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CN-121979900-A - Project development data management method, system, equipment and medium based on artificial intelligence

CN121979900ACN 121979900 ACN121979900 ACN 121979900ACN-121979900-A

Abstract

The application relates to a project development data management method, system, equipment and medium based on artificial intelligence. The method comprises the steps of establishing a resource index library based on a preset container mirror image library, combining received task description and project anchor points, generating a development scheme, executing data modification operation in an independent container based on the development scheme, generating a detailed operation log, receiving a process instruction of a user in a project development process, combining the current project state, generating an instruction context, generating a response result through an AI model module, executing an operation capable of being automated in the independent container, generating an instruction execution log, checking whether project resources such as the detailed operation log and the instruction execution log in the independent container are complete when the project development is completed, and packaging and submitting the project resources to a project warehouse if the project resources are complete. By adopting the method, the problems of heterogeneous environment, data dispersion, unsmooth AI interaction and the like in the traditional development can be effectively solved, and the collaboration efficiency is remarkably improved.

Inventors

  • LAI JINYAN

Assignees

  • 赖金燕

Dates

Publication Date
20260505
Application Date
20260202

Claims (9)

  1. 1. An artificial intelligence-based project development data management method, which is characterized by comprising the following steps: Establishing a resource index library based on a preset container mirror library and combining the received task description and project anchor points, wherein each container mirror in the preset container mirror library is preloaded with an AI proxy client; Generating a development scheme through an AI model module according to the resource index library and the task description, and executing data modification operation in an independent container based on the development scheme to generate a detailed operation log; Receiving a process instruction of a user in a project development process, and generating an instruction context based on the process instruction and the current project state; generating a response result by the AI model module based on the process instruction and the instruction context, and executing an operation capable of being automated in the independent container according to the response result to generate an instruction execution log; Receiving a submitting instruction of the user when the project development is completed, and checking whether project resources in the independent container are complete or not based on the submitting instruction to obtain a checking result, wherein the project resources comprise deliverable results, configuration files, test reports, the detailed operation log and the instruction execution log; And if the verification result is that the project resources in the independent container are complete, generating a data storage instruction, wherein the data storage instruction is used for indicating that the project resources are packaged and submitted to the project warehouse.
  2. 2. The method of claim 1, wherein the establishing a resource index library based on the preset container mirror library in combination with the received task description and the project anchor point, wherein each container mirror in the preset container mirror library is preloaded with an AI proxy client, comprises: Acquiring a corresponding container mirror image from a preset container mirror image library according to task description and project anchor points submitted by a user through an operation console; Creating an independent container based on the container image, and activating the independent container based on a security authentication key and an item repository access token to obtain an activated container; and acquiring project basic resources according to the project anchor points through the activated container, and establishing the resource index library based on the project basic resources.
  3. 3. The method of claim 2, wherein the obtaining, by the activated container, project base resources from the project anchor points and building the resource index base based on the project base resources comprises: According to the project warehouse address and the basic branch version in the project anchor point, combining the project warehouse access token, and acquiring a project file from a project warehouse; Carrying out full-scale scanning on the project files through the activated container to obtain a file scanning data set, wherein the file scanning data set comprises file content and metadata of each file, and the metadata comprises file size, modification time and authority setting; Classifying and analyzing the file content to obtain an analysis result, and generating a dependency graph of each project file based on the analysis result, wherein the dependency graph is used for recording references and inclusion relations among files; and constructing the structured resource index library based on the file scanning data set, the analysis result and the dependency graph.
  4. 4. The method of claim 1, wherein generating a development scheme by an AI model module from the resource index library and the task description and performing data modification operations within the independent container based on the development scheme, generating a detailed operation log comprises: Establishing an encrypted interactive link between the AI proxy client and an AI model module through a preset communication protocol and a safety authentication key; packaging the task description and the resource index library to generate a standardized data packet, wherein the standardized data packet comprises a task keyword, a resource path list and a data abstract; Transmitting the standardized data packet to the AI model module through the encrypted interactive link to generate a development scheme, wherein the development scheme comprises a code modification paragraph, a new file template or a configuration adjustment instruction; Analyzing the operation instructions in the development scheme to obtain operation instruction analysis results, and executing data modification in the independent container based on the operation instruction analysis results to obtain all data modification operations corresponding to the development scheme; and generating the detailed operation log based on all the data modification operations, wherein the detailed operation log comprises a time stamp, an operation type and an affected file list.
  5. 5. The method of claim 1, wherein the receiving the process instructions of the user during the project development process and generating an instruction context based on the process instructions and a current project state comprises: Receiving a process instruction text of the user in a project development process, and carrying out intention analysis on the process instruction text to obtain an instruction type, wherein the instruction type comprises a distinguishing resource modification instruction, a query instruction and a test instruction; Acquiring current project state information from the resource index library, wherein the current project state information comprises a recently modified file list, a dependent version and a test result; And generating the instruction context by combining the instruction type and the current project state information, wherein the instruction context comprises a current operation file path, a relevant dependent module and a history modification record.
  6. 6. The method of claim 1, wherein generating, based on the process instructions and the instruction context, response results by the AI model module and performing automatable operations within the independent container in accordance with the response results, generating an instruction execution log comprises: Inputting the process instruction and the instruction context to the AI model module to generate a response result, wherein the response result comprises a concrete modification suggestion, a code segment or a test case; Verifying the response result to obtain a response result verification report, wherein the verification comprises an operation unit test and static code analysis; executing an automatizable operation in the independent container based on the response result verification report to obtain an automatizable operation result, wherein the automatizable operation comprises file replacement and configuration update; And generating the instruction execution log based on the automatizable operation result.
  7. 7. An artificial intelligence based project development data management system, the system comprising: The system comprises an index library construction module, a resource index library, a storage module and a storage module, wherein the index library construction module is used for establishing a resource index library based on a preset container mirror library and combining received task descriptions and project anchors, and each container mirror in the preset container mirror library is preloaded with an AI proxy client; the modification log generation module is used for generating a development scheme through the AI model module according to the resource index library and the task description, and executing data modification operation in an independent container based on the development scheme to generate a detailed operation log; The context generation module is used for receiving a process instruction of a user in a project development process and generating an instruction context based on the process instruction and the current project state; The execution log generation module is used for generating a response result through the AI model module based on the process instruction and the instruction context, executing an operation which can be automated in the independent container according to the response result and generating an instruction execution log; The resource verification module is used for receiving a submission instruction of the user when the project development is completed, and verifying whether project resources in the independent container are complete or not based on the submission instruction to obtain a verification result, wherein the project resources comprise deliverable results, configuration files, test reports, the detailed operation log and the instruction execution log; And the resource storage module is used for generating a data storage instruction if the verification result is that the project resources in the independent container are complete, and the data storage instruction is used for indicating the project resources to be packaged and submitted to the project warehouse.
  8. 8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
  9. 9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

Description

Project development data management method, system, equipment and medium based on artificial intelligence Technical Field The invention belongs to the technical field of project management, and particularly relates to a project development data management method, system, equipment and medium based on artificial intelligence. Background With the deep fusion of artificial intelligence technology in the field of project development, an intelligent development mode based on AI assistance is becoming a key means for improving efficiency. The technology provides intelligent support for development links such as code generation, defect detection, resource optimization and the like by integrating AI capabilities such as a machine learning model, natural language processing and the like, and remarkably reduces the manual intervention intensity. At present, an independent AI tool plug-in is introduced into a local development environment or auxiliary processing of specific tasks is carried out by depending on a cloud AI service interface, so that a basic collaboration mode of 'local environment+external AI service' is formed. In the conventional technology, project data management mainly relies on a version control system (such as Git and SVN) to perform file-level change tracking and collaborative modification. Development team members need to configure the development environment locally, manually pull codes, run AI tools, process the generated results, and submit modified content to a central repository. In this process, the AI model is typically invoked in the form of an external service, whose generated data, intermediate results, and context information are dispersed in the local work directories of the different members, lacking a unified, structured management mechanism. The integration and synchronization of project data is highly dependent on manual operation and third party communication tools, and the AI model and the development environment are in a loose coupling state. However, the current AI auxiliary development mode has the following outstanding problems that the operation conditions of AI tools are inconsistent due to local environment isomerism, the generated data format and quality are uneven, the overall consistency and reproducibility of project data are difficult to ensure, temporary data, scheme suggestions, test results and other intermediate assets generated in the development process of an AI model are lack of effective organization and version management, are easy to lose or conflict in multi-person cooperation and prevent smooth task relay, and in addition, the cutting of AI and development flow also enables data flow to depend on manual transfer, so that the operation complexity is increased, and the end-to-end automatic data management and control from task issuing and intelligent development to result submission cannot be realized. Disclosure of Invention Accordingly, in order to solve the above-mentioned problems, it is necessary to provide an artificial intelligence-based project development data management method capable of realizing intelligent management of the whole life cycle of project data and ensuring data consistency, traceability and safety. In a first aspect, the present application provides an artificial intelligence based project development data management method, comprising: establishing a resource index library based on a preset container mirror image library and combining the received task description and project anchor points, wherein each container mirror image in the preset container mirror image library is preloaded with an AI proxy client; Generating a development scheme through an AI model module according to the resource index library and the task description, and executing data modification operation in an independent container based on the development scheme to generate a detailed operation log; Receiving a process instruction of a user in a project development process, and generating an instruction context based on the process instruction and the current project state; based on the process instruction and the instruction context, generating a response result through the AI model module, and executing an operation which can be automated in the independent container according to the response result to generate an instruction execution log; Receiving a submitting instruction of a user when the project development is completed, and checking whether project resources in the independent container are complete based on the submitting instruction to obtain a checking result, wherein the project resources comprise deliverable results, configuration files, test reports, detailed operation logs and instruction execution logs; if the verification result is that the project resources in the independent container are complete, generating a data storage instruction, wherein the data storage instruction is used for indicating that the project resources are packaged and su