Program information generation method and apparatus, electronic device, and storage medium

Abstract

This application discloses a method, apparatus, electronic device, and storage medium for generating solution information. The method includes: responding to an error request from a client, obtaining error description information from the error request, the error description information describing the error problem; obtaining log information corresponding to the error description information based on the error description information; identifying the log information to obtain path information from the log information; inputting the path information and the error description information into a large language model to obtain target solution information output by the large language model, the target solution information including at least one solution used to guide the handling of the error problem; and sending the target solution information to the client. These steps significantly shorten the fault handling cycle, eliminate reliance on technical personnel, flexibly address large-scale, high-frequency fault handling needs, and directly output solutions for users to reference and handle error problems, thus comprehensively improving fault handling efficiency.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to a method, apparatus, electronic device and storage medium for generating solution information. Background Technology

[0002] In the field of software development and operations, the scale and complexity of software systems are constantly increasing with the iteration of information technology. Faults or code errors during system operation have become commonplace in the industry. If such faults and errors are not handled promptly, they may lead to system malfunctions, service interruptions, or even data loss, directly impacting business continuity and user experience. Therefore, an efficient fault diagnosis and problem-solving mechanism is one of the core requirements for ensuring the smooth progress of software development and operations.

[0003] Currently, the traditional handling process for system failures and code errors in the industry generally relies on manual intervention to complete the entire process. Specifically, when a system malfunctions, users such as developers and testers collect error information; subsequently, these technical personnel need to manually reproduce the failure scenario and retrieve and review system operation logs to troubleshoot the problem.

[0004] The aforementioned existing technical solutions have significant drawbacks, making it difficult to meet the current field's demand for efficient and accurate fault handling. They are slow to respond and have long fault handling cycles; they are heavily reliant on expert resources such as senior technicians and are unable to cope with large-scale, high-frequency faults. Summary of the Invention

[0005] This application provides a solution information generation method, apparatus, electronic device, and storage medium, which can significantly shorten the fault handling cycle, eliminate the need for technical personnel, flexibly cope with large-scale and high-frequency fault handling needs, and directly output solutions for users to refer to and handle error problems, thereby comprehensively improving fault handling efficiency.

[0006] In a first aspect, embodiments of this application provide a method for generating scheme information, the method comprising: In response to an error request from a client, obtain error description information from the error request, wherein the error description information is used to describe the error problem; Based on the error description information, obtain the log information corresponding to the error description information; The log information is identified to obtain the path information within the log information; The path information and the error description information are input into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error problems. The target solution information is sent to the client.

[0007] In one embodiment of this application, the error description information includes a user identifier, error information, and the time when the error occurred; The step of obtaining the log information corresponding to the error description information includes: Scan the error description information; If a target identifier is detected in the error description information, a first query request is sent to the log system so that the log system can obtain the corresponding log information based on the target identifier, the user identifier, the error information and the error occurrence time range carried in the first query request, wherein the error occurrence time range is determined based on the error occurrence time. Alternatively, if no target identifier is found in the error description information, a second query request is sent to the log system so that the log system can obtain the corresponding log information based on the user identifier, the error information, and the time range of the error occurrence carried in the second query request; Receive the log information sent by the log system.

[0008] In one embodiment of this application, the step of identifying the log information to obtain the path information in the log information includes: Obtain the pre-configured first prompt statement, which is a prompt statement related to log query; The log information and the first prompt statement are input into the large language model to obtain the path information in the log information output by the large language model.

[0009] In one embodiment of this application, after identifying the log information and obtaining the path information in the log information, and before inputting the path information and the error description information into a large language model to obtain the target solution information output by the large language model, the method includes: Based on the path information, determine the target code platform corresponding to the path information; A third query request is sent to the target code platform, the third query request carrying the path information, so that the target code platform can find the corresponding code information based on the path information; The system receives feedback information sent by the target code platform. The feedback information includes the code information or a first indication, whereby the first indication indicates that no code information was found.

[0010] In one embodiment of this application, the step of inputting the path information and the error description information into a large language model to obtain the target solution information output by the large language model includes: If the feedback information includes the code information, a pre-configured second prompt statement is obtained, and the path information, the error description information, the code information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model. or, If the feedback information includes the first instruction, a pre-configured second prompt statement is obtained, and the path information, the error description information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model.

[0011] In one embodiment of this application, sending the target solution information to the client includes: If the feedback information includes the code information, the error description information, the log information, the code information, and the target solution information are added to a pre-configured template to generate first target information, and the first target information containing the target solution information is sent to the client. or, If the feedback information includes the first instruction, the error description information, the log information, and the target solution information are added to a pre-configured template to generate second target information, and the second target information containing the target solution information is sent to the client.

[0012] Secondly, embodiments of this application provide a scheme information generation apparatus, the apparatus comprising: The transceiver module is used to respond to the client's error request and obtain the error description information in the error request, wherein the error description information is used to describe the error problem; The acquisition module is used to acquire the log information corresponding to the error description information based on the error description information; The first processing module is used to identify the log information and obtain the path information in the log information; The second processing module is used to input the path information and the error description information into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error problems. The transceiver module is also used to send the target scheme information to the client.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the scheme information generation method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the scheme information generation method as described in the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the scheme information generation method as described in the first aspect.

[0016] This embodiment provides a method, apparatus, electronic device, and storage medium for generating solution information. In response to an error request from a client, it obtains error description information from the error request, which describes the error problem. Based on the error description information, it obtains corresponding log information. It identifies the log information to obtain path information. It inputs the path information and the error description information into a large language model to obtain target solution information output by the large language model. The target solution information includes at least one solution used to guide the handling of the error problem. The target solution information is then sent to the client. These steps offer a fast response time, significantly shortening the fault handling cycle, reducing reliance on expert resources, eliminating the need for technical personnel, and flexibly addressing large-scale, high-frequency fault handling needs. Furthermore, it can directly output solutions for users to reference and handle error problems, comprehensively improving fault handling efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the scheme information generation method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the solution information generation device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0021] In all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. Additionally, when embodiments of this application require access to sensitive personal information, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments obtained.

[0022] To address the problems of the prior art, embodiments of this application provide a method, apparatus, electronic device, and storage medium for generating solution information. The solution information generation method provided in this application embodiment will be described first below.

[0023] Figure 1 A flowchart illustrating a scheme information generation method according to an embodiment of this application is shown. Figure 1 As shown, the solution information generation method provided in this application embodiment is applied to electronic devices, such as servers, and includes the following steps 101-105, wherein: Step 101: In response to the error request from the client, obtain the error description information in the error request, which is used to describe the error problem.

[0024] The executing entity can be an electronic device. The user sends an error request through a client, such as entering an error description in the client interface and clicking the error button, thereby triggering the error request. The electronic device responds to the error request from the client, parses the error request, and obtains the error description information in the error request. The error description information is used to describe the error problem. For example, the error request includes the user identifier, error information, and error time. The user identifier can be a user code, such as an enterprise code, and each user has a unique identifier.

[0025] For example, a user enters on the client interface that our service reported an error around 8:30, user ID: abc123, error message: NullPointerException, and clicks the error report button on the interface to trigger an error report request.

[0026] Among them, 8:30 is the time the error occurred, which is a required field; the error message is a required field; and the user identifier is a required field. There are also some optional fields, such as request identifier, link identifier, and engine code.

[0027] The client reports an error request through the interface module. The interface module receives the error description information and can preprocess the error description information, including encoding conversion and / or sensitive information filtering.

[0028] Step 102: Obtain the log information corresponding to the error description information based on the error description information.

[0029] In this embodiment, log information corresponding to the error description information is obtained from the log system based on the error description information.

[0030] Step 103: Identify the log information to obtain the path information in the log information.

[0031] In this embodiment, log information is identified, and path information is obtained from the log information. The path information is used to query code information, which refers to the source code. Logical errors in the source code cause runtime errors. The purpose of querying the source code is to find the code of the line with the error, so as to trace the root cause and modify the logic to fix the error.

[0032] Step 104: Input the path information and the error description information into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error problems.

[0033] In this embodiment, path information and error description information are input into a Large Language Model (LLM). The LLM analyzes the path information and error description information and outputs target solution information, which includes at least one solution to guide the user in resolving the error problem.

[0034] Step 105: Send the target solution information to the client.

[0035] In this embodiment, the target solution information is sent to the client, which then displays the target solution information for the user to view. The user then performs corresponding processing based on the solution in the target solution information to resolve the error.

[0036] In this embodiment, in response to an error request from the client, the error description information in the error request is obtained. Based on the error description information, the corresponding log information is obtained. The log information is identified to obtain the path information in the log information. Further, the log information and the error description information are input into a large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to indicate the handling of the error problem. The target solution information is sent to the client. In the above steps, the response speed is fast, which can significantly shorten the fault handling cycle, reduce the dependence on expert resources, and eliminate the need to rely on technical personnel. It can flexibly cope with large-scale and high-frequency fault handling needs, and can directly output solutions for users to refer to and handle error problems, thus comprehensively improving fault handling efficiency.

[0037] In one embodiment of this application, the error description information includes a user identifier, error information, and the time when the error occurred; The step of obtaining the log information corresponding to the error description information includes: Scan the error description information; If a target identifier is detected in the error description information, a first query request is sent to the log system so that the log system can obtain the corresponding log information based on the target identifier, the user identifier, the error information and the error occurrence time range carried in the first query request, wherein the error occurrence time range is determined based on the error occurrence time. Alternatively, if no target identifier is found in the error description information, a second query request is sent to the log system so that the log system can obtain the corresponding log information based on the user identifier, the error information, and the time range of the error occurrence carried in the second query request; Receive the log information sent by the log system.

[0038] In this embodiment, the error description information includes the user identifier, error message, and the time when the error occurred.

[0039] The error description information is scanned. Specifically, the error description information is scanned using preset rules to determine whether there is a target identifier in the error description information. The target identifier includes the trace identifier (traceId), engine code (engineCode), and request identifier (requestId).

[0040] If a target identifier is detected in the error description information, a first query request is sent to the log system. The first query request carries the target identifier, user identifier, error information, and error time. The log system queries relevant log information based on the target identifier, user identifier, error information, and error time. By querying the above four pieces of information, more accurate log information can be obtained. The log information sent by the log system is then received.

[0041] Alternatively, if the target identifier is not found in the error description information, a second query request is sent to the log system. The second query request carries the user identifier, error information, and error time. The log system queries the relevant log information based on the user identifier, error information, and error time. The log information sent by the log system can also be queried using the above three pieces of information.

[0042] Optionally, the preset rules can be regular expressions. Multiple regular expressions can be pre-configured to extract structured key information from unstructured error description information, namely the target identifier, trace identifier (traceId), engine code (engineCode), or request identifier (requestId) mentioned above.

[0043] Specifically, the regular expression corresponding to the link identifier is used, such as: (?i)traceId[::]\s The error description information is scanned using the method ([a-z0-9-]{16,}). If the traceId is found to be xyz-789, it means that a link identifier is found in the error description information. If no traceId is found, it means that no link identifier is found in the error description information.

[0044] Furthermore, the error description information is scanned using a regular expression corresponding to the engine code to determine whether the engineCode has been extracted. Then, a regular expression corresponding to the request identifier is used to scan the error description information to determine whether the requestId has been extracted. Multiple regular expressions are used to scan the error description information. If traceId and / or engineCode and / or requestId are present, it indicates that the target identifier has been detected; if traceId, engineCode, and requestId are not present, it indicates that the target identifier has not been detected.

[0045] The error occurrence time range is determined based on the error occurrence time. Specifically, the estimated error end time is obtained by adding a first preset duration to the error occurrence time; the estimated error start time is obtained by subtracting a second preset duration from the error occurrence time. The time range consisting of the estimated error start time and the estimated error end time is the error occurrence time range. For example, if the user provides an error occurrence time of 8:30, the first preset duration is 5 minutes, and the second preset duration is 5 minutes and 59 seconds, the estimated error start time is 8:25:00 and the estimated error end time is 8:35:59. By completing these parameters, the log information at the time of the error can be obtained.

[0046] If the target identifier is a traceId, the electronic device constructs a query statement based on the traceId, user identifier, error information, and error occurrence time range. The first query request includes the query statement, which carries the target identifier, user identifier, error information, and error occurrence time range. The query statement can be: (|where engineCode='abc123' and traceId='xyz-789' and startTime>='8:25:00' and endTime<='8:35:59'| limit 100), where limit limits the number of log results returned in this query to a maximum of 100. If 50 logs meet the conditions, 50 are returned; if 200 logs meet the conditions, only the first 100 are returned; if 0 logs meet the conditions, nothing is returned. This avoids slowing down the query speed due to a large number of results. For example, if there are 10,000 logs that meet the conditions, returning all of them would cause lag. Prioritizing the viewing of the first 100 core logs, where error logs are usually concentrated, meets the needs of rapid troubleshooting.

[0047] If no target identifier exists, the electronic device constructs a query statement based on the user identifier, error information, and error occurrence time range. The second query request includes the query statement, such that the second query request carries the user identifier, error information, and error occurrence time range.

[0048] The method of using regular expressions and rules (such as time completion rules) to extract key information from error descriptions and intelligently complete missing information (such as completing dates) provides accurate parameters for subsequent processes.

[0049] In one embodiment of this application, the step of identifying the log information to obtain the path information in the log information includes: Obtain the pre-configured first prompt statement, which is a prompt statement related to log query; The log information and the first prompt statement are input into the large language model to obtain the path information in the log information output by the large language model.

[0050] In this embodiment, log information returned by the logging system is received. The log information includes multiple lines. In order to quickly locate the path information, a large language model can be used to set a pre-defined first prompt statement. The first prompt statement is a relevant query statement pre-configured for log query. The log information and the first prompt statement are input into the large language model to obtain the path information in the log information output by the large language model. The path information is the source code file path. When the log records an exception (such as a null pointer exception), the path will clearly indicate which class and which line the error occurred in, without having to search through massive amounts of source code one by one.

[0051] Optionally, the first prompt statement could be: "Please extract the main source code file path from the following stack trace information, returning only the path string." The "Please extract the main source code file path from the following stack trace information, returning only the path string" statement and the log information are input into the large language model. The large language model analyzes the stack trace lines in the log information. For example, the returned log information might include: "at com.example.service.UserServiceImpl.getUserInfo(UserServiceImpl.java:125);" indicating that the exception was triggered on line 125 of the UserServiceImpl.java file, specifically the getUserInfo method of the UserServiceImpl class under the com.example.service package. The file path information (filePath) output by the large language model is: com / example / service / UserServiceImpl.java.

[0052] By employing a large language model and setting corresponding prompts, path information can be obtained quickly and accurately from log information.

[0053] In one embodiment of this application, after identifying the log information and obtaining the path information in the log information, and before inputting the path information and the error description information into a large language model to obtain the target solution information output by the large language model, the method includes: Based on the path information, determine the target code platform corresponding to the path information; A third query request is sent to the target code platform, the third query request carrying the path information, so that the target code platform can find the corresponding code information based on the path information; The system receives feedback information sent by the target code platform. The feedback information includes the code information or a first indication, whereby the first indication indicates that no code information was found.

[0054] In this embodiment, the target code platform corresponding to the path information is determined from at least two code platforms based on the path information, wherein the code platforms include GitLab and GitHub.

[0055] A third query request is then sent to the target code platform. The third query request carries path information. The target code platform searches for the corresponding code information based on the path information in the third query request. The target code platform receives feedback information, which includes code information or a first indication. The first indication is used to indicate that no code information was found.

[0056] If the target code platform finds the code information based on the path information, the feedback information sent by the target code platform includes the code information; if the target code platform does not find the relevant code information based on the path information, the feedback information sent by the target code platform includes the first instruction.

[0057] Optionally, the electronic device constructs a query statement based on the path information. The third query request includes the query statement, which carries the path information. The query statement is: ` / api / v4 / projects / {project_id} / repository / files / {file_path} / raw?ref=main`, which retrieves the raw content (raw) of a file (file_path) in the code repository (repository) of the specified project (project_id), specifying the code branch to be read as `main`. The code repository is the target platform mentioned above. Here, `main` is the default branch, or it can be version information. The code information is obtained through a code platform such as GitLab / GitHub. If the retrieval fails, `filePath` is marked as an invalid path, and the source code is set to "none". The code repository is the target code platform mentioned above.

[0058] Obtain code information from the relevant code platform to provide a foundation for subsequent analysis.

[0059] In one embodiment of this application, the step of inputting the path information and the error description information into a large language model to obtain the target solution information output by the large language model includes: If the feedback information includes the code information, a pre-configured second prompt statement is obtained, and the path information, the error description information, the code information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model. or, If the feedback information includes the first instruction, a pre-configured second prompt statement is obtained, and the path information, the error description information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model.

[0060] In this embodiment, when the feedback information includes code information, a pre-configured second prompt statement is obtained. The path information, code information, and error description information are combined with the second prompt statement and input into the large language model to obtain the target solution information output by the large language model.

[0061] Alternatively, if the feedback information includes the first instruction, indicating that no code information is available, a pre-configured second prompt statement is obtained, and the path information, error description information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model.

[0062] Optionally, the second prompt statement includes the following: System command: Imagine you are a senior technical expert; Context: xxxx; Task instructions: If source code is available, combine the log stack trace line number (e.g., java:125) with the source code to pinpoint the line of code causing the error, analyze the context logic, and propose specific code modification solutions (e.g., adding a null value check if (user != null) {…}). If source code is unavailable, based on the log error type and common patterns, propose actionable suggestions (e.g., "Check the url parameter in the database connection configuration application.yml").

[0063] Combine the second prompt statement with the path information, error description information, and code information. Add the path information, error description information, and code information to the context of the second prompt statement. Context: path information, error description information, code information (or "none").

[0064] The aforementioned common patterns include null pointer exceptions (NullPointerException, undefined), unreleased / leaked resources (outOfMemory), configuration errors (service not found), network failures (Timeout), etc.

[0065] Leveraging large language models to obtain solutions can greatly improve the accuracy, reliability, and operability of the solutions. A dual-mode analysis path, with and without source code, employs different analysis strategies (combining line-level code analysis with source code and general log-based analysis only). Robust design ensures uninterrupted fault handling throughout the entire process and consistently outputs effective solutions.

[0066] In one embodiment of this application, sending the target solution information to the client includes: If the feedback information includes the code information, the error description information, the log information, the code information, and the target solution information are added to a pre-configured template to generate first target information, and the first target information containing the target solution information is sent to the client. or, If the feedback information includes the first instruction, the error description information, the log information, and the target solution information are added to a pre-configured template to generate second target information, and the second target information containing the target solution information is sent to the client.

[0067] In this embodiment, when the feedback information includes code information, the error description information, log information, code information, and target solution information are added to a pre-configured template to generate first target information. The first target information is then sent to the client so that the user can view the solution, log information, and code information. The method of carrying code allows the user to view more comprehensive information so as to quickly locate the problem.

[0068] Alternatively, if the feedback information includes the first instruction, the error description, log information, and target solution information can be added to a pre-configured template to generate second target information. This second target information can then be sent to the client so that the user can view the solution and log information, thereby resolving the error.

[0069] Optionally, if the feedback information includes code information, the solution is for the large language model to perform line-by-line analysis combining logs and code information to locate specific classes / methods / line numbers and generate code-level modification schemes; if the feedback information includes a first indication, i.e. no code information, the solution is for the large language model to perform root cause analysis based on log information and propose general suggestions to generate configuration / parameter-level adjustment suggestions.

[0070] Optionally, the pre-configured modules are as follows: ##1. Error Description Information## (User's original description) ##2. Log Information: ## (The raw log returned by the logging system) ##3. Path Information:## (The file path returned by the target code platform; if not found, enter "File not located") ##4. Code Information:## (If source code is available, paste the key snippets including line numbers; if no source code is available, enter "None") ##5. Target Solution Information:## (List each solution item by item).

[0071] Junior developers or support staff can receive solutions comparable to those from senior experts. Meanwhile, the standardized output format ensures the integrity and consistency of the solutions, avoiding the oversights and arbitrariness of manual responses.

[0072] Optionally, the above embodiments can be implemented by an intelligent agent, as shown below: [Perception] ↓ (Enter: Work order text) Understanding & Planning | - Intent Category: Code Error Ticket Information Extraction: Extracting Key Entities | - Task breakdown: Planned as [Check logs → Find files → Read code → Analyze] ↓ [Action Execution] | - Call tool 1: Query_SLS_Error(parameters...) | - Received result: Raw log text | - Using tool 2: llm-model (log text) | - Received result: filePath ↓ (Repeat this process until the process ends) [Evaluation and Learning] | - Check if the results are complete - Proceed to the next step or retry according to the process. ↓ [Output] → Formatted solution In response to the client's error request, the agent retrieves the work order text (i.e., the error description information mentioned above) from the error request. The agent inputs the work order text, classifies the intent of the work order text: it is a code error work order, and extracts key entities: namely, the user identifier, error information, and error occurrence time in the work order text (i.e., the error description information mentioned above includes the user identifier, error information, and error occurrence time). The task is broken down into: checking logs → finding files → reading code → analyzing code.

[0073] The agent invokes tool 1: Query_SLS_Erro, which calls SLS (Simple Log Service) to query for errors, i.e., to obtain log information from the log system (SLS). The parameters are those carried in the first or second query request. The received result is the raw log text (i.e., the log information sent by the log system mentioned above).

[0074] The agent invokes tool 2: llm-model, which calls the large language model. This involves inputting the log information and the first prompt statement into the large language model and obtaining the path information (filePath) from the log information output by the large language model.

[0075] Evaluation: Input the path information and work order text into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error issues. The output is a formatted solution.

[0076] The end-to-end formatted output, from unstructured information to code-level solutions, is a structured document that strictly follows a predetermined template. This document must contain, and in a fixed order, the original error description, the specific code file located, the relevant source code snippets, and a step-by-step solution that can be implemented, so that users can efficiently resolve error issues.

[0077] Figure 2 A structural diagram of the scheme information generation apparatus provided in an embodiment of this application is shown. Figure 2 As shown, the scheme information generation device 200 includes: The transceiver module 201 is used to respond to the error request from the client and obtain the error description information in the error request, wherein the error description information is used to describe the error problem; The acquisition module 202 is used to acquire the log information corresponding to the error description information based on the error description information; The first processing module 203 is used to identify the log information and obtain the path information in the log information; The second processing module 204 is used to input the path information and the error description information into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, and the solution is used to guide the handling of the error problem. The transceiver module 201 is also used to send the target scheme information to the client.

[0078] In one embodiment of this application, the error description information includes a user identifier, error information, and the time when the error occurred; The acquisition module 202 is specifically used to scan the error description information; if a target identifier is found in the error description information, a first query request is sent to the log system, so that the log system can obtain the corresponding log information based on the target identifier, the user identifier, the error information, and the error occurrence time range carried in the first query request, wherein the error occurrence time range is determined based on the error occurrence time; or, if a target identifier is not found in the error description information, a second query request is sent to the log system, so that the log system can obtain the corresponding log information based on the user identifier, the error information, and the error occurrence time range carried in the second query request; and the log information sent by the log system is received.

[0079] In one embodiment of this application, the first processing module 203 is specifically used to obtain a pre-configured first prompt statement, wherein the first prompt statement is a prompt statement related to log query; input the log information and the first prompt statement into a large language model to obtain the path information in the log information output by the large language model.

[0080] In one embodiment of this application, the apparatus further includes: a third processing module; The third processing module is used to determine the target code platform corresponding to the path information based on the path information. The transceiver module is also used to send a third query request to the target code platform, the third query request carrying the path information so that the target code platform can find the corresponding code information based on the path information; The transceiver module is also used to receive feedback information sent by the target code platform. The feedback information includes the code information or a first indication, wherein the first indication is used to indicate that no code information was found.

[0081] In one embodiment of this application, the second processing module is specifically used to obtain a pre-configured second prompt statement when the feedback information includes the code information, and input the path information, the error description information, the code information and the second prompt statement into the large language model to obtain the target solution information output by the large language model; or, If the feedback information includes the first instruction, a pre-configured second prompt statement is obtained, and the path information, the error description information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model.

[0082] In one embodiment of this application, the transceiver module is specifically used to add the error description information, the log information, the code information, and the target solution information to a pre-configured template when the feedback information includes the code information, generate first target information, and send the first target information containing the target solution information to the client; or, If the feedback information includes the first instruction, the error description information, the log information, and the target solution information are added to a pre-configured template to generate second target information, and the second target information containing the target solution information is sent to the client.

[0083] The scheme information generation device provided in this application embodiment can implement all the processes implemented in the aforementioned scheme information generation method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0084] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0085] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0086] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0087] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0088] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of this disclosure.

[0089] The processor 301 implements any of the methods described above in the above embodiments by reading and executing computer program instructions stored in the memory 302.

[0090] In one example, the electronic device may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0091] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0092] Bus 310 includes hardware, software, or both, that couples components of a method or electronic device as described above together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0093] Alternatively, embodiments of this application can be implemented using a computer storage medium. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the information generation methods described in the above embodiments.

[0094] Alternatively, this application embodiment can provide a computer program product for implementation, wherein when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device implements any of the scheme information generation methods in the above embodiments.

[0095] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described as examples. However, the method process of this application is not limited to the specific steps described. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0096] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0097] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0098] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0099] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for generating scheme information, characterized in that, The method includes: In response to an error request from a client, obtain error description information from the error request, wherein the error description information is used to describe the error problem; Based on the error description information, obtain the log information corresponding to the error description information; The log information is identified to obtain the path information within the log information; The path information and the error description information are input into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error problems. The target solution information is sent to the client.

2. The scheme information generation method according to claim 1, characterized in that, The error description information includes the user identifier, the error message, and the time the error occurred; The step of obtaining the log information corresponding to the error description information includes: Scan the error description information; If a target identifier is detected in the error description information, a first query request is sent to the log system so that the log system can obtain the corresponding log information based on the target identifier, the user identifier, the error information and the error occurrence time range carried in the first query request, wherein the error occurrence time range is determined based on the error occurrence time. Alternatively, if no target identifier is found in the error description information, a second query request is sent to the log system so that the log system can obtain the corresponding log information based on the user identifier, the error information, and the time range of the error occurrence carried in the second query request; Receive the log information sent by the log system.

3. The scheme information generation method according to claim 1, characterized in that, The step of identifying the log information to obtain the path information in the log information includes: Obtain the pre-configured first prompt statement, which is a prompt statement related to log query; The log information and the first prompt statement are input into the large language model to obtain the path information in the log information output by the large language model.

4. The scheme information generation method according to claim 1, characterized in that, After identifying the log information and obtaining the path information from the log information, and before inputting the path information and the error description information into the large language model to obtain the target solution information output by the large language model, the method includes: Based on the path information, determine the target code platform corresponding to the path information; A third query request is sent to the target code platform, the third query request carrying the path information, so that the target code platform can find the corresponding code information based on the path information; The system receives feedback information sent by the target code platform. The feedback information includes the code information or a first indication, whereby the first indication indicates that no code information was found.

5. The scheme information generation method according to claim 4, characterized in that, The step of inputting the path information and the error description information into the large language model to obtain the target solution information output by the large language model includes: If the feedback information includes the code information, a pre-configured second prompt statement is obtained, and the path information, the error description information, the code information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model. or, If the feedback information includes the first instruction, a pre-configured second prompt statement is obtained, and the path information, the error description information, and the second prompt statement are input into the large language model to obtain the target solution information output by the large language model.

6. The scheme information generation method according to claim 4, characterized in that, Sending the target solution information to the client includes: If the feedback information includes the code information, the error description information, the log information, the code information, and the target solution information are added to a pre-configured template to generate first target information, and the first target information containing the target solution information is sent to the client. or, If the feedback information includes the first instruction, the error description information, the log information, and the target solution information are added to a pre-configured template to generate second target information, and the second target information containing the target solution information is sent to the client.

7. A scheme information generation device, characterized in that, The device includes: The transceiver module is used to respond to the client's error request and obtain the error description information in the error request, wherein the error description information is used to describe the error problem; The acquisition module is used to acquire the log information corresponding to the error description information based on the error description information; The first processing module is used to identify the log information and obtain the path information in the log information; The second processing module is used to input the path information and the error description information into the large language model to obtain the target solution information output by the large language model. The target solution information includes at least one solution, which is used to guide the handling of error problems. The transceiver module is also used to send the target scheme information to the client.

8. An electronic device, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the scheme information generation method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the scheme information generation method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the scheme information generation method as described in any one of claims 1-6.

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