Automatic report generation method based on large model Agent

Abstract

The invention discloses an automatic report generation method based on a large model Agent, which comprises the following steps: user demand input: transmitting a demand provided by a user to the large model Agent for processing to obtain an executable code; the generated executable code is used as core logic of an application program, and a system mirror image is generated through a virtualization technology; the system mirror image is mounted in the cloud native service platform through the MCP mirror image mounting service; database operation and data table generation; performing intelligent analysis on the result data by using a large model knowledge base; an analysis result is transmitted to an MCP data visualization service for data visualization; the final results are summarized to form a report with illustrations and texts; and exporting the report. According to the method, the data analysis report can be efficiently and automatically generated, manual intervention is reduced, the working efficiency is improved, meanwhile, high expandability and flexibility are achieved, and the method is suitable for various data analysis tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of automated report generation, and particularly to an automated report generation method based on a large model Agent. Background Art

[0002] With the continuous development of data analysis and artificial intelligence technologies, many fields require the rapid and efficient generation of graphic reports. Traditional report generation methods usually require manual coding, database operation, and manual report production. This method is not only time-consuming and laborious but also error-prone. Therefore, how to automatically generate reports from users' text or voice requirements has become an important technical problem. At present, preliminary progress has been made in task orchestration and automated analysis based on large model Agents, but most of them focus on a single link of code generation or data analysis. There is a lack of a comprehensive system that can combine all links to achieve a fully automated workflow from requirement input to final report output. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide an automated report generation method based on a large model Agent, which can efficiently and automatically generate data analysis reports, reduce manual intervention, improve work efficiency, and at the same time has high scalability and flexibility, and is applicable to various data analysis tasks.

[0004] The present invention solves the technical problems by adopting the following technical solutions:

[0005] An automated report generation method based on a large model Agent includes the following steps:

[0006] Step S1, user requirement input: The input source of the system is the requirements provided by the user;

[0007] Step S2, large model Agent orchestration and conversion into executable code: The requirements provided by the user are passed to the large model Agent for processing to obtain executable code;

[0008] Step S3, image generation: The generated executable code will be used as the core logic of the application program and run in a virtualized environment. To ensure that the executable code can run stably in different environments, a system image is generated through virtualization technology;

[0009] Step S4, MCP image mounting service: The system image is mounted to the cloud native service platform through the MCP image mounting service for subsequent operations;

[0010] Step S5, database operation and data table generation;

[0011] Step S6, Big Model Knowledge Base Data Analysis: After obtaining the database calculation results, the system will use the big model knowledge base to perform intelligent analysis on the result data;

[0012] Step S7, MCP Service Data Visualization: After performing data analysis, the system will pass the analysis results to the MCP data visualization service for data visualization;

[0013] Step S8, Report Generation: After data analysis and visualization are generated, the system will summarize the final results to form a report with pictures and texts;

[0014] Step S9, Export Report: The finally generated report can be viewed in the big model Agent service and can also be exported as different text formats with the help of the MCP file generation service, which is convenient for users to use.

[0015] Furthermore, in Step S1, the user submits requirements by text or voice:

[0016] Text Requirement: The user inputs text through the system interface;

[0017] Voice Requirement: The user uses speech recognition technology to convert speech into text form and then inputs it into the system; after the speech recognition module converts the speech into text, it passes it to the subsequent processing module.

[0018] Furthermore, in Step S2, the big model Agent processing includes:

[0019] Requirement Analysis: The big model Agent analyzes the user's requirements based on the input text or voice and extracts key information;

[0020] Task Orchestration: The big model Agent orchestrates the obtained information into executable steps;

[0021] Code Generation: The big model Agent converts the parsed requirements into corresponding code snippets and generates SQL query statements, data processing code, and visualization code according to the type of requirements.

[0022] Furthermore, the executable steps include: code generation, image generation, image mounting, database result data calculation, result data analysis based on the knowledge base, data visualization, and data summarization.

[0023] Furthermore, in Step S3, generating the system image includes:

[0024] Image Generation: The system uses virtualization technology to package the executable code and its dependent environment into a system image;

[0025] Image Configuration: The system image will contain a complete running environment configuration to ensure that the generated code can be successfully executed in a container or virtual machine.

[0026] Further, in step S4, the mounting of the MCP mirror to the cloud-native service platform includes:

[0027] Cloud-native service: The system image is deployed in the cloud-native service platform using the cloud computing platform. The cloud-native service platform can automatically schedule and manage the operation of the image to ensure that the system can execute at any time according to requirements;

[0028] Mirror mounting: The generated system image is mounted to the cloud platform through the MCP service to ensure that it has sufficient computing resources for task execution.

[0029] Further, in step S5, database operations and data table generation include:

[0030] Database connection: The generated code will establish a connection with the data source to obtain the required raw data;

[0031] Data calculation: According to the user's needs, corresponding database operations are performed; the generated data table is based on the calculation results of the needs.

[0032] Further, in step S6, after obtaining the database calculation results, the system will use the large model knowledge base to perform intelligent analysis on the result data, including:

[0033] Knowledge base query: The system passes the generated data table to the large model knowledge base. The large model Agent deeply analyzes the data by accessing the existing knowledge base and obtains insights meaningful to the business;

[0034] Automated analysis: The large model Agent automatically discovers abnormal trends, association rules, and potential problems in the data based on the information in the data table; the model Agent can also generate a natural language analysis report for the data.

[0035] Further, in step S7, data visualization includes: The MCP service will generate charts and other visual elements according to the analysis results.

[0036] Further, in step S8, report integration is also performed: The large model Agent combines the analysis results, the generated data table, and the visual charts into a complete report.

[0037] An automated report generation method based on a large model Agent disclosed by the present invention has the following beneficial effects:

[0038] High degree of automation: The large model Agent converts the user's needs into executable code and automatically executes the entire process. From requirement to report generation, it is completely automated, reducing manual operations.

[0039] High integration: Integrates multiple technical aspects such as code generation, image generation, database operations, knowledge base analysis, and data visualization, providing an integrated solution.

[0040] Improve efficiency: Can quickly generate reports with pictures and texts, greatly improving the efficiency of data analysis and report generation.

[0041] Flexibility: Supports inputting requirements in two ways, text and voice, to meet the needs of different users.

[0042] Strong scalability: The system can be expanded according to user needs and supports the output of reports in multiple formats. Description of the Drawings

[0043] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] The objective of the present invention is to provide an automated report generation method based on a large model Agent. The large model Agent can convert the text or voice requirements provided by the user into tasks and generate executable code, generate a system image through virtualization technology, execute the MCP image mounting service operation, automatically analyze the result data with the help of the large model, and finally perform data visualization through the MCP service, and finally generate a report containing picture and text content and support export to different text formats.

[0046] Reference Figure 1 , an automated report generation method based on a large model Agent disclosed by the present invention, includes the following steps:

[0047] Step S1, user requirement input: The input source of the system is the requirements provided by the user, and the user can submit requirements in text or voice.

[0048] Text requirement: The user inputs text through the system interface; for example, input a paragraph describing the requirements for the task to be completed (for example: "Generate a report on the sales data in 2020").

[0049] Voice Requirement: The user converts the voice into text through speech recognition technology and inputs it into the system; after the speech recognition module converts the voice into text, it is passed to the subsequent processing module.

[0050] Step S2, Large Model Agent Orchestration and Conversion to Executable Code: The requirements provided by the user are passed to the large model Agent for processing to obtain executable code;

[0051] Requirement Analysis: The large model Agent analyzes the user's requirements based on the input text or voice and extracts key information; such as required functions, data sources, analysis logic, etc.

[0052] Task Orchestration: The large model Agent orchestrates the obtained information into executable steps, such as code generation, image generation, image mounting, database result data calculation, result data analysis based on the knowledge base, data visualization, and data aggregation, etc.;

[0053] Code Generation: The large model Agent converts the parsed requirements into corresponding code snippets and generates SQL query statements, data processing code, visualization code, etc. according to the type of requirements. For example, if the user requests to generate a sales data analysis report, the model will generate code for extracting sales data, calculating aggregated data, and visualizing it.

[0054] Step S3, Image Generation: The generated executable code will serve as the core logic of the application and run in a virtualized environment. To ensure that the executable code can run stably in different environments, a system image is generated through virtualization technology;

[0055] Image Generation: The system uses virtualization technology (such as Docker, virtual machines, etc.) to package the executable code and its dependent environment into a system image; these dependencies may include the operating system, runtime environment, required libraries, and tools, etc.

[0056] Image Configuration: The system image will contain a complete running environment configuration to ensure that the generated code can be successfully executed in a container or virtual machine.

[0057] Step S4, MCP Image Mounting Service: The system image is mounted into the cloud-native service platform through the MCP image mounting service for subsequent operations;

[0058] Cloud-Native Service: Use a cloud computing platform (such as container orchestration tools like Kubernetes, etc.) to deploy the system image in the cloud-native service platform. The cloud-native service platform can automatically schedule and manage the operation of the image to ensure that the system can execute according to requirements at any time;

[0059] Mirror Mounting: Mount the generated system image to the cloud platform through the MCP service to ensure that it has sufficient computing resources (such as CPU, memory, storage, etc.) for task execution.

[0060] Step S5, Database Operations and Data Table Generation;

[0061] The code in the image will start to execute, mainly involving database operations:

[0062] Database Connection: The generated code will establish a connection with the data source (such as relational databases, NoSQL databases, etc.) to obtain the required raw data; the data source can be an enterprise internal database or other external data interfaces.

[0063] Data Calculation: According to the user's requirements, perform corresponding database operations; such as querying, filtering, summarizing, calculating, etc. The generated data table is based on the calculation results of the requirements. For example, sales data may include monthly aggregated sales amounts, sales quantities of different products, etc.

[0064] Step S6, Big Model Knowledge Base Data Analysis: After obtaining the database calculation results, the system will use the big model knowledge base to perform intelligent analysis on the result data;

[0065] Knowledge Base Query: The system passes the generated data table to the big model knowledge base, and the big model Agent conducts in-depth analysis on the data by accessing the existing knowledge base to obtain insights meaningful to the business;

[0066] Automated Analysis: The big model Agent automatically discovers abnormal trends, association rules, potential problems, etc. in the data based on the information in the data table; the model Agent can also generate a natural language analysis report for the data.

[0067] Step S7, MCP Service Data Visualization: After performing data analysis, the system passes the analysis results to the MCP data visualization service for data visualization;

[0068] The MCP service will generate charts (such as bar charts, line charts, pie charts, etc.) and other visualization elements (such as heat maps, maps, etc.) based on the analysis results. Data visualization can help users more intuitively understand data trends and potential business opportunities.

[0069] Step S8, Report Generation: After data analysis and visualization are generated, the system summarizes the final results to form a report with pictures and texts;

[0070] Report Integration is also performed: The big model Agent combines the analysis results, the generated data tables, and the visualization charts into a complete report. The report format can be any format required by the user (such as PDF, Word, HTML, etc.).

[0071] Step S9, export the report: The finally generated report can be viewed in the large model Agent service and exported in different text formats by means of the MCP file generation service, which is convenient for users to use.

[0072] The present invention provides an automated report generation method based on a large model Agent. After the user inputs requirements through text or voice, the system uses the large model Agent to parse the requirements and orchestrate tasks, and at the same time generates executable code. The code is packaged into an image through virtualization technology and executed on the cloud platform through the MCP image mounting service. The system automatically extracts relevant data from the database, performs data processing and analysis, and conducts intelligent analysis through the large model, and finally generates a report with both pictures and texts. The system includes a requirement input module, a large model Agent orchestration and conversion to executable code module, an image generation and deployment module, a database operation module, a data analysis and visualization module, and a report generation and export module. The working principle is: user inputs requirements → large model Agent task orchestration → parses and generates code → the system packages into an image and deploys → executes database query and data processing → intelligent analysis and visualization → automatically generates a report and exports it. This technical solution can efficiently and automatically generate data analysis reports, reduce manual intervention, improve work efficiency, and at the same time has high scalability and flexibility, and is applicable to various data analysis tasks.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated report generation method based on a large model Agent, characterized in that, The steps include: Step S1, user demand input: the system input source is the demand provided by the user; Step S2: The large model agent arranges and converts the code into executable code: The requirements provided by the user are passed to the large model agent for processing to obtain the executable code; Step S3, image generation: The generated executable code will serve as the core logic of the application and run in a virtualized environment. To ensure that the executable code can run stably in different environments, a system image is generated using virtualization technology; Step S4, MCP image mounting service: The system image is mounted to the cloud native service platform through the MCP image mounting service for subsequent operations; Step S5, database operation and data table generation; Step S6, large model knowledge base data analysis: After obtaining the database calculation results, the system will use the large model knowledge base to perform intelligent analysis on the result data; Step S7, MCP service data visualization: After performing data analysis, the system passes the analysis results to the MCP data visualization service for data visualization; Step S8, report generation: After data analysis and visualization generation, the system summarizes the final results and forms a report with both pictures and text; Step S9, exporting the report: The final generated report can be viewed in the large model Agent service and exported to different text formats with the help of the MCP file generation service for user convenience.

2. The automated report generation method based on the large model Agent according to claim 1, wherein In step S1, the user submits the request via text or voice: Text requirements: users input text through the system interface; Voice requirements: Users use voice recognition technology to convert voice into text and then input it into the system; the voice recognition module converts the voice into text and passes it to the subsequent processing module.

3. The method for generating an automated report based on a large model agent according to claim 2, characterized in that: In step S2, the large model agent processing includes: Demand analysis: The large model agent analyzes user needs based on input text or voice and extracts key information; Task orchestration: The large model agent orchestrates the acquired information into executable steps; Code generation: The large model agent converts the parsed requirements into corresponding code snippets and generates SQL query statements, data processing code, and visualization code based on the type of requirements.

4. The automated report generation method based on the large model Agent according to claim 3, characterized in that, The executable steps include: code generation, image generation, image mounting, database result data calculation, knowledge base-based result data analysis, data visualization and data aggregation.

5. The automated report generation method based on the large model Agent according to claim 4, wherein, In step S3, generating a system image includes: Image generation: The system uses virtualization technology to package the executable code and its dependent environment into a system image; Image configuration: The system image will contain a complete runtime environment configuration to ensure that the generated code can be successfully executed in the container or virtual machine.

6. The automated report generation method based on the large model Agent according to claim 5, wherein In step S4, the MCP image mounting service is mounted on the cloud native service platform, including: Cloud-native services: Use cloud computing platforms to deploy system images on cloud-native service platforms. Cloud-native service platforms can automatically schedule and manage the operation of images, ensuring that the system can be executed according to demand at any time. Image mounting: Mount the generated system image to the cloud platform through the MCP service to ensure that it has sufficient computing resources to execute tasks.

7. An automated report generation method based on a large model Agent according to claim 6, characterized in that In step S5, database operations and data table generation include: Database connection: The generated code will establish a connection with the data source to obtain the required raw data; Data calculation: Execute corresponding database operations according to user needs; the generated data table is the calculation result based on the needs.

8. An automated report generation method based on a large model Agent according to claim 7, characterized in that, In step S6, after obtaining the database calculation results, the system will use the large model knowledge base to perform intelligent analysis on the result data, including: Knowledge base query: The system transfers the generated data table to the big model knowledge base. The big model agent accesses the existing knowledge base to conduct in-depth analysis of the data and obtain meaningful insights into the business. Automated analysis: The large model agent automatically discovers abnormal trends, association rules, and potential problems in the data based on the information in the data table; the model agent can also generate natural language analysis reports for the data.

9. A method for automatically generating reports based on a large model Agent according to claim 8, characterized in that, In step S7, data visualization includes: the MCP service generates charts and other visualization elements based on the analysis results.

10. The method for generating an automatic report based on a large model agent according to claim 9, characterized in that: In step S8, report integration is also performed: the large model agent combines the analysis results, generated data tables and visual charts into a complete report.

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