Fault positioning method and device and fault positioning agent

Abstract

The invention provides a fault positioning method and device and a fault positioning 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, the method comprises the steps of obtaining fault description information used for describing a fault positioning task in a natural language; first diagnosis steps needing to be executed for executing the fault positioning task and a diagnosis sequence of the first diagnosis steps are planned and obtained based on the fault description information by utilizing the fault positioning agent; calling a target diagnosis tool corresponding to each first diagnosis step by using the fault positioning agent to execute each first diagnosis step according to the diagnosis sequence to obtain a diagnosis result of each first diagnosis step; and summarizing the diagnosis results of the first diagnosis steps by using the fault positioning agent to obtain an execution result of the fault positioning 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 a fault location method, device, and fault location intelligent agent. Background Art

[0002] In the daily operation and maintenance process, in order to troubleshoot the fault in a timely manner, the operation and maintenance personnel need to locate the cause of the fault. Summary of the invention

[0003] The present disclosure provides a fault location method, device and fault location intelligent agent, which are used to improve the accuracy of fault location.

[0004] According to a first aspect of the present disclosure, a fault location method is provided, comprising:

[0005] Acquire fault description information for describing the fault location task in natural language;

[0006] Using the fault location agent, based on the fault description information, a first diagnostic step required to perform the fault location task and a diagnostic order of each of the first diagnostic steps are planned;

[0007] Using the fault location agent, calling the target diagnostic tool corresponding to each of the first diagnostic steps to respectively execute each of the first diagnostic steps in the diagnostic order to obtain the diagnostic results of each of the first diagnostic steps, wherein the target diagnostic tool is a tool that can execute the corresponding diagnostic step among a plurality of preset diagnostic tools;

[0008] Using the fault location agent, the execution result of the fault location task is obtained based on the diagnosis result summary of each of the first diagnosis steps.

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

[0010] Acquire sample description information for describing a sample fault location question in natural language, a second diagnostic step required to answer the sample fault location question, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location question, 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 diagnostic step, training a first large language model, wherein the first large language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word;

[0012] Based on the diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answer to the sample fault location question, training a second large language model, wherein the second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt words to obtain the answer to the sample fault location 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, a fault location device is provided, comprising:

[0015] An information acquisition module, used to acquire fault description information used to describe the fault location task in natural language;

[0016] A planning module, used to use the fault location agent to plan, based on the fault description information, the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps;

[0017] an execution module, configured to utilize the fault location agent to call a target diagnostic tool corresponding to each of the first diagnostic steps to respectively execute each of the first diagnostic steps in the diagnostic sequence to obtain a diagnostic result of each of the first diagnostic steps, wherein the target diagnostic tool is a tool among a plurality of preset diagnostic tools that can execute the corresponding diagnostic step;

[0018] The result module is used to utilize the fault location agent to obtain the execution result of the fault location task based on the diagnosis result summary of each of the first diagnosis 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 fault location question in natural language, a second diagnostic step required to answer the sample fault location question, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location question, 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 diagnostic step, wherein the first large language model is used to plan the diagnostic steps required to answer the sample fault location 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 diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answer to the sample fault location question, wherein the second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt words to obtain the answer to the sample fault location 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, there is provided a fault localization agent, 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 1 It is a flow chart of a fault location 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 a fault location intelligent agent provided by the present disclosure;

[0037] Figure 4 is a schematic diagram of an architecture for implementing a fault location 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 a fault locating 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 fault location 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 locate the cause of the fault, and related technologies often require manual positioning. Manual positioning requires operation and maintenance personnel to have certain relevant knowledge, which is more difficult for novice operation and maintenance personnel, and may even lead to inaccurate fault positioning due to their limited relevant knowledge reserves.

[0047] Based on this, the present application provides a fault location agent, so that the fault location agent can be used to perform the fault location task and obtain the execution result of the fault location task.

[0048] The fault location agent can perform the fault location task in the following ways: Figure 1 As shown, including:

[0049] Step S101: Acquire fault description information for describing a fault location task in natural language.

[0050] Step S102: utilizing a fault location agent to plan and obtain the first diagnostic steps required to perform the fault location task and the diagnostic sequence of each first diagnostic step based on the fault description information.

[0051] Step S103, using the fault location intelligent agent, calling the target diagnostic tools corresponding to each first diagnostic step to respectively execute each first diagnostic step in a diagnostic sequence to obtain the diagnostic results of each first diagnostic step.

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

[0053] Step S104: utilizing the fault location agent to obtain the execution result of the fault location task based on the diagnosis result summary of each first diagnosis step.

[0054] This embodiment is selected to directly use the pre-trained fault location agent to plan the fault description information, and then call each diagnostic tool to execute each first diagnostic step obtained by the plan, and summarize the diagnostic results of each first diagnostic step to obtain the execution result of the fault location task. In the whole process, the operation and maintenance personnel can realize fault location without manually locating the fault based on their own experience, which effectively improves the accuracy of fault location.

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

[0056] Among them, in step S101, the fault 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 "the cause of the failure of device A" or input the voice "the cause of the failure of device A".

[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 maintenance personnel are all text, but this is only for the convenience of showing the problem description information input by the maintenance personnel. The principle is the same for the case of voice input, which will not be repeated here.

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

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

[0063] Step S1021, using the fault location intelligent agent, calling the first large language model, and inputting the fault description information and the first prompt word into the first large language model, obtaining the first diagnostic steps required to perform the fault location task and the diagnostic order of each first diagnostic step output by the first large language model under the guidance of the first prompt word.

[0064] The first prompt word is used to guide the first language model to perform task 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] Your task is to come up with a short plan that helps me locate the user's problem in a few steps, using at most one of the provided tools for each step. You can create new steps with the help of the following tools.

[0066] Executing a tool can be thought of as: using the tool to test the target, where the target is a sub-target of the original target. You will get a summary of the tool's output.

[0067] You can use {tool_specification}

[0068] Note: log_retrieval needs to rely on the coverage node information to retrieve logs, so you need to execute the cover_check tool first

[0069] {current_date_and_time}

[0070] Construct data according to the planning results. The output can only be in the following JSON format. There should be no comments in the JSON result:

[0071] {{

[0072] "steps": [

[0073] "Step1: xxx",

[0074] "Step2: xxx"

[0075] ],

[0076] "order": "Execution order logic format"

[0077] }}

[0078] Make sure the result can be parsed by Python's json.loads

[0079] Here are some examples.

[0080] Objective: Check the opencdnmt fault of XXX application, the request success rate of Henan Unicom has decreased, and the quality has deteriorated.

[0081] - Thinking: First of all, the request success rate in the target question is an indicator provided by the customer and cannot be directly located, so there is no need to consider it. I need to first confirm whether there are any abnormalities in the basic information such as the overall bandwidth and number of requests of the opencdnmt service; at the same time, obtain the basic information of the fault reporting service, such as the changes in the scheduling coverage information of opencdnmt in Henan Unicom.

[0082] Step 1: Use the metric_check tool to check the bandwidth, number of requests, and status code indicator information of the domain name opencdnmt in Henan Unicom

[0083] Step 2: Use the cover_check tool to check the current coverage changes of the domain name opencdnmt in Henan Unicom and the current bearer information

[0084] - Thinking: Now that I know the coverage and service node information of the opencdnmt domain name at the time of the failure (current), I need to query the basic indicators of the node, such as detection success rate, detection speed, node CPU pressure, etc., to see if there are any abnormalities; at the same time, obtain information on whether the node has abnormal changes at the same time

[0085] Step 3: Use the metric_check tool to detect the current detection success rate, detection speed, and node CPU pressure indicator information of the opencdnmt coverage node

[0086] Step 4: Use the change_check tool to detect recent abnormal changes or events in the opencdnmt coverage node

[0087] - Thinking: In order to achieve the goal, I can execute [Step 1] and [Step 2] at the same time. After these two steps are completed, I can execute [Step 3], so I can construct the operation logic like this:

[0088] Execution order: (Step1 AND Step2) -> (Step3 AND Step4)

[0089] Objective: To investigate why vd3.bdstatic.com Xinjiang Mobile's traffic increased by 502 yesterday.

[0090] - Thinking: I need to first confirm whether the abnormal status code 502 indicator of the vd3.bdstatic.com domain name is abnormal; and confirm the coverage changes of the domain name in Xinjiang Mobile;

[0091] Step 1: Use the metric_check tool to check the 502 status code indicator information of the domain name vd3.bdstatic.com yesterday

[0092] Step 2: Use the cover_check tool to check the coverage change information of the domain name vd3.bdstatic.com in Xinjiang Mobile yesterday

[0093] - Thinking: Now I know that the 502 information generated by the domain name vd3.bdstatic.com yesterday has been covered by the scheduling node at that time; I need to query the basic indicators of the node, such as detection success rate, detection speed, node CPU pressure, etc., to see if there are any abnormalities; at the same time, obtain information on whether the node has abnormal changes at the same time; at the same time, I need to retrieve the domain name log on the node to summarize the log rules

[0094] Step 3: Use the metric_check tool to detect the detection success rate, detection speed, and node CPU pressure indicator information of the above nodes yesterday

[0095] Step 4: Use the log_retrieval tool to retrieve the log information of the domain name vd3.bdstatic.com on the above node yesterday

[0096] Step 5: Use the change_check tool to detect recent abnormal changes or events of vd3.bdstatic.com on the above nodes

[0097] - Thinking: In order to achieve the goal, I can execute [Step 1] and [Step 2] at the same time, and then execute [Step 3] and [Step 4] at the same time, so I can construct the operation logic like this:

[0098] Execution order: (Step1 AND Step2) -> (Step3 AND Step4 AND Step5)

[0099] This is the problem reported by the user: {goal}

[0100] This embodiment is selected to utilize a pre-trained fault location agent, and planning is performed based on the fault 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 first language model to plan tasks can be fully utilized, thereby improving the accuracy of the planned diagnostic steps, and thus making the subsequent execution results based on the diagnostic steps more accurate.

[0101] The first prompt word is only a possible example, and 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 diagnostic steps required to perform the example fault location task and the diagnostic order of each diagnostic step based on the example fault description information, wherein the example fault location task is the fault location task described by the example fault description information.

[0102] For example, the first prompt in the above example, "Goal: Troubleshooting the opencdnmt fault reported by XXX application, the request success rate of Henan Unicom has decreased, and the quality has deteriorated" is the example fault description information, and the following content is the part used to describe the process of planning the diagnostic steps required to perform the example fault location task and the diagnostic order of each diagnostic step based on the example fault description information:

[0103] -Thinking: First of all, the request success rate in the target problem is an indicator provided by the customer and cannot be directly located, so there is no need to consider it. I need to first confirm whether there are any abnormalities in the basic information such as the overall bandwidth and number of requests of the opencdnmt service; at the same time, obtain the basic information of the fault reporting service, such as the changes in the scheduling coverage information of opencdnmt in Henan Unicom.

[0104] Step 1: Use the metric_check tool to check the bandwidth, number of requests, and status code indicator information of the domain name opencdnmt in Henan Unicom

[0105] Step 2: Use the cover_check tool to check the current coverage changes of the domain name opencdnmt in Henan Unicom and the current bearer information

[0106] - Thinking: Now that I know the coverage and service node information of the opencdnmt domain name at the time of the failure (current), I need to query the basic indicators of the node, such as detection success rate, detection speed, node CPU pressure, etc., to see if there are any abnormalities; at the same time, obtain information on whether the node has abnormal changes at the same time

[0107] Step 3: Use the metric_check tool to detect the current detection success rate, detection speed, and node CPU pressure indicator information of the opencdnmt coverage node

[0108] Step 4: Use the change_check tool to detect recent abnormal changes or events in the opencdnmt coverage node

[0109] - Thinking: In order to achieve the goal, I can execute [Step 1] and [Step 2] at the same time. After these two steps are completed, I can execute [Step 3], so I can construct the operation logic like this:

[0110] Execution order: (Step1 AND Step2) -> (Step3 AND Step4)

[0111] Objective: To investigate why vd3.bdstatic.com Xinjiang Mobile's traffic increased by 502 yesterday.

[0112] - Thinking: I need to first confirm whether the abnormal status code 502 indicator of the vd3.bdstatic.com domain name is abnormal; and confirm the coverage changes of the domain name in Xinjiang Mobile;

[0113] Step 1: Use the metric_check tool to check the 502 status code indicator information of the domain name vd3.bdstatic.com yesterday

[0114] Step 2: Use the cover_check tool to check the coverage change information of the domain name vd3.bdstatic.com in Xinjiang Mobile yesterday

[0115] - Thinking: Now I know that the 502 information generated by the domain name vd3.bdstatic.com yesterday has been covered by the scheduling node at that time; I need to query the basic indicators of the node, such as detection success rate, detection speed, node CPU pressure, etc., to see if there are any abnormalities; at the same time, obtain information on whether the node has abnormal changes at the same time; at the same time, I need to retrieve the domain name log on the node to summarize the log rules

[0116] Step 3: Use the metric_check tool to detect the detection success rate, detection speed, and node CPU pressure indicator information of the above nodes yesterday

[0117] Step 4: Use the log_retrieval tool to retrieve the log information of the domain name vd3.bdstatic.com on the above node yesterday

[0118] Step 5: Use the change_check tool to detect recent abnormal changes or events of vd3.bdstatic.com on the above nodes

[0119] - Thinking: In order to achieve the goal, I can execute [Step 1] and [Step 2] at the same time, and then execute [Step 3] and [Step 4] at the same time, so I can construct the operation logic like this:

[0120] Execution order: (Step1 AND Step2) -> (Step3 AND Step4 AND Step5)

[0121] In step S103, the number and type of the preset diagnostic tools may vary according to different application scenarios. In addition, in a possible embodiment, the target diagnostic tools corresponding to each first diagnostic step may be obtained in the following manner:

[0122] Inputting the fault description information, the first prompt word, and the description information of each of the plurality of preset diagnostic tools into the first large language model, obtaining the first diagnostic steps required to perform the fault location task and the diagnostic sequence of each first diagnostic step output by the first large language model under the guidance of the first prompt word, and the target diagnostic tool corresponding to each first diagnostic step output by the first large language model under the guidance of the first prompt word

[0123] By selecting this embodiment, the fault location intelligent agent can grasp the capabilities of each diagnostic tool based on the description information of each diagnostic tool, thereby more accurately calling the diagnostic tool with corresponding capabilities to perform the first diagnostic step, thereby improving the accuracy of the diagnostic results, and further improving the accuracy of the subsequent execution results obtained based on the summary of the diagnostic results.

[0124] 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 diagnostic tool corresponding to each first diagnostic step, the first prompt word can be used to describe the precautions when calling each diagnostic tool. For example, the first prompt word in the aforementioned example, "Note: log_retrieval log retrieval needs to rely on coverage node information, so the cover_check tool needs to be executed first" is used to describe the precautions when calling each diagnostic tool.

[0125] In step S104, the fault localization agent may summarize based on the pre-learned knowledge on how to summarize the diagnosis results of each diagnosis step. When summarizing, the fault localization agent may be implemented based on any algorithm model with summarization capability.

[0126] Exemplarily, in a possible embodiment, step S104 includes:

[0127] Step S1041, using the fault location agent, calling the second largest language model, and inputting the diagnosis results of each first diagnosis step and the second prompt word into the second largest language model, to obtain the execution result of the fault location task output by the second largest language model under the guidance of the second prompt word.

[0128] 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.

[0129] This embodiment is selected to utilize a pre-trained fault location agent and perform summarization through the trained second language model combined with the second prompt word. With 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.

[0130] The execution result obtained in step S104 can be used to represent the location result of the fault indicated by the fault location task, and can also be used to represent the location result and the solution to the fault. It is understandable that in some application scenarios, the operation and maintenance personnel are only concerned about the cause of the fault (i.e., the location result), while in other application scenarios, inexperienced operation and maintenance personnel are also concerned about how to solve the fault. Therefore, the location result is represented by the execution result, or the location result plus the solution, which can meet the needs of different operation and maintenance personnel in different situations.

[0131] The following will explain how to train a fault location agent. Figure 3 , Figure 3 The figure is a flow chart of the training method of the fault location agent provided by the present application, including:

[0132] Step S301, obtaining sample description information for describing the sample fault location problem in natural language, the second diagnostic steps required to answer the sample fault location question, the diagnostic results of each second diagnostic step, the answer to the sample fault location question, the first sample prompt word and the second sample prompt word.

[0133] Step S302: training a first language model based on the sample description information, the first sample prompt word and the second diagnosis step.

[0134] The first language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word.

[0135] Step S303: training a second language model based on the diagnosis results of each second diagnosis step, the second sample prompt words, and the answers to the sample fault location questions.

[0136] The second largest language model is used to summarize the diagnosis results of each second diagnosis step under the guidance of the second sample prompt word to obtain the answer to the sample fault location problem.

[0137] The trained fault location agent includes a first language model and a second language model.

[0138] 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 diagnostic 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.

[0139] In step S303, the diagnosis results of each second diagnostic step and the second sample prompt word may 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 fault location question may be 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 may be adjusted according to the loss.

[0140] By selecting this embodiment, the fault location process of the operation and maintenance personnel can be integrated into the function of the fault location intelligent agent by training the first largest language model and the second largest language model, so that fault location can be performed through the fault location intelligent agent, thereby improving the accuracy of fault location.

[0141] In the above example, the process of implementing fault location based on the fault location agent is described. In order to more clearly describe the fault location agent provided by the present application, the software architecture for implementing fault location is described below. For example, see Figure 4 , Figure 4 The figure shows the software architecture for implementing the fault location method provided in the present application.

[0142] Among them, the conversational front end can be Figure 2a As shown, the service number of the user interface layer can be Figure 2b As shown, the super assistant can Figure 2c shown.

[0143] The fault location in the intelligent assistant layer is the aforementioned fault location agent.

[0144] The perception, planning, execution and reflection of the fault location layer are the four stages of the fault location agent, wherein 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 fault location agent to optimize itself based on its own output.

[0145] exist Figure 4 In the example shown, the perception phase includes intent recognition, parameter extraction, and abnormal data perception, where abnormal data perception refers to the discovery of abnormal data. The planning phase includes execution chain generation, missing parameter tracing, and multi-round planning. The execution phase includes parameter acquisition, task scheduling execution, log retrieval through tools, indicator query, event query, etc. The reflection phase includes tool result summary, planning process summary, and intelligent suggestions.

[0146] based on Figure 4 Based on the framework shown, the fault localization agent can achieve the following functions:

[0147] Data-driven model training: Utilizes a large amount of historical failure data and log information for training, enabling the agent to identify different failure modes and characteristics.

[0148] Continuous learning and optimization: By continuously updating and optimizing the model, the latest fault data and solutions are introduced to maintain the advancement and accuracy of diagnostic capabilities.

[0149] Intelligent prompt generation: Combined with natural language processing technology, the prompt vocabulary is optimized to enable the intelligent agent to generate accurate diagnostic prompts based on the fault context.

[0150] Dynamic prompt adjustment: Dynamically adjust prompt words based on real-time feedback during troubleshooting to ensure the relevance and effectiveness of prompt information.

[0151] Data fusion and analysis: Integrate data from different monitoring systems, log files, and network traffic for comprehensive analysis to improve the accuracy of fault location.

[0152] Real-time data processing: Use streaming data processing technology to quickly analyze real-time data and promptly discover and locate faults.

[0153] Automated fault detection: The agent can automatically identify abnormal behaviors and fault signals, and quickly trigger the fault diagnosis process.

[0154] Decision support system: Provides detailed fault analysis reports and solution suggestions to assist operation and maintenance personnel in making decisions, and can automatically execute some solutions.

[0155] User-friendly interface design: Design an intuitive user interface so that operators can easily interact with the agent and obtain the required fault information and suggestions.

[0156] Automated tool chain integration: Seamlessly integrate with existing operation and maintenance tools and processes to automate the troubleshooting and resolution process.

[0157] Corresponding to the above-mentioned fault location method, the present application also provides an intelligent agent training method, such as Figure 5 As shown, including:

[0158] Step S501, obtaining sample description information for describing a sample fault location problem in natural language, a second diagnostic step required to answer the sample fault location problem, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location problem, a first sample prompt word, and a second sample prompt word.

[0159] Step S502: training a first language model based on the sample description information, the first sample prompt word and the second diagnosis step.

[0160] The first language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word.

[0161] Step S503: training a second language model based on the diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answers to the sample fault location questions.

[0162] The second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt word to obtain the answer to the sample fault location problem.

[0163] The trained intelligent agent includes the first largest language model and the second largest language model.

[0164] Corresponding to the above-mentioned fault location method, the present application also provides a fault location device, such as Figure 6 As shown, including:

[0165] An information acquisition module 601 is used to acquire fault description information used to describe the fault location task in natural language;

[0166] A planning module 602 is used to use the fault location agent to plan, based on the fault description information, the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps;

[0167] An execution module 603 is used to use the fault location agent to call the target diagnostic tool corresponding to each of the first diagnostic steps to respectively execute each of the first diagnostic steps in the diagnostic order to obtain the diagnostic results of each of the first diagnostic steps, wherein the target diagnostic tool is a tool that can execute the corresponding diagnostic step among a plurality of preset diagnostic tools;

[0168] The result module 604 is used to utilize the fault location agent to obtain the execution result of the fault location task based on the diagnosis result summary of each of the first diagnosis steps.

[0169] In a possible embodiment, the execution result is used to indicate the result of locating the fault indicated by the fault locating task, or the execution result is used to indicate the result of locating the fault and a solution to the fault.

[0170] In a possible embodiment, the planning module uses the fault location agent to plan, based on the fault description information, the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps, including:

[0171] Utilizing the fault location agent, the first large language model is called, and the fault description information and the first prompt word are input into the first large language model to obtain the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps output by the first large language model under the guidance of the first prompt word.

[0172] In a possible embodiment, the first prompt word is used to describe the process of planning the diagnostic steps required to execute an example fault location task and the diagnostic order of each diagnostic step based on the example fault description information, and the example fault location task is the fault location task described by the example fault description information.

[0173] In a possible embodiment, the planning module inputs the fault description information and the first prompt word into the first large language model, and obtains the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps output by the first large language model under the guidance of the first prompt word, including:

[0174] The fault description information, the first prompt word, and the description information of each of the multiple preset diagnostic tools are input into the first large language model to obtain the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps, which are output by the first large language model under the guidance of the first prompt word, and the target diagnostic tools corresponding to each of the first diagnostic steps, which are output by the first large language model under the guidance of the first prompt word.

[0175] In a possible embodiment, the first prompt word is used to describe the precautions when calling each of the diagnostic tools.

[0176] In a possible embodiment, the using the fault localization agent to obtain the execution result of the fault localization task based on the diagnosis result summary of each of the first diagnosis steps includes:

[0177] Utilizing the fault location agent, the second largest language model is called, and the diagnosis results of each of the first diagnostic steps and the second prompt word are input into the second largest language model to obtain the execution result of the fault location task output by the second largest language model under the guidance of the second prompt word.

[0178] In a possible embodiment, the training process of the fault location agent includes:

[0179] Acquire sample description information for describing a sample fault location question in natural language, a second diagnostic step required to answer the sample fault location question, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location question, a first sample prompt word, and a second sample prompt word;

[0180] Based on the sample description information, the first sample prompt word and the second diagnostic step, training a first large language model, wherein the first large language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word;

[0181] Based on the diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answer to the sample fault location question, training a second large language model, wherein the second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt words to obtain the answer to the sample fault location question;

[0182] The trained fault location agent includes the first largest language model and the second largest language model.

[0183] 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:

[0184] The sample acquisition module 701 is used to acquire sample description information for describing a sample fault location question in natural language, a second diagnostic step required to answer the sample fault location question, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location question, a first sample prompt word, and a second sample prompt word;

[0185] 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 diagnostic step, wherein the first large language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word;

[0186] A second training module 703 is used to train a second large language model based on the diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answer to the sample fault location question, wherein the second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt words to obtain the answer to the sample fault location question;

[0187] The trained intelligent agent includes the first largest language model and the second largest language model.

[0188] The present application also provides a fault location intelligent agent, including:

[0189] An input module, used for receiving input information;

[0190] 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 execute any of the above-mentioned fault location methods by calling the large language model to obtain output information;

[0191] An output module is used to output the output information obtained by the processing module.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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 a fault location method or an agent training method. For example, in some embodiments, the fault location method or the 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 fault location method or the agent training method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the fault localization method or the intelligent agent training method in any other appropriate manner (eg, by means of firmware).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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. A fault location method, comprising: Acquire fault description information for describing the fault location task in natural language; Using a fault location agent, based on the fault description information, a first diagnostic step required to perform the fault location task and a diagnostic order of each of the first diagnostic steps are planned; Using the fault location agent, calling the target diagnostic tool corresponding to each of the first diagnostic steps to respectively execute each of the first diagnostic steps in the diagnostic order to obtain the diagnostic results of each of the first diagnostic steps, wherein the target diagnostic tool is a tool that can execute the corresponding diagnostic step among a plurality of preset diagnostic tools; The fault location agent is used to obtain the execution result of the fault location task based on the diagnosis result summary of each of the first diagnosis steps.

2. The method according to claim 1, wherein: The execution result is used to indicate the result of locating the fault indicated by the fault locating task, or the execution result is used to indicate the result of locating the fault and a solution to the fault.

3. The method according to claim 1, wherein: The method of using the fault location agent to plan and obtain the first diagnostic steps required to perform the fault location task and the diagnostic sequence of each of the first diagnostic steps based on the fault description information includes: Utilizing the fault location agent, the first large language model is called, and the fault description information and the first prompt word are input into the first large language model to obtain the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps output by the first large language model under the guidance of the first prompt word.

4. The method according to claim 3, wherein: The first prompt word is used to describe the process of planning the diagnostic steps required to execute an example fault location task and the diagnostic order of each diagnostic step based on the example fault description information. The example fault location task is the fault location task described by the example fault description information.

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

6. The method according to claim 5, wherein: The first prompt word is used to describe the precautions when calling each of the diagnostic tools.

7. The method according to claim 1, wherein: The using the fault location agent to obtain the execution result of the fault location task based on the diagnosis result summary of each of the first diagnosis steps includes: Utilizing the fault location agent, the second largest language model is called, and the diagnosis results of each of the first diagnostic steps and the second prompt word are input into the second largest language model to obtain the execution result of the fault location task output by the second largest language model under the guidance of the second prompt word.

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

9. A method for training an intelligent agent, comprising: Acquire sample description information for describing a sample fault location question in natural language, a second diagnostic step required to answer the sample fault location question, a diagnostic result of each of the second diagnostic steps, an answer to the sample fault location question, 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 diagnostic step, training a first large language model, wherein the first large language model is used to plan the diagnostic steps required to answer the sample fault location question under the guidance of the first sample prompt word; Based on the diagnosis results of each of the second diagnosis steps, the second sample prompt words, and the answer to the sample fault location question, training a second large language model, wherein the second large language model is used to summarize the diagnosis results of each of the second diagnosis steps under the guidance of the second sample prompt words to obtain the answer to the sample fault location question; The trained intelligent agent includes the first largest language model and the second largest language model.

10. A fault location device, comprising: An information acquisition module, used to acquire fault description information used to describe the fault location task in natural language; A planning module, used to use the fault location agent to plan, based on the fault description information, the first diagnostic steps required to perform the fault location task and the diagnostic order of each of the first diagnostic steps; an execution module, configured to utilize the fault location agent to call a target diagnostic tool corresponding to each of the first diagnostic steps to respectively execute each of the first diagnostic steps in the diagnostic sequence to obtain a diagnostic result of each of the first diagnostic steps, wherein the target diagnostic tool is a tool among a plurality of preset diagnostic tools that can execute the corresponding diagnostic step; The result module is used to utilize the fault location agent to obtain the execution result of the fault location task based on the diagnosis result summary of each of the first diagnosis steps.

11. A fault location intelligent 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 8 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 1-8.

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 8.

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