Large model Agent technology-based DevOps assembly line generation system and method

Abstract

The invention discloses a large model Agent technology-based DevOps assembly line generation system and method, and the system comprises an AI Agent module which is used for understanding user demands, and automatically generating and optimizing a DevOps assembly line; the automatic configuration module is used for automatically executing tasks, configuring tools and managing processes; the intelligent decision support module is used for monitoring task execution conditions in real time and providing intelligent suggestions and solutions when abnormity occurs or decision needs to be made; the self-adaptive optimization module is used for automatically adjusting assembly line configuration and execution strategies according to dynamic requirements and environment changes of the project; and the user interface module is used for providing a user operation interface. According to the method, the operation process is simplified, the automation degree is improved, intelligent decision support is provided, the system flexibility is enhanced, the operation cost is reduced, and the efficiency and reliability of the financial OS project are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of project management, and specifically relates to a DevOps pipeline generation system and method based on large model Agent technology. Background Art

[0002] As financial OS projects become increasingly complex, the role of DevOps platforms becomes increasingly prominent. As the core project management platform in financial OS projects, DevOps carries important tasks such as system creation and deployment. Users drag and drop on the platform to build and execute various operations like an assembly line, which undoubtedly increases the complexity of operations and the need for professional knowledge.

[0003] However, with the rapid development of AI Agent technology, this problem seems to have found a solution. AI Agent technology, as a cutting-edge artificial intelligence application, can simulate the intelligent behavior of human employees and play an important role in business. They can not only understand and perform complex tasks, but also greatly simplify the operation process through intelligent automatic construction technology in the process of building pipelines.

[0004] The traditional way of building a pipeline often requires users to have certain professional knowledge and experience and to complete it through complex operations. However, with AI Agent technology, this process will become simpler and more efficient. Agents can automatically complete the construction of pipelines by learning and understanding user needs, greatly reducing the user's operational burden.

[0005] In addition, AI Agents also have natural advantages in terms of tool call process execution. They can automatically call various tools and perform tasks according to preset processes without human intervention. This automated process execution not only improves work efficiency, but also reduces the occurrence of human errors, thereby ensuring the smooth progress of the project.

[0006] The application of AI Agent technology has brought revolutionary changes to the DevOps platform in financial OS projects. Through intelligent automatic construction and automated process execution, they have greatly simplified the operation process, improved work efficiency, and provided a strong guarantee for the successful implementation of financial OS projects. With the continuous advancement of technology, AI Agent will play a more important role in future financial OS projects.

[0007] In the DevOps platform of financial OS projects, existing technical solutions usually include the following aspects:

[0008] 1. Manually build pipelines: In traditional DevOps practices, users need to manually set up and configure pipelines, which includes selecting and configuring various tools, defining the order and conditions for task execution, etc. This approach requires users to have a strong technical background and a deep understanding of the entire build process.

[0009] 2. Templates and preset processes: To simplify user operations, some platforms provide predefined pipeline templates. Users can select appropriate templates for quick start based on project requirements. These templates usually include some common building, testing, and deployment tasks.

[0010] 3. Scripts and automation tools: To improve efficiency, many teams use scripting languages ​​(such as Shell, Python, etc.) to write automation scripts to automate building, testing, and deployment. These scripts can be automatically executed under specific trigger conditions, reducing manual intervention.

[0011] 4. Continuous Integration / Continuous Deployment (CI / CD): CI / CD is the core of modern software development practices. It integrates, tests, and deploys code in an automated way. In financial OS projects, CI / CD tools such as Jenkins and GitLab CI are widely used to achieve rapid iteration and deployment of code.

[0012] 5. Monitoring and log analysis: To ensure the stability and reliability of the pipeline, the existing technical solutions also include monitoring and log analysis of the pipeline execution process. These tools can help the team discover and solve problems in a timely manner and improve the stability of the system.

[0013] 6. Human intervention and decision-making: Although the degree of automation is increasing, human intervention and decision-making are still required in some complex scenarios. For example, when encountering unknown errors or situations that require special handling, developers may need to intervene manually.

[0014] Compared with AI Agent technology, existing technical solutions still have a lot of room for improvement in automation and intelligence. AI Agent can learn user behavior patterns and project requirements through machine learning algorithms, automatically optimize pipeline configurations, and even provide intelligent decision support when problems arise. This intelligent automation can not only improve efficiency, but also reduce human errors to a certain extent, and improve the quality and stability of the entire DevOps process. With the continuous development of AI technology, it is expected that AI Agent will play an increasingly important role in the DevOps platform of financial OS projects.

[0015] Specifically, the shortcomings of the prior art include:

[0016] High operational complexity: Although the existing DevOps platform provides pipeline automation capabilities, its configuration and operation are still complex, especially in complex projects, users need to have a high level of technical background and experience. This not only increases the learning cost, but may also lead to configuration errors, affecting the stability and efficiency of the system.

[0017] Limited automation: Although existing technical solutions have introduced templates, preset processes and script tools, these methods still rely on manual settings and management and cannot fully achieve full automation. Existing automation tools are more passive in executing tasks rather than actively optimizing and adjusting task processes, resulting in a low degree of automation.

[0018] Lack of intelligent decision support: The existing DevOps process relies more on manual intervention by users. In particular, when encountering unknown errors or special situations, the system cannot provide effective intelligent decision support. Users need to handle these problems manually, which is not only time-consuming and labor-intensive, but may also introduce new error risks.

[0019] Lack of flexibility: Although existing DevOps platforms provide a variety of tools and templates, they are not flexible enough to meet the needs of different projects. Users need to adjust and configure them by themselves, and cannot make timely adjustments based on the dynamic changes of the project.

[0020] High cost: In some complex projects, in order to achieve a stable automation process, you may need to purchase additional plug-ins, tools or professional services, which invisibly increases the cost of the project. Moreover, when errors occur or maintenance is required, professional intervention is required, which further increases operating costs. Summary of the invention

[0021] In view of the above problems, the present invention is proposed to provide a DevOps pipeline generation system and method based on large model Agent technology to overcome the above problems or at least partially solve the above problems.

[0022] To achieve the above object, the present invention adopts the following technical solutions:

[0023] A DevOps pipeline generation system based on large model Agent technology, the system comprising:

[0024] AI Agent module, used to understand user needs and automatically generate and optimize DevOps pipelines;

[0025] Automation configuration module, used to automate tasks, configuration tools and management processes;

[0026] Intelligent decision support module, which is used to monitor the execution of tasks in real time and provide intelligent suggestions and solutions when anomalies occur or decisions are needed;

[0027] Adaptive optimization module, which is used to automatically adjust pipeline configuration and execution strategy according to the dynamic needs of the project and environmental changes;

[0028] The user interface module is used to provide a user operation interface.

[0029] Optionally, the AI ​​Agent module extracts key information from the user's description through natural language processing and machine learning technology, and automatically generates the optimal pipeline configuration based on historical data and preset rules.

[0030] Optionally, the automated configuration module automatically selects appropriate tools, defines the execution order and conditions of tasks, and makes dynamic adjustments based on the recommendations provided by the AI ​​Agent module.

[0031] Optionally, the intelligent decision support module optimizes the decision model through the learning ability of the AI ​​Agent module to improve the accuracy and timeliness of decisions.

[0032] Optionally, the Adaptive Optimization module monitors the project status in real time and automatically adjusts the configuration through the analysis capabilities of the AI ​​Agent module to ensure that the project always runs in the optimal state.

[0033] Optionally, the user interface module supports users to interact with the AI ​​Agent module through dragging and text input.

[0034] Optionally, the user interface module supports real-time feedback to display the execution status of the pipeline and the decision suggestions of the AI ​​Agent module.

[0035] The present invention also provides a DevOps pipeline generation method based on large model Agent technology, the method is based on any of the aforementioned DevOps pipeline generation systems based on large model Agent technology, the method comprising:

[0036] Users describe project requirements by text input or by selecting preset templates. The AI ​​Agent module parses the user input and extracts the key requirements of the project.

[0037] According to the demand analysis results, the AI ​​Agent module calls the preset model library to automatically generate the most suitable pipeline configuration for the current project;

[0038] The automated configuration module receives the pipeline configuration generated by the AI ​​Agent module and automatically integrates and configures the tools;

[0039] After completing the configuration, the automated configuration module executes the pipeline tasks, and the execution status and results of each task are transmitted to the intelligent decision support module in real time;

[0040] The intelligent decision support module monitors the execution of tasks in real time and detects possible abnormal situations. When an abnormality occurs, the intelligent decision support module will analyze the cause and provide corresponding solutions or suggestions;

[0041] When an exception occurs during the execution of the pipeline, the intelligent decision support module provides multiple solutions for users to choose from through the decision model of the AI ​​Agent module;

[0042] Based on historical execution data and current task status, the intelligent decision support module provides optimization suggestions at key nodes of the process to help users improve the efficiency and reliability of the assembly line;

[0043] The adaptive optimization module adjusts the pipeline configuration in real time according to the dynamic changes in project requirements;

[0044] Collect and analyze feedback data from pipeline execution, adjust the decision model and optimization strategy of the AI ​​Agent module, and improve the automation and intelligence level of the entire DevOps process.

[0045] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0046] 1. This invention uses AI Agent technology to simplify the operating process, improve the degree of automation, provide intelligent decision-making support, enhance system flexibility and reduce operating costs, thereby solving the technical defects in the existing DevOps platform and greatly improving the efficiency and reliability of financial OS projects.

[0047] 2. The present invention significantly reduces the complexity of user operations by introducing AI Agent technology. AI Agent can automatically understand user needs, and automatically build and adjust pipelines according to project requirements, reducing the requirements for user technical background, thereby improving the convenience of operation; AI Agent's learning and optimization capabilities realize the comprehensive automation of DevOps processes. AI Agent can not only perform tasks, but also actively analyze and optimize pipeline configurations, minimize manual intervention, and achieve highly automated process management.

[0048] 3. The present invention can adjust and optimize the DevOps process in real time according to the dynamic changes of project requirements, greatly improving the flexibility of the system without the need for manual adjustments by users; the system can automatically adapt to different project scenarios and provide the best solution; by simplifying the operating process and improving the degree of automation, the present invention can significantly reduce the operating costs of the project without the need to purchase additional tools or rely on professionals, and can provide efficient and stable DevOps services at a lower cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a DevOps pipeline generation system flow based on large model Agent technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, 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 in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] Embodiment 1:

[0052] See also Figure 1 This embodiment provides a DevOps pipeline generation system based on large model Agent technology, the system comprising:

[0053] AI Agent module, used to understand user needs and automatically generate and optimize DevOps pipelines.

[0054] The AI ​​Agent module extracts key information from the user's description through natural language processing and machine learning technology, and automatically generates the optimal pipeline configuration based on historical data and preset rules.

[0055] The automated configuration module integrates the pipeline configuration generated by the AI ​​Agent to automate task execution, configuration tools, and management processes.

[0056] The automated configuration module automatically selects appropriate tools, defines the execution order and conditions of tasks, and makes dynamic adjustments based on the recommendations provided by the AI ​​Agent module.

[0057] The intelligent decision support module is used to monitor the execution of tasks in real time and provide intelligent suggestions and solutions when anomalies occur or decisions are required.

[0058] The intelligent decision support module optimizes the decision model through the learning ability of the AI ​​Agent module to improve the accuracy and timeliness of decision-making.

[0059] The adaptive optimization module is used to automatically adjust the pipeline configuration and execution strategy according to the dynamic needs of the project and environmental changes.

[0060] The adaptive optimization module monitors the project status in real time and automatically adjusts the configuration through the analysis capabilities of the AI ​​Agent module to ensure that the project always runs in the optimal state.

[0061] The user interface module is used to provide a user operation interface that is simple and intuitive.

[0062] The user interface module supports users to interact with the AI ​​Agent module through dragging, text input, etc. The user interface module also supports real-time feedback to display the execution status of the pipeline and the decision suggestions of the AI ​​Agent module.

[0063] This embodiment uses AI Agent technology to simplify the operating process, improve the degree of automation, provide intelligent decision-making support, enhance system flexibility, and reduce operating costs, thereby solving the technical defects in the existing DevOps platform and greatly improving the efficiency and reliability of the financial OS project.

[0064] This embodiment significantly reduces the complexity of user operations by introducing AI Agent technology. AI Agent can automatically understand user needs, and automatically build and adjust pipelines according to project requirements, reducing the requirements for user technical background, thereby improving the convenience of operation; AI Agent's learning and optimization capabilities realize the full automation of DevOps processes. AI Agent can not only perform tasks, but also actively analyze and optimize pipeline configurations, minimize manual intervention, and achieve highly automated process management.

[0065] This embodiment can adjust and optimize the DevOps process in real time according to the dynamic changes of project requirements, greatly improving the flexibility of the system without the need for manual adjustments by users; the system can automatically adapt to different project scenarios and provide the best solution; by simplifying the operating process and improving the degree of automation, the present invention can significantly reduce the operating costs of the project without the need to purchase additional tools or rely on professionals, and can provide efficient and stable DevOps services at a lower cost.

[0066] Embodiment 2:

[0067] Embodiment 2 discloses a DevOps pipeline generation method based on large model Agent technology. The method is based on the above-mentioned DevOps pipeline generation system based on large model Agent technology. The method includes the following steps:

[0068] S1. Users describe project requirements by text input or by selecting preset templates. The AI ​​Agent module parses the user input and extracts the key requirements of the project, such as specific requirements for building, testing, and deployment.

[0069] S2. Based on the results of the demand analysis, the AI ​​Agent module calls the preset model library to automatically generate the pipeline configuration that best suits the current project. This includes selecting appropriate tools (such as Jenkins, Docker, etc.), defining the order of tasks, and execution conditions.

[0070] S3. The automated configuration module receives the pipeline configuration generated by the AI ​​Agent module and automatically integrates and configures the tools. This process does not require manual intervention by the user. The system will automatically complete the task configuration, environment setup and other steps.

[0071] S4. After completing the configuration, the automated configuration module executes the pipeline tasks, and the execution status and results of each task are transmitted to the intelligent decision support module in real time.

[0072] S5. The intelligent decision support module monitors the execution of tasks in real time and detects possible abnormal situations (such as task failure, delay, etc.). When an abnormality occurs, the intelligent decision support module will analyze the cause and provide corresponding solutions or suggestions.

[0073] S6. When an exception occurs during the execution of the pipeline, the intelligent decision support module provides multiple solutions for users to choose from through the decision model of the AI ​​Agent module. If the exception can be solved automatically, the system will automatically handle it according to the best solution.

[0074] S7. Based on historical execution data and current task status, the intelligent decision support module provides optimization suggestions at key nodes of the process to help users improve the efficiency and reliability of the assembly line.

[0075] S8, the adaptive optimization module adjusts the pipeline configuration in real time according to the dynamic changes in project requirements. For example, when the project scale expands or the environment changes, the module will automatically reconfigure resources to ensure the stable operation of the pipeline.

[0076] S9. Collect and analyze feedback data from pipeline execution, adjust the decision model and optimization strategy of the AI ​​Agent module, and improve the automation and intelligence level of the entire DevOps process.

[0077] Key technical points and their implementation details include:

[0078] The core technologies of AI Agent include natural language processing (NLP) and machine learning (ML) algorithms. Through these algorithms, AI Agent can effectively understand user needs and automatically generate suitable DevOps pipeline configurations.

[0079] The automated configuration module automatically completes tool configuration and task definition by integrating with existing CI / CD tools (such as Jenkins, GitLab CI, etc.). This module also supports docking with cloud computing platforms (such as AWS, Azure, etc.) to achieve automated resource configuration.

[0080] The intelligent decision support module is based on the learning model of AI Agent, analyzes the feedback data of task execution in real time, and provides optimization suggestions or automatic solutions based on the decision tree algorithm.

[0081] The adaptive optimization module dynamically adjusts the configuration of the pipeline by monitoring the project's resource usage and environmental changes, and continuously optimizes the process based on historical data and AI Agent feedback.

[0082] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A DevOps pipeline generation system based on large model Agent technology, characterized in that: The system comprises: AI Agent module, used to understand user needs and automatically generate and optimize DevOps pipelines; Automation configuration module, used to automate tasks, configuration tools and management processes; Intelligent decision support module, which is used to monitor the execution of tasks in real time and provide intelligent suggestions and solutions when anomalies occur or decisions are needed; Adaptive optimization module, which is used to automatically adjust pipeline configuration and execution strategy according to the dynamic needs of the project and environmental changes; The user interface module is used to provide a user operation interface.

2. A DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The AI ​​Agent module extracts key information from the user's description through natural language processing and machine learning technology, and automatically generates the optimal pipeline configuration based on historical data and preset rules.

3. A DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The automated configuration module automatically selects appropriate tools, defines the execution order and conditions of tasks, and makes dynamic adjustments based on the recommendations provided by the AI ​​Agent module.

4. A DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The intelligent decision support module optimizes the decision model through the learning ability of the AI ​​Agent module to improve the accuracy and timeliness of decision-making.

5. The DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The adaptive optimization module monitors the project status in real time and automatically adjusts the configuration through the analysis capabilities of the AI ​​Agent module to ensure that the project always runs in the optimal state.

6. A DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The user interface module supports users to interact with the AI ​​Agent module through dragging and text input.

7. A DevOps pipeline generation system based on large model Agent technology as described in claim 1, characterized in that: The user interface module supports real-time feedback to display the execution status of the pipeline and the decision suggestions of the AI ​​Agent module.

8. A DevOps pipeline generation method based on large model Agent technology, the method is based on a DevOps pipeline generation system based on large model Agent technology as described in any one of claims 1-7, characterized in that: The method comprises: Users describe project requirements by text input or by selecting preset templates. The AI ​​Agent module parses the user input and extracts the key requirements of the project. According to the demand analysis results, the AI ​​Agent module calls the preset model library to automatically generate the most suitable pipeline configuration for the current project; The automated configuration module receives the pipeline configuration generated by the AI ​​Agent module and automatically integrates and configures the tools; After completing the configuration, the automated configuration module executes the pipeline tasks, and the execution status and results of each task are transmitted to the intelligent decision support module in real time; The intelligent decision support module monitors the execution of tasks in real time and detects possible abnormal situations. When an abnormality occurs, the intelligent decision support module will analyze the cause and provide corresponding solutions or suggestions; When an exception occurs during the execution of the pipeline, the intelligent decision support module provides multiple solutions for users to choose from through the decision model of the AI ​​Agent module; Based on historical execution data and current task status, the intelligent decision support module provides optimization suggestions at key nodes of the process to help users improve the efficiency and reliability of the assembly line; The adaptive optimization module adjusts the pipeline configuration in real time according to the dynamic changes in project requirements; Collect and analyze feedback data from pipeline execution, adjust the decision model and optimization strategy of the AI ​​Agent module, and improve the automation and intelligence level of the entire DevOps process.

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