource management description information used for describing an edge resource management task in a natural language is obtained; first resource analysis steps needing to be executed for executing the edge resource management task and a resource analysis sequence of the first resource analysis steps are planned and obtained based on the resource management description information by utilizing an edge resource management agent; using the edge resource management agent to call a target resource analysis tool corresponding to each first resource analysis step to execute each first resource analysis step according to the resource analysis sequence to obtain a resource analysis result of each first resource analysis step; and summarizing the resource analysis results of the first resource analysis steps by utilizing an edge resource management positioning agent to obtain an execution result of the edge resource management task.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the fields of artificial intelligence, cloud computing, and more particularly to an edge resource management method, device, and edge resource management intelligent entity. Background Art

[0002] In CDN scenarios, operation and maintenance personnel often need to plan, optimize and manage edge resources. Summary of the invention

[0003] The present disclosure provides a method, an apparatus, a device and a storage medium for improving the accuracy of edge resource management.

[0004] According to a first aspect of the present disclosure, there is provided an edge resource management method, comprising:

[0005] Obtain resource management description information for describing edge resource management tasks in natural language;

[0006] Using the edge resource management agent, based on the resource management description information, a first resource analysis step required to execute the edge resource management task and a resource analysis sequence of each of the first resource analysis steps are planned;

[0007] Utilizing the edge resource management agent, calling the target resource analysis tool corresponding to each of the first resource analysis steps to respectively execute each of the first resource analysis steps in accordance with the resource analysis order, and obtaining the resource analysis results of each of the first resource analysis steps, wherein the target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute the corresponding resource analysis step;

[0008] The edge resource management positioning agent is utilized to obtain the execution result of the edge resource management task based on the summary of the resource analysis results of each of the first resource analysis steps.

[0009] According to a second aspect of the present disclosure, there is provided an agent training method, comprising:

[0010] Obtaining sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word;

[0011] Based on the sample description information, the first sample prompt word and the second resource analysis step, training a first large language model, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word;

[0012] Based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, training a second large language model, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question;

[0013] The trained intelligent agent includes the first largest language model and the second largest language model.

[0014] According to a third aspect of the present disclosure, there is provided an edge resource management device, comprising:

[0015] An information acquisition module, used to acquire resource management description information used to describe the edge resource management task in natural language;

[0016] A planning module, used to use the edge resource management agent to plan, based on the resource management description information, the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each of the first resource analysis steps;

[0017] An execution module, used to use the edge resource management agent to call the target resource analysis tool corresponding to each of the first resource analysis steps to respectively execute each of the first resource analysis steps in accordance with the resource analysis order, and obtain the resource analysis results of each of the first resource analysis steps, wherein the target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute the corresponding resource analysis step;

[0018] A result module is used to utilize the edge resource management positioning agent to obtain the execution result of the edge resource management task based on the resource analysis result summary of each of the first resource analysis steps.

[0019] According to a fourth aspect of the present disclosure, there is provided an intelligent agent training device, comprising:

[0020] A sample acquisition module, used to acquire sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word;

[0021] A first training module is used to train a first large language model based on the sample description information, the first sample prompt word and the second resource analysis step, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word;

[0022] A second training module is used to train a second large language model based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question;

[0023] The trained intelligent agent includes the first largest language model and the second largest language model.

[0024] According to a fifth aspect of the present disclosure, an edge resource management agent is provided, comprising:

[0025] An input module, used for receiving input information;

[0026] a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by calling the large language model to execute any one of the methods described in the first aspect above;

[0027] An output module is used to output the output information obtained by the processing module.

[0028] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods described in the first aspect.

[0029] According to a seventh aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of the above first aspects.

[0030] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0032] Figure 1It is a flow chart of the edge resource management method provided by the present disclosure;

[0033] Figure 2a It is a diagram of a user entering descriptive information through a conversational front end;

[0034] Figure 2b It is a schematic diagram of a user inputting description information through a service number;

[0035] Figure 2c It is a schematic diagram of a user inputting description information through an intelligent assistant;

[0036] Figure 3 It is a flow chart of a training method for an edge resource management agent provided by the present disclosure;

[0037] Figure 4 is a schematic diagram of an architecture for implementing an edge resource management method provided by the present disclosure;

[0038] Figure 5 It is a flow chart of the intelligent agent training method provided by the present disclosure;

[0039] Figure 6 is a structural schematic diagram of an edge resource management device provided by the present disclosure;

[0040] Figure 7 is a structural schematic diagram of an intelligent agent training device provided by the present disclosure;

[0041] Figure 8 It is a block diagram of an electronic device used to implement the edge resource management method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0043] First, the relevant terms or concepts in the embodiments of the present disclosure are explained:

[0044] AI Agent: It is an intelligent agent system with a large language model as the core controller. Its essence is an agent system that controls the large language model to solve user problems. It relies on the large language model as its core decision-making and processing unit, and can use the logical reasoning ability of the large language model to solve user problems. In practical applications, the agent can be a software or hardware entity that can act autonomously.

[0045] LLM (Large Language Model) refers to a deep learning model trained on massive text data, which has strong expressive and generalization capabilities. It can not only generate natural language text, but also deeply understand the meaning of the text and handle various natural language tasks such as text summarization, question answering, translation, etc.

[0046] It is understandable that in some application scenarios, users want to plan and optimize edge resources, and related technologies often require manual query of edge resources, and then manually formulate planning and optimization plans based on their own experience. Manual planning and optimization plans require operation and maintenance personnel to have certain relevant knowledge, which is difficult for new operation and maintenance personnel. They may even have limited relevant knowledge reserves, resulting in inaccurate planning and optimization plans.

[0047] Based on this, the present application provides an edge resource management intelligent agent, so that the edge resource management intelligent agent can be used to perform edge resource management tasks and obtain the execution results of the edge resource management tasks.

[0048] The edge resource management agent can perform edge resource management tasks in the following ways: Figure 1 As shown, including:

[0049] Step S101: Acquire resource management description information for describing edge resource management tasks in natural language.

[0050] Step S102, using the edge resource management agent, based on the resource management description information, plans to obtain the first resource analysis steps required to execute the edge resource management task and the resource analysis sequence of each first resource analysis step.

[0051] Step S103, using the edge resource management intelligent body, calling the target resource analysis tool corresponding to each first resource analysis step to execute each first resource analysis step in a resource analysis order, and obtaining the resource analysis results of each first resource analysis step.

[0052] The target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute corresponding resource analysis steps.

[0053] Step S104, using the edge resource management positioning agent, and summarizing the resource analysis results of each first resource analysis step to obtain the execution result of the edge resource management task.

[0054] This embodiment is selected to directly use the pre-trained edge resource management agent to plan the resource management description information, and then call each resource analysis tool to execute the first resource analysis steps obtained by the plan, and summarize the resource analysis results of each first resource analysis step to obtain the execution result of the edge resource management task. During the whole process, the operation and maintenance personnel can formulate the planning and optimization plan without manually formulating the planning and optimization plan based on their own experience, which effectively improves the accuracy of edge resource management.

[0055] The above steps S101 to S104 will be described below respectively:

[0056] Among them, in step S101, the resource management description information can be input by the operation and maintenance personnel in text form, or in voice form. For example, the operation and maintenance personnel can input the text "Can Henan Telecom add a new node?" or input the voice "Can Henan Telecom add a new node?"

[0057] When the operation and maintenance personnel input text, the text can be directly used as the problem description information. When the operation and maintenance personnel input voice, the voice can be recognized and the recognized text can be used as the problem description information.

[0058] The execution subjects of the cross-platform operation method provided in this application include but are not limited to mobile phones, desktop computers, tablet computers, smart watches, smart speakers, etc. For mobile phones, desktop computers, platform computers and devices that support text input, the operation and maintenance personnel can input text or voice, while for devices such as smart speakers that do not support text input, the operation and maintenance personnel can only input voice.

[0059] The operation and maintenance personnel may input text or voice by opening a specific page and inputting text or voice on the page. For example, Figure 2a It is also possible to open a specific service number through a specific application and input text or voice in the dialog window with the service number, for example, Figure 2b It is also possible to input text or voice by interacting with a specific group chat robot in the group chat window of the social software, for example, Figure 2c shown.

[0060] It is understandable that although Figure 2a , Figure 2b as well as Figure 2c The inputs of the operation and maintenance personnel are shown to be text, but this is only for the convenience of showing the problem description information input by the operation and maintenance personnel. The principle is the same for the case of voice input, which will not be repeated here.

[0061] In step S102, the edge resource management agent may perform planning based on pre-learned knowledge about how to perform planning. During planning, the edge resource management agent may be implemented based on any algorithm model with planning capabilities.

[0062] Exemplarily, in a possible embodiment, step S102 includes:

[0063] Step S1021, using the edge resource management agent, calls the first large language model, and inputs the resource management description information and the first prompt word into the first large language model, obtains the first resource analysis step output and executed by the first large language model under the guidance of the first prompt word, and the resource analysis order of each first resource analysis step.

[0064] The first prompt word is used to guide the first language model to perform step planning. The first prompt word can be pre-set or obtained when training the first language model. For example, the first prompt word can be:

[0065] # Role

[0066] You are an edge construction expert who needs to be familiar with and analyze each link in the CDN or BEC construction process, including but not limited to demand analysis, construction process management, asset lifecycle management, construction plan output, and node capacity analysis.

[0067] # Target

[0068] 1. Based on the user's question, accurately analyze and select the appropriate plug-in to answer according to the plug-in description.

[0069] 2. If the problem is complex, you need to call multiple different plug-ins, call them in sequence to meet the needs of solving complex problems.

[0070] 3. Record and analyze historical conversations within three cycles to achieve the purpose of multi-round conversations.

[0071] 4. If there is no plug-in that can be called, the knowledge document is used first to answer the question.

[0072] 5. For conceptual questions, try to use knowledge documents to answer them, such as asking about processes, specifications, etc.

[0073] # limit

[0074] 1. You can only answer questions related to CDN or BEC construction. For non-related questions, you should clearly inform users that you cannot answer them.

[0075] 2. When calling a plug-in, ensure the applicability and accuracy of the plug-in to avoid providing incorrect information.

[0076] 3. The historical conversations within three cycles must be recorded and analyzed to maintain coherence and accuracy in multiple rounds of conversations.

[0077] 4. When conducting multiple rounds of conversations, make full use of historical conversation records to avoid asking the same questions repeatedly and improve conversation efficiency.

[0078] This embodiment is selected to utilize a pre-trained edge resource management agent, and planning is performed based on resource management description information through the trained first language model combined with the first prompt word. Due to the guidance of the first prompt word, the ability of the planning step of the first language model can be fully utilized, thereby improving the accuracy of the resource analysis step obtained by the plan, and further making the subsequent execution results based on the resource analysis step more accurate.

[0079] The first prompt word is only a possible example. In other possible embodiments, the first prompt word can also be set according to the needs of the user. In order to enable the first prompt word to better guide the first language model, the first prompt word can be used to describe the process of planning the resource analysis steps required to execute the example edge resource management task and the resource analysis order of each resource analysis step based on the example resource management description information. The example edge resource management task is the edge resource management task described by the example resource management description information;

[0080] And / or, the first prompt word is used to guide the first language model to use the first knowledge base, and the first knowledge base is used to record the health and / or construction cost of edge resources. The construction cost is used to represent the cost required for deploying some edge resources.

[0081] In step S103, the number and type of preset resource analysis tools may vary according to different application scenarios. In addition, in a possible embodiment, the target resource analysis tools corresponding to each first resource analysis step may be obtained in the following manner:

[0082] The resource management description information, the first prompt word, and the description information of each of the multiple preset resource analysis tools are input into the first large language model to obtain the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each first resource analysis step output by the first large language model under the guidance of the first prompt word, as well as the target resource analysis tools corresponding to each first resource analysis step output by the first large language model under the guidance of the first prompt word.

[0083] By selecting this embodiment, the edge resource analysis agent can grasp the capabilities of each resource analysis tool based on the description information of each resource analysis tool, thereby more accurately calling the resource analysis tool with corresponding capabilities to perform the first resource analysis step, thereby improving the accuracy of the resource analysis results, and further improving the accuracy of subsequent execution results obtained based on the summary of the resource analysis results.

[0084] Correspondingly, in this embodiment, in order to enable the first prompt word to better guide the first large language model to accurately output the target resource analysis tool corresponding to each first resource analysis step, the first prompt word can be used to describe the precautions when calling each resource analysis tool.

[0085] In step S104, the edge resource analysis agent can summarize based on the pre-learned knowledge on how to summarize the resource analysis results of each resource analysis step. When summarizing, the edge resource analysis agent can be implemented based on any algorithm model with summarizing capabilities.

[0086] Exemplarily, in a possible embodiment, step S104 includes:

[0087] Step S1041, using the edge resource management agent, calling the second largest language model, and inputting the resource analysis results of each first resource analysis step and the second prompt word into the second largest language model, to obtain the execution result of the edge resource management task output by the second largest language model under the guidance of the second prompt word.

[0088] The second prompt word is used to guide the second language model to summarize. The second prompt word can be pre-set or obtained when training the second language model. The summary in this article refers to the process of refining the results of the task to obtain the answer to the question.

[0089] This embodiment is selected to utilize the pre-trained edge resource management agent to perform summary through the trained second language model combined with the second prompt word. Due to the guidance of the second prompt word, the summarizing ability of the second language model can be fully utilized, thereby improving the accuracy of the execution result obtained by the summary.

[0090] The execution result obtained in step S104 can be used to indicate whether the edge resources can meet the requirements indicated by the edge resource management task, or the edge resource deployment scheme that can meet the requirements indicated by the edge resource management task. It is understandable that in some application scenarios, the operation and maintenance personnel have some requirements but are not clear whether the edge resources can meet the requirements, while in other application scenarios, the operation and maintenance personnel have some requirements but are not clear how to achieve the requirements. Therefore, the execution result indicates whether the edge resources can meet the requirements, or the deployment scheme that can meet the requirements, which can meet the needs of different operation and maintenance personnel in different situations.

[0091] The following will explain how to train the edge resource management agent. Figure 3 , Figure 3 The figure is a flow chart of the training method of the edge resource management agent provided by the present application, including:

[0092] Step S301, obtain sample description information used to describe the sample edge resource management problem in natural language, the second resource analysis steps required to answer the sample edge resource management problem, the resource analysis results of each second resource analysis step, the answer to the sample edge resource management problem, the first sample prompt word and the second sample prompt word.

[0093] Step S302: training a first language model based on the sample description information, the first sample prompt word and the second resource analysis step.

[0094] The first language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word.

[0095] Step S303: training a second language model based on the resource analysis results of each second resource analysis step, the second sample prompt words, and the answers to the sample edge resource management questions.

[0096] The second largest language model is used to summarize the resource analysis results of each second resource analysis step under the guidance of the second sample prompt word to obtain the answer to the sample edge resource management problem.

[0097] The trained edge resource management agent includes the first largest language model and the second largest language model.

[0098] Among them, in step S302, the sample description information and the first sample prompt word can be used as the input of the first large language model to obtain the output of the first large language model, and the second resource analysis step can be used as the true value to calculate the loss between the output of the first large language model and the true value, and then the first large language model is adjusted according to the loss.

[0099] In step S303, the resource analysis results of each second resource analysis step and the second sample prompt word can be used as the input of the second largest language model to obtain the output of the second largest language model, and the answer to the sample edge resource management question is used as the true value to calculate the loss between the output of the second largest language model and the true value, and then the second largest language model is adjusted according to the loss.

[0100] By selecting this embodiment, the edge resource management process of the operation and maintenance personnel can be integrated into the function of the edge resource management intelligent agent by training the first largest language model and the second largest language model, so that edge resource management can be performed through the edge resource management intelligent agent, thereby improving the accuracy of edge resource management.

[0101] In the above example, the process of implementing edge resource management based on the edge resource management agent is described. In order to more clearly describe the edge resource management agent provided by the present application, the software architecture for implementing edge resource management will be described below. For example, see Figure 4 , Figure 4 The figure shows the software architecture for implementing the edge resource management method provided in this application.

[0102] Among them, the service number, super assistant and conversational front end of the user interface layer correspond to the aforementioned Figure 2b , Figure 2a as well as Figure 2c The interface in the example shown.

[0103] Resource management in the intelligent assistant layer is the aforementioned edge resource management agent.

[0104] The perception, planning, execution and reflection of the resource management layer are the four stages of the edge resource management agent, among which the perception stage and the planning stage correspond to the aforementioned step S102, the execution stage corresponds to the aforementioned step S103 and step S104, and the reflection stage is used for the edge resource management agent to optimize itself based on its own output.

[0105] exist Figure 4 In the example shown, the perception phase includes resource demand, resource health (i.e., obtaining the health of edge resources from the first knowledge base), construction cost (i.e., obtaining the construction cost from the first knowledge base), and model-based analysis. The planning phase includes tool chain, parameter parsing, and multiple rounds of planning. The execution phase includes relying on the enterprise large model platform (i.e., the aforementioned operation and maintenance platform), task scheduling execution, construction plan output achieved through tools, capacity assessment, construction process control, etc. The reflection phase includes result summary, model optimization, and intelligent suggestions.

[0106] based on Figure 4 Based on the framework shown, the edge resource management agent can achieve the following functions:

[0107] Natural language processing: The agent uses natural language processing technology to analyze resource construction requirements from operation and maintenance personnel or systems. These requirements may include the deployment of new nodes, expansion or optimization of existing resources, etc.

[0108] Dynamic demand assessment: Combining real-time data monitoring and historical data analysis, the intelligent agent can dynamically assess the urgency and impact of the demand, thereby prioritizing key tasks.

[0109] Intelligent reasoning ability: Using the reasoning ability of large models, intelligent agents can perform calculations in complex resource environments and derive the optimal resource allocation plan, including the location of computing nodes, optimization of network topology, and allocation of storage resources.

[0110] Multi-dimensional optimization: Considering multiple dimensions such as cost, performance, reliability and scalability, the intelligent agent can generate a construction plan that balances various factors to ensure maximum resource utilization.

[0111] Automation tool chain integration: The intelligent agent is seamlessly integrated with the automation deployment tool to achieve rapid deployment and configuration of resources. This includes automated script execution, configuration file generation, and real-time status monitoring.

[0112] Real-time adjustment and update: During the resource deployment process, the agent can make dynamic adjustments based on real-time feedback to ensure the accuracy and efficiency of resource construction.

[0113] Continuous monitoring: The agent continuously monitors deployed resources to detect and warn of potential problems in a timely manner. This can include abnormal fluctuations in resource usage, increased network latency, etc.

[0114] Closed-loop feedback mechanism: Through the feedback mechanism, the intelligent agent can collect resource usage data and feedback from operation and maintenance personnel, and continuously optimize and adjust resource management strategies.

[0115] Multi-agent collaboration: In large-scale CDN and edge computing networks, multiple agents can work together to distribute resource management tasks and improve the overall efficiency of the system.

[0116] Decision support system: The intelligent agent provides management with detailed resource status reports and optimization suggestions to assist decision makers in formulating strategic resource management plans.

[0117] Corresponding to the aforementioned edge resource management method, the present application also provides an agent training method, such as Figure 5 As shown, including:

[0118] Step S501, obtain sample description information used to describe the sample edge resource management problem in natural language, the second resource analysis step required to answer the sample edge resource management problem, the resource analysis results of each of the second resource analysis steps, the answer to the sample edge resource management problem, the first sample prompt word and the second sample prompt word.

[0119] Step S502: training a first language model based on the sample description information, the first sample prompt word and the second resource analysis step.

[0120] The first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word.

[0121] Step S503: training a second language model based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answers to the sample edge resource management questions.

[0122] Among them, the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt word to obtain the answer to the sample edge resource management problem.

[0123] The trained intelligent agent includes the first largest language model and the second largest language model.

[0124] Corresponding to the aforementioned edge resource management method, the present application also provides an edge resource management device, such as Figure 6 As shown, including:

[0125] An information acquisition module 601 is used to acquire resource management description information used to describe the edge resource management task in a natural language;

[0126] A planning module 602 is used to use the edge resource management agent to plan the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each of the first resource analysis steps based on the resource management description information;

[0127] An execution module 603 is used to utilize the edge resource management agent to call the target resource analysis tool corresponding to each of the first resource analysis steps to respectively execute each of the first resource analysis steps in accordance with the resource analysis sequence to obtain the resource analysis results of each of the first resource analysis steps, wherein the target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute the corresponding resource analysis step;

[0128] The result module 604 is used to utilize the edge resource management positioning agent to obtain the execution result of the edge resource management task based on the resource analysis result summary of each of the first resource analysis steps.

[0129] In a possible embodiment, the execution result is used to indicate whether the edge resource can meet the requirement indicated by the edge resource management task, or an edge resource deployment solution that can meet the requirement indicated by the edge resource management task.

[0130] In a possible embodiment, the planning module uses the edge resource management agent to plan, based on the resource management description information, the first resource analysis steps required to execute the edge resource management task and the resource analysis sequence of each of the first resource analysis steps, including:

[0131] Utilizing the edge resource management agent, the first large language model is called, and the resource management description information and the first prompt word are input into the first large language model to obtain the first resource analysis steps output and executed by the first large language model under the guidance of the first prompt word, as well as the resource analysis order of each of the first resource analysis steps.

[0132] In a possible embodiment, the first prompt word is used to describe the process of planning the resource analysis steps required to execute an example edge resource management task and the resource analysis order of each resource analysis step based on example resource management description information, and the example edge resource management task is the edge resource management task described by the example resource management description information; and / or, the first prompt word is used to guide the first large language model to use a first knowledge base, and the first knowledge base is used to record the health and / or construction cost of edge resources.

[0133] In a possible embodiment, the planning module inputs the resource management description information and the first prompt word into the first large language model, and obtains the first resource analysis steps output by the first large language model under the guidance of the first prompt word and the resource analysis sequence of each of the first resource analysis steps, including:

[0134] The resource management description information, the first prompt word, and the description information of each of the multiple preset resource analysis tools are input into the first large language model to obtain the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each of the first resource analysis steps, which are output by the first large language model under the guidance of the first prompt word, and the target resource analysis tools corresponding to each of the first resource analysis steps, which are output by the first large language model under the guidance of the first prompt word.

[0135] In a possible embodiment, the utilizing the edge resource management positioning agent to obtain the execution result of the edge resource management task based on the resource analysis result summary of each of the first resource analysis steps includes:

[0136] Utilizing the edge resource management agent, the second largest language model is called, and the resource analysis results of each of the first resource analysis steps and the second prompt word are input into the second largest language model to obtain the execution result of the edge resource management task output by the second largest language model under the guidance of the second prompt word.

[0137] In a possible embodiment, the training process of the edge resource management agent includes:

[0138] Obtaining sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word;

[0139] Based on the sample description information, the first sample prompt word and the second resource analysis step, training a first large language model, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word;

[0140] Based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, training a second large language model, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question;

[0141] The trained edge resource management agent includes the first largest language model and the second largest language model.

[0142] Corresponding to the aforementioned intelligent agent training method, the present application also provides an intelligent agent training device, such as Figure 7 As shown, including:

[0143] The sample acquisition module 701 is used to acquire sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word;

[0144] A first training module 702 is used to train a first large language model based on the sample description information, the first sample prompt word and the second resource analysis step, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word;

[0145] A second training module 703 is used to train a second large language model based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question;

[0146] The trained intelligent agent includes the first largest language model and the second largest language model.

[0147] The present application also provides an edge resource management agent, including:

[0148] An input module, used for receiving input information;

[0149] a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by executing any of the above-mentioned edge resource management methods by calling the large language model;

[0150] An output module is used to output the output information obtained by the processing module.

[0151] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0152] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0153] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0154] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0155] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0156] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as edge resource management methods or agent training methods. For example, in some embodiments, the edge resource management method or agent training method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the edge resource management method or agent training method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the edge resource management method or the agent training method in any other appropriate manner (eg, by means of firmware).

[0157] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0159] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0162] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0163] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0164] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An edge resource management method, comprising: Obtain resource management description information for describing edge resource management tasks in natural language; Using an edge resource management agent, based on the resource management description information, a first resource analysis step required to execute the edge resource management task and a resource analysis sequence of each of the first resource analysis steps are planned; Utilizing the edge resource management agent, calling the target resource analysis tool corresponding to each of the first resource analysis steps to respectively execute each of the first resource analysis steps in accordance with the resource analysis order, and obtaining the resource analysis results of each of the first resource analysis steps, wherein the target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute the corresponding resource analysis step; The edge resource management positioning agent is utilized to obtain the execution result of the edge resource management task based on the summary of the resource analysis results of each of the first resource analysis steps.

2. The method according to claim 1, wherein: The execution result is used to indicate whether the edge resource can meet the requirement indicated by the edge resource management task, or an edge resource deployment solution that can meet the requirement indicated by the edge resource management task.

3. The method according to claim 1, wherein: The using the edge resource management agent to plan the first resource analysis steps required to execute the edge resource management task and the resource analysis sequence of each of the first resource analysis steps based on the resource management description information includes: Utilizing the edge resource management agent, the first large language model is called, and the resource management description information and the first prompt word are input into the first large language model to obtain the first resource analysis steps output and executed by the first large language model under the guidance of the first prompt word, as well as the resource analysis order of each of the first resource analysis steps.

4. The method according to claim 3, wherein: The first prompt word is used to describe the process of planning the resource analysis steps required to execute an example edge resource management task and the resource analysis order of each resource analysis step based on the example resource management description information, and the example edge resource management task is the edge resource management task described by the example resource management description information; and / or, the first prompt word is used to guide the first large language model to use a first knowledge base, and the first knowledge base is used to record the health and / or construction cost of edge resources.

5. The method according to claim 3, wherein: The step of inputting the resource management description information and the first prompt word into the first large language model to obtain the first resource analysis steps output by the first large language model under the guidance of the first prompt word and the resource analysis sequence of each of the first resource analysis steps includes: The resource management description information, the first prompt word, and the description information of each of the multiple preset resource analysis tools are input into the first large language model to obtain the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each of the first resource analysis steps, which are output by the first large language model under the guidance of the first prompt word, and the target resource analysis tools corresponding to each of the first resource analysis steps, which are output by the first large language model under the guidance of the first prompt word.

6. The method according to claim 1, wherein: The utilizing the edge resource management positioning agent to obtain the execution result of the edge resource management task based on the resource analysis result summary of each of the first resource analysis steps includes: Utilizing the edge resource management agent, the second largest language model is called, and the resource analysis results of each of the first resource analysis steps and the second prompt word are input into the second largest language model to obtain the execution result of the edge resource management task output by the second largest language model under the guidance of the second prompt word.

7. The method according to any one of claims 1 to 6, wherein: The training process of the edge resource management agent includes: Obtaining sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word; Based on the sample description information, the first sample prompt word and the second resource analysis step, training a first large language model, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word; Based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, training a second large language model, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question; The trained edge resource management agent includes the first largest language model and the second largest language model.

8. A method for training an intelligent agent, comprising: Obtaining sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word; Based on the sample description information, the first sample prompt word and the second resource analysis step, training a first large language model, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word; Based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, training a second large language model, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question; The trained intelligent agent includes the first largest language model and the second largest language model.

9. An edge resource management device, comprising: An information acquisition module, used to acquire resource management description information used to describe the edge resource management task in natural language; A planning module, used to use the edge resource management agent to plan, based on the resource management description information, the first resource analysis steps required to execute the edge resource management task and the resource analysis order of each of the first resource analysis steps; An execution module, used to use the edge resource management agent to call the target resource analysis tool corresponding to each of the first resource analysis steps to respectively execute each of the first resource analysis steps in accordance with the resource analysis order, and obtain the resource analysis results of each of the first resource analysis steps, wherein the target resource analysis tool is a tool among a plurality of preset resource analysis tools that can execute the corresponding resource analysis step; A result module is used to utilize the edge resource management positioning agent to obtain the execution result of the edge resource management task based on the resource analysis result summary of each of the first resource analysis steps.

10. An intelligent agent training device, comprising: A sample acquisition module, used to acquire sample description information for describing a sample edge resource management problem in natural language, a second resource analysis step required to answer the sample edge resource management problem, resource analysis results of each of the second resource analysis steps, an answer to the sample edge resource management problem, a first sample prompt word, and a second sample prompt word; A first training module is used to train a first large language model based on the sample description information, the first sample prompt word and the second resource analysis step, wherein the first large language model is used to plan the resource analysis steps required to answer the sample edge resource management question under the guidance of the first sample prompt word; A second training module is used to train a second large language model based on the resource analysis results of each of the second resource analysis steps, the second sample prompt words, and the answer to the sample edge resource management question, wherein the second large language model is used to summarize the resource analysis results of each of the second resource analysis steps under the guidance of the second sample prompt words to obtain the answer to the sample edge resource management question; The trained intelligent agent includes the first largest language model and the second largest language model.

11. An edge resource management agent, comprising: An input module, used for receiving input information; a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by executing any one of the methods of claims 1 to 7 by calling the large language model; An output module is used to output the output information obtained by the processing module.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

13. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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