A docker-based multi-database practical training environment dynamic construction system and method

Abstract

The application relates to the technical field of computer software, and discloses a multi-database practical training environment dynamic construction system and method based on Docker, which comprises a public basic support module, a teacher-end multi-software data integration and environment configuration module, a student-end environment simulation deployment and data acquisition module, an administrator-end system resource and data management and control module, and an AI-assisted learning and big data practice module; comprises the following steps: S1, a system login and initialization stage; S2, a teacher-end data integration and environment configuration stage; S3, a student-end environment simulation deployment stage; S4, a deployment result confirmation and problem troubleshooting stage; S5, a system operation and maintenance and data management stage; and S6, a data analysis and big data practice and AI-assisted learning stage; the application realizes full-process closed-loop support of practical training teaching from early-stage data integration, cross-system environment deployment to late-stage data analysis and AI-assisted learning, and simplifies the teaching preparation and practical training management and control work of teachers.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and in particular to a dynamic construction system and method for a multi-database training environment based on Docker. Background Technology

[0002] With the rapid development of big data and artificial intelligence technologies, society is increasingly demanding multi-skilled talents with the ability to operate multiple software programs and deploy across systems. This has highlighted the growing importance of practical training in universities and training institutions. Against this backdrop, Docker containerization technology, with its characteristics of environment isolation, lightweight portability, and compatibility, is gradually being introduced into practical training scenarios to solve compatibility issues in building different software training environments. Simultaneously, continuous iteration and upgrading of front-end technology frameworks provide technical support for the visual interaction of training platforms.

[0003] Currently, most practical training scenarios have begun to try to introduce digital platforms to assist teaching, but the front-end design of these platforms generally focuses on the practical operation of a single software and has not yet formed an integrated front-end solution for data collection, organization, and cross-system deployment simulation of multiple software. In the process of practical training, teachers and students still need to face problems such as scattered data from multiple software programs, complex cross-system deployment and configuration, and fragmented front-end operation entry points, which make it difficult to meet the needs of modern practical training for efficiency, integration, and visualization. Therefore, there is an urgent need for a front-end technology solution that can realize the integration of data from multiple software programs and cross-system environment simulation. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic construction system and method for multi-database training environments based on Docker, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic construction system for multi-database training environments based on Docker includes a common basic support module, a teacher-side multi-software data integration and environment configuration module, a student-side environment simulation deployment and data acquisition module, an administrator-side system resource and data management module, and an AI-assisted learning and big data practice module. These modules work together to achieve full-process support for multi-software data collection and organization, cross-system environment simulation deployment, front-end data integration, data analysis, and AI-assisted learning.

[0006] As a further improvement to this technical solution: the common basic support module includes a unified login and permission management device, a system navigation and global configuration unit, and a cross-system interaction adaptation component; the unified login and permission management device supports account password login and verification code login, integrates a JWTToken authentication mechanism, and dynamically loads corresponding function menus and operation permissions according to user roles after login; the system navigation and global configuration unit includes a top navigation bar, a left role-based sidebar, and a global configuration panel. The top navigation bar displays the system name, message notifications, and personal center. The left sidebar supports collapsing, expanding, and custom sorting. The global configuration panel supports theme switching, language switching, and deployment environment preview style settings; the cross-system interaction adaptation component encapsulates Docker API front-end interaction logic, multi-system deployment protocol adaptation interfaces, and database connection adaptation components, supports deployment in multiple system environments such as Windows and Linux, adapts to different system terminal styles, file directory structures, and environment variable configurations, and establishes communication links between the front-end and back-end container services and multi-software database services.

[0007] As a further improvement to this technical solution: the teacher-side multi-software data integration and environment configuration module includes a multi-software data collection unit, a data organization and processing unit, and a cross-system environment simulation deployment configuration unit. The multi-software data collection unit supports uploading system configuration files, database structure files, and dependency package lists of various teaching software such as Software A and Software B, provides online search functionality, can obtain official data resources of publicly available teaching software, and supports data format verification. The data organization and processing unit classifies and archives the collected multi-software data according to software type, version, and data category, automatically extracts key information such as software name, version number, database type, core fields, and dependent components, generates a standardized data list, and supports manual editing to supplement data details and delete redundant data. The cross-system environment simulation deployment configuration unit supports selecting target deployment system types such as Windows and Linux, visually configuring the hardware parameters, database connection parameters, and dependent component versions required for software operation, associating the organized software data with database resources, generating a reusable environment deployment template, and providing a deployment effect preview function.

[0008] As a further improvement to this technical solution: the student-side environment simulation deployment and data acquisition module includes an environment simulation deployment operation unit, an integrated data acquisition unit, and a deployment process visualization unit; the environment simulation deployment operation unit supports selecting environment deployment templates shared by teachers, triggering the simulation deployment process with one click, and displaying deployment steps, progress bars, and current status in real time on the front end, supporting deployment pause, rollback, and redeployment operations, and simulating abnormal prompts during the deployment process; the integrated data acquisition unit directly associates with the multi-software data and database resource packages organized by teachers, supports online preview of data lists and database table structures, and provides local export functionality; the deployment process visualization unit displays the status of each stage of deployment in the form of flowcharts and progress bars, marks key configuration parameters, provides real-time feedback on deployment results, and provides deployment log viewing functionality.

[0009] As a further improvement to this technical solution: the administrator-side system resource and data management module includes a software data resource library management device, a system deployment environment configuration unit, and an operation log monitoring unit; the software data resource library management device reviews the software data uploaded by teachers, stores multiple software system configurations and database resources according to software type and version, supports resource retrieval, update, and deletion operations, and sets resource storage limits; the system deployment environment configuration unit sets the type of deployable system that can be simulated, the upper limit of hardware resources, and the data storage duration, manages Docker container image resources, and configures communication parameters between the front-end and back-end services; the operation log monitoring unit records key operations such as user data upload, environment configuration, and simulated deployment, supports log retrieval by user, time, and operation type, and displays the system resource usage status in real time.

[0010] As a further improvement to this technical solution: the AI-assisted learning and big data practice module includes an AI-assisted environment configuration unit, an AI-assisted database design unit, an AI-assisted simulated data creation unit, an AI-assisted data analysis unit, and an AI-assisted programming unit. The AI-assisted environment configuration unit supports automatically recommending suitable big data components and hardware resource configuration schemes based on user-inputted training needs, and generating environment configuration scripts. The AI-assisted database design unit supports natural language input of database requirements, automatically generating table structure design schemes, field constraints, and index design suggestions, and supports manual editing and optimization. The AI-assisted simulated data creation unit supports setting data volume, data type, and field association rules, automatically generating simulated datasets that conform to business scenarios, and supports data export and direct import into the training environment. The AI-assisted data analysis unit covers the entire process of data collection, data cleaning, data mining, data visualization, and data storage, providing automated analysis tools and step guidance, and supporting the generation of analysis reports. The AI-assisted programming unit provides code generation, syntax correction, logic optimization, and comment supplementation functions, and supports commonly used training programming languages ​​such as SQL and Python.

[0011] A method for dynamically building a multi-database training environment based on Docker includes the following steps: S1. System login and initialization phase: Users log in through the unified login and permission control device. The system dynamically loads the function menu according to the role. Administrators set the configurable software type and resource limit through the system deployment environment configuration unit and maintain Docker container image resources. S2, Teacher-side Data Integration and Environment Configuration Stage: Teachers upload or retrieve the system configuration and database resources of the software required for teaching through the multi-software data collection unit; classify and archive the data through the data organization and sorting unit and generate a standardized data list; and select the target system type, configure deployment parameters, associate data resources and generate an environment deployment template through the cross-system environment simulation deployment configuration unit. S3, Student-side Environment Simulation Deployment Phase: Students select the deployment template shared by the teacher through the environment simulation deployment operation unit, start the simulation deployment with one click, view the deployment progress and status through the deployment process visualization unit, and preview or export the organized software data and database resources online through the integrated data acquisition unit. S4. During the deployment result confirmation and problem troubleshooting stage, students view the deployment results and logs displayed in the visualization unit of the deployment process. If any abnormalities occur, they can adjust the deployment operation or reselect the template according to the prompts. Teachers can simultaneously view the students' deployment status. S5. System operation and maintenance and data management stage. The administrator audits and maintains the software data resources in the platform through the software data resource library management device, retrieves the user operation records through the operation log monitoring unit, and the system automatically retains the data related to data integration and deployment for subsequent traceability. S6. Data analysis, big data practice, and AI-assisted learning stage. The user obtains an adapted big data environment configuration plan through the AI-assisted environment configuration unit, completes the table structure design through the AI-assisted database design unit, generates the data set required for training through the AI-assisted simulated data creation unit, completes the whole process practice of data collection, cleaning, mining, visualization, and storage through the AI-assisted data analysis unit, and obtains supports such as code generation, error correction, and optimization through the AI-assisted programming unit to complete the big data and programming training tasks.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention realizes the full-process closed-loop support of training teaching from the early-stage data integration, cross-system environment deployment to the later-stage data analysis and AI-assisted learning, and simplifies the teaching preparation and training management work of teachers; teachers do not need to manually collect and sort out various teaching software and database resources separately, and can complete the centralized acquisition of resources, standardized regularization, and rapid creation of cross-system deployment templates through the exclusive module, avoiding complex script writing and repeated operations. At the same time, with the help of the AI-assisted module, an adapted training environment and data resources can be designed for students, enabling teachers to devote more energy to teaching content design and training guidance. In addition, the administrator can achieve unified control of software data resources and system running status and full-process traceability of operation logs through the platform, standardize the management process of training resources, monitor the system resource occupancy in real time, effectively ensure the stable and orderly operation of the training platform, and reduce the difficulty and cost of platform operation and maintenance.

[0013] 2. The present invention reduces the operation threshold for students' cross-system training, big data, and programming practice, and creates a convenient and integrated training learning environment for students; students do not need to manually download software and configure complex cross-system deployment parameters, and can complete the simulation deployment of the training environment in one click by selecting the template configured by the teacher. They can also intuitively view the deployment progress and logs, quickly locate and solve problems during the deployment process; in the core practice link, the full-process AI-assisted ability can cover the whole process of environment configuration, database design, simulated data creation, and data analysis in big data training and programming practice, providing students with supports such as automated solution recommendation, code generation, syntax error correction, and logic optimization, helping students quickly master the core logic of big data analysis and programming, and improving the efficiency and effect of training learning. At the same time, the platform realizes the integrated acquisition and use of training resources, and students do not need to switch between multiple channels to obtain data and resources, effectively improving the coherence and convenience of training learning.

[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A schematic diagram of the system architecture for a dynamic construction system and method for a multi-database training environment based on Docker; Figure 2 This is a schematic diagram of the common basic support module structure of a dynamic construction system and method for multi-database training environments based on Docker; Figure 3 A schematic diagram of the structure of a teacher-side multi-software data integration and environment configuration module for a dynamic construction system and method of multi-database training environment based on Docker; Figure 4 A schematic diagram of the structure of a teacher-side multi-software data integration and environment configuration module for a dynamic construction system and method of multi-database training environment based on Docker; Figure 5 A schematic diagram of the administrator-side system resource and data management module structure for a Docker-based multi-database training environment dynamic construction system and method; Figure 6 This is a schematic diagram of the structure of an AI-assisted learning and big data practice module, which is a dynamic construction system and method for a multi-database training environment based on Docker. Detailed Implementation

[0016] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0017] Please see Figures 1-6In this embodiment of the invention, a dynamic construction system for a multi-database training environment based on Docker includes a common basic support module, a teacher-side multi-software data integration and environment configuration module, a student-side environment simulation deployment and data acquisition module, an administrator-side system resource and data management module, and an AI-assisted learning and big data practice module. The modules work together to achieve full-process support for multi-software data collection and organization, cross-system environment simulation deployment, front-end data integration, data analysis, and AI-assisted learning. Specifically, the modules consist of: a public infrastructure support module, a teacher-side multi-software data integration and environment configuration module, a student-side environment simulation deployment and data acquisition module, an administrator-side system resource and data management module, and an AI-assisted learning and big data practice module. Overall Function: The modules work together to provide full-process support for the collection and organization of data from multiple software programs, the simulation and deployment of cross-system environments, and the integrated integration of front-end data. It focuses on the early data integration and environment deployment, and solves the pain points of scattered data from multiple software programs and cumbersome cross-system deployment for teachers and students.

[0018] The public infrastructure support module includes a unified login and access control device, a system navigation and global configuration unit, and a cross-system interaction adaptation component. The unified login and access control device supports account / password login and verification code login, integrates a JWTToken authentication mechanism, and dynamically loads corresponding function menus and operation permissions based on the user's role after login. The system navigation and global configuration unit includes a top navigation bar, a left role-based sidebar, and a global configuration panel. The top navigation bar displays the system name, message notifications, and the user center; the left sidebar supports collapsing, expanding, and custom sorting; and the global configuration panel supports theme switching, language switching, and deployment environment preview style settings. The cross-system interaction adaptation component encapsulates Docker API front-end interaction logic, multi-system deployment protocol adaptation interfaces, and database connection adaptation components. It supports deployment in multiple system environments such as Windows and Linux, adapts to different system terminal styles, file directory structures, and environment variable configurations, and establishes communication links between the front-end and back-end container services and multi-software database services. Specifically, the unified login and access control device: Function: Supports account and password login and verification code login, integrates JWTToken authentication mechanism, and dynamically loads function menus and operation permissions according to role (student, teacher, administrator) after login, realizes one login to access the entire platform, and ensures data security and access control. System Navigation and Global Configuration Unit: Function: Includes a top navigation bar (system name, message notifications, personal center), a left role-based sidebar (supports collapsing, expanding, and custom sorting), and a global configuration panel (theme, language, deployment environment preview style settings), providing a unified and intuitive operation entry point to adapt to different user operating habits; Cross-system interaction adaptation component: Function: Encapsulates Docker API front-end interaction logic, multi-system deployment protocol adaptation interfaces, and database connection adaptation components. It supports front-end visual adaptation for multi-system deployment environments such as Windows and Linux, simulates the terminal style, file directory structure, and environment variable configuration interface of different systems, and establishes communication links between the front-end and back-end container services and multi-software database services. As the underlying interaction support, it breaks down the interaction barriers of cross-system deployment, eliminating the need for users to manually configure communication parameters and achieving seamless linkage between the front-end and multiple system environments.

[0019] The teacher-side multi-software data integration and environment configuration module includes a multi-software data collection unit, a data organization and arrangement unit, and a cross-system environment simulation deployment and configuration unit. The multi-software data collection unit supports uploading system configuration files, database structure files, and dependency package lists for various teaching software such as Software A and Software B. It provides online search functionality, allowing access to official data resources for publicly available teaching software, and supports data format verification. The data organization and arrangement unit categorizes and archives the collected multi-software data by software type, version, and data category. It automatically extracts key information such as software name, version number, database type, core fields, and dependent components, generating a standardized data list. It also supports manual editing to supplement data details and delete redundant data. The cross-system environment simulation deployment and configuration unit supports selecting target deployment system types such as Windows and Linux. It visually configures the hardware parameters, database connection parameters, and dependent component versions required for software operation, associates the organized software data with database resources, generates reusable environment deployment templates, and provides a deployment effect preview function. Specifically, the multi-software data collection unit's functions include: supporting the uploading of system configuration files, database structure files, and dependency package lists for various teaching software such as Software A and Software B; providing online search functionality to obtain official data resources for publicly available teaching software; supporting data format validation (automatically identifying invalid files and providing prompts); and centrally collecting scattered data from multiple software programs, avoiding teachers having to manually search for and download various resources, reducing tedious data acquisition operations, and ensuring the integrity and validity of teaching data. Data Organizing and Consolidation Unit: Function: It categorizes and archives collected multi-software data according to software type, version, and data category (system configuration, database resources, dependency packages), automatically extracts key information such as software name, version number, database type, core fields, and dependent components, generates a standardized data list, and supports manual editing to add details and delete redundant data; it transforms scattered and non-standardized data into a unified format of usable resources, solves the problem of chaotic multi-software data, and provides a organized data foundation for creating environment deployment templates; Cross-system environment simulation deployment configuration unit: Function: Supports selection of target deployment system types such as Windows and Linux, visually configures the hardware parameters (CPU, memory, disk), database connection parameters (address, port, account), and dependent component versions required for software operation, associates the organized software data with database resources, generates reusable environment deployment templates, and provides a deployment effect preview function (simulating the system interface and resource usage after deployment); realizes visual configuration of cross-system deployment environments, eliminates the need to write complex deployment scripts, quickly generates standardized templates, and ensures that the student simulation deployment environment is consistent with teaching needs.

[0020] The student-side environment simulation deployment and data acquisition module includes an environment simulation deployment operation unit, an integrated data acquisition unit, and a deployment process visualization unit. The environment simulation deployment operation unit allows users to select environment deployment templates shared by teachers, triggering the simulation deployment process with a single click. The front end displays deployment steps, progress bars, and current status in real time, supporting deployment pause, rollback, and redeployment operations, and providing error prompts during the deployment process. The integrated data acquisition unit directly links to the teacher-organized multi-software data and database resource packages, supporting online preview of data lists and database table structures, and providing local export functionality. The deployment process visualization unit displays the deployment status at each stage in the form of flowcharts and progress bars, annotates key configuration parameters, provides real-time feedback on deployment results, and offers deployment log viewing functionality. Specifically, the environment simulation deployment operation unit: Its function is to support selecting environment deployment templates shared by teachers, triggering a simulated deployment process with one click, and displaying the deployment steps (software installation, database configuration, dependency loading), progress bar, and current status in real time on the front end. It supports deployment pause, rollback, and redeployment operations, and simulates abnormal prompts during the deployment process (such as missing dependencies or incorrect parameters). It restores the real deployment process, eliminating the need for students to manually download software and configure parameters, lowering the barrier to cross-system deployment operations, and allowing students to focus on the deployment process and logical learning. Integrated Data Acquisition Unit: Function: Directly links to the teacher-organized multi-software data and database resource packages, supports online preview of data lists and database table structures, and provides local export functionality; solves the problem of scattered and cumbersome acquisition of teaching software data by students, realizes one-click acquisition of organized data, and ensures the accuracy and completeness of the data required for deployment; Deployment Process Visualization Unit: Function: Displays the status of each stage of deployment in the form of flowcharts and progress bars, marks key configuration parameters (such as database port and dependency version), provides real-time feedback on deployment results, and provides deployment log viewing function (records deployment steps, time consumption, and exception information), allowing students to intuitively grasp the deployment progress and key nodes, and facilitating the troubleshooting of deployment problems.

[0021] The administrator-side system resource and data management module includes a software data resource repository management device, a system deployment environment configuration unit, and an operation log monitoring unit. The software data resource repository management device reviews software data uploaded by teachers, stores multi-software system configurations and database resources categorized by software type and version, supports resource retrieval, update, and deletion operations, and sets resource storage limits. The system deployment environment configuration unit sets the types of deployable systems that can be simulated, hardware resource limits, and data storage duration, manages Docker container image resources, and configures communication parameters between the front-end and back-end services. The operation log monitoring unit records key operations such as user data uploads, environment configuration, and simulated deployments, supports log retrieval by user, time, and operation type, and displays the real-time system resource usage status. Specifically, the software data resource library management device has the following functions: reviewing software data uploaded by teachers, classifying and storing multiple software system configurations and database resources according to software type and version, supporting resource retrieval, updating, and deletion operations, setting resource storage limits, standardizing platform data resource management, and ensuring the compliance and security of teaching data; System Deployment Environment Configuration Unit: Function: Set the types of deployment systems that can be simulated (such as adding a specific Linux distribution), hardware resource limits (CPU, memory thresholds), data storage duration, manage Docker container image resources, configure communication parameters between the front-end and back-end services, ensure the stability of the platform deployment environment, and adapt to the resource needs of different teaching scenarios. Operation Log Monitoring Unit: Function: Records key operations such as user data upload, environment configuration, and simulated deployment. It supports searching logs by user, time, and operation type, displays the real-time status of system resource usage (CPU, memory, and disk usage), quickly traces operational behavior, promptly detects and handles system resource anomalies, and ensures stable platform operation.

[0022] The AI-assisted learning and big data practice module includes an AI-assisted environment configuration unit, an AI-assisted database design unit, an AI-assisted simulated data creation unit, an AI-assisted data analysis unit, and an AI-assisted programming unit. The AI-assisted environment configuration unit automatically recommends suitable big data components and hardware resource configuration schemes based on user-inputted training needs, and generates environment configuration scripts. The AI-assisted database design unit supports natural language input of database requirements, automatically generating table structure design schemes, field constraints, and index design suggestions, and supports manual editing and optimization. The AI-assisted simulated data creation unit allows setting data volume, data type, and field association rules, automatically generating simulated datasets that conform to business scenarios, and supports data export and direct import into the training environment. The AI-assisted data analysis unit covers the entire process of data collection, data cleaning, data mining, data visualization, and data storage, providing automated analysis tools and step-by-step guidance, and supporting the generation of analysis reports. The AI-assisted programming unit provides code generation, syntax correction, logic optimization, and comment supplementation functions, and supports commonly used training programming languages ​​such as SQL and Python. Specifically, the AI-assisted environment configuration unit can receive the user's specific requirements for big data training, and automatically recommend suitable big data component combinations and hardware resource configuration schemes based on information such as training scenarios and data volume. At the same time, it automatically generates corresponding environment configuration scripts. Users can directly reuse the scripts to complete the big data environment configuration without having to manually write complex configuration commands, saving environment configuration time and ensuring the matching of configuration schemes with training requirements. AI-assisted database design unit: It supports users to input database design requirements in natural language. Based on the requirements, AI automatically generates table structure design schemes, field data types and constraints, and index design optimization suggestions that conform to business logic. At the same time, it provides a visual design scheme editing interface, which supports users to manually edit and optimize. It solves the problem that database design is highly professional and difficult for beginners to get started quickly, and enables students to quickly master the core logic of database design. AI-assisted simulation data creation unit: It allows users to set parameters such as data volume, data type, field association rules, and business scenario characteristics according to training needs. The AI ​​automatically generates a simulation dataset that conforms to the actual business scenario based on the parameters. The generated dataset can be directly exported as a file or imported into the deployed training database environment with one click. Users do not need to manually create or find training data, which solves the problems of data missing and data mismatch with the scenario in the training process and improves the convenience of big data practice. AI-Assisted Data Analysis Unit: Covering the entire big data analysis process from data collection, data cleaning, data mining, data visualization, and data storage, it provides automated analysis tools and step-by-step operation guidance for each stage. It supports connecting to database resources in the training environment to complete data collection, provides intelligent data cleaning functions to automatically handle missing and outlier values, has built-in commonly used data mining algorithms and provides algorithm selection suggestions, supports the automatic generation of various forms of data visualization charts, and provides suitable data storage solution suggestions. Finally, it can automatically generate data analysis reports, allowing students to clearly grasp the logic of the entire data analysis process and reduce the operational difficulty of big data practice. AI-Assisted Programming Unit: Provides full-process AI assistance for commonly used programming languages ​​in practical training such as SQL and Python. It supports generating corresponding functional code based on the user's natural language needs, performing real-time syntax correction and logic optimization on the user's code, automatically adding standardized comments to the code, and explaining the user's code line by line to help students understand the code logic. This solves the problems of students' weak programming foundation and difficulties in writing and debugging code, and improves the efficiency of programming learning.

[0023] A method for dynamically building a multi-database training environment based on Docker includes the following steps: S1. System login and initialization phase: Users log in through the unified login and permission control device. The system dynamically loads the function menu according to the role. Administrators set the configurable software type and resource limit through the system deployment environment configuration unit and maintain Docker container image resources. S2, Teacher-side Data Integration and Environment Configuration Stage: Teachers upload or retrieve the system configuration and database resources of the software required for teaching through the multi-software data collection unit; classify and archive the data through the data organization and sorting unit and generate a standardized data list; and select the target system type, configure deployment parameters, associate data resources and generate an environment deployment template through the cross-system environment simulation deployment configuration unit. S3, Student-side Environment Simulation Deployment Phase: Students select the deployment template shared by the teacher through the environment simulation deployment operation unit, start the simulation deployment with one click, view the deployment progress and status through the deployment process visualization unit, and preview or export the organized software data and database resources online through the integrated data acquisition unit. S4. During the deployment result confirmation and problem troubleshooting stage, students view the deployment results and logs displayed in the visualization unit of the deployment process. If any abnormalities occur, they can adjust the deployment operation or reselect the template according to the prompts. Teachers can simultaneously view the students' deployment status. S5. In the system operation and maintenance and data management phase, the administrator reviews and maintains the software data resources in the platform through the software data resource library management device, retrieves user operation records through the operation log monitoring unit, and the system automatically retains data integration and deployment related data for subsequent traceability. S6. In the data analysis and big data practice and AI-assisted learning stage, users can obtain a suitable big data environment configuration solution through the AI-assisted environment configuration unit, complete table structure design through the AI-assisted database design unit, generate the dataset required for training through the AI-assisted simulation data creation unit, complete the entire process of data collection, cleaning, mining, visualization and storage through the AI-assisted data analysis unit, and obtain support for code generation, error correction and optimization through the AI-assisted programming unit to complete the big data and programming training tasks. Specifically, in the S1 system login and initialization phase: users log in through a unified login and permission control device, and the system dynamically loads function menus according to roles; administrators set the simulated system type and resource limits through the system deployment environment configuration unit, maintain Docker container image resources, and complete the basic platform configuration. S2, Teacher-side Data Integration and Environment Configuration Stage: Teachers upload or retrieve the system configurations and database resources of the software required for teaching through the multi-software data collection unit; classify and archive data through the data organization and sorting unit and generate a standardized data list; select the target system type, configure deployment parameters, associate data resources, and generate an environment deployment template through the cross-system environment simulation deployment configuration unit. S3, Student-side Environment Simulation Deployment Phase: Students select the deployment template shared by the teacher through the environment simulation deployment operation unit and start the simulation deployment with one click; they can view the deployment progress and status through the deployment process visualization unit; and they can preview or export the organized software data and database resources online through the integrated data acquisition unit. S4. Deployment Result Confirmation and Problem Troubleshooting Stage: Students view the deployment results and logs displayed in the visualization unit of the deployment process. If any abnormalities occur, they can adjust the deployment operation or reselect the template according to the prompts. Teachers simultaneously view the students' deployment status and provide targeted guidance. S5. System Operation and Data Management Phase: Administrators review and maintain software data resources within the platform through the software data resource library management device; retrieve user operation records through the operation log monitoring unit; the system automatically retains data integration and deployment-related data for subsequent traceability. In the S6 stage of data analysis and big data practice and AI-assisted learning, users, based on the training environment deployed in the previous steps, obtain a big data environment configuration scheme adapted to the training needs through the AI-assisted environment configuration unit, ensuring the rationality of the training environment; complete the database table structure design required for training through the AI-assisted database design unit, laying the foundation for data analysis; generate simulated datasets that fit the training scenario through the AI-assisted simulated data creation unit, solving the training data needs; complete the entire big data practice of data collection, cleaning, mining, visualization, and storage through the AI-assisted data analysis unit, mastering core data analysis skills; and obtain support for code generation, error correction, and optimization through the AI-assisted programming unit to complete programming training tasks. The entire process is assisted by AI, reducing the learning threshold of big data and programming training and improving the efficiency and effectiveness of training.

[0024] The method of use and working principle of this invention are as follows: Usage: Users first log in through a unified login and access control device. The system dynamically loads the corresponding function menu based on the user's role. Administrators simultaneously complete the settings for simulated system types, resource limits, and Docker container image resources. Teachers then obtain the system configurations and database resources of the software required for teaching through the multi-software data collection unit. A standardized data list is generated through the data organization unit, and then the target system type, deployment parameters, and environment deployment template are selected through the cross-system environment simulation deployment configuration unit. Students then select the deployment template shared by the teacher to start the simulated deployment with one click. The deployment progress and status are viewed through the deployment process visualization unit, and the regulated software data and database resources can be previewed online or exported. Students view the deployment results and logs; if an error occurs, they can adjust their operations or reselect the template based on the prompts. Teachers simultaneously view student deployment status and provide guidance; then, administrators review and maintain software data resources within the platform through the software data resource library management device, retrieve user operation records through the operation log monitoring unit, and the system automatically retains data integration and deployment-related data for subsequent traceability; finally, the data analysis and big data practice and AI-assisted learning stage begins. Based on the deployed training environment, users obtain a suitable big data environment configuration scheme through the AI-assisted environment configuration unit, complete table structure design through the AI-assisted database design unit, generate training datasets through the AI-assisted simulation data creation unit, complete the entire process of data collection, cleaning, mining, visualization, and storage through the AI-assisted data analysis unit, and obtain code generation, error correction, and optimization support with the help of the AI-assisted programming unit, thus completing the entire set of big data and programming training tasks.

[0025] Working Principle: User authentication and role-based access control are achieved through a unified login and permission management system. The system navigation and global configuration unit provides standardized operation entry points. Cross-system interaction adaptation components encapsulate relevant interfaces and interaction logic, simulating the front-end display of a multi-system deployment environment and establishing communication links between the front-end and back-end container services and multi-software database services. The teacher-side multi-software data integration and environment configuration module centrally acquires various teaching software resources through the multi-software data collection unit. The data organization and processing unit categorizes, archives, and standardizes these resources. The cross-system environment simulation deployment configuration unit then associates the data resources and generates reusable environment deployment templates, providing standardized resources and environment support for practical training. The student-side environment simulation deployment and data acquisition module triggers the simulation deployment process based on the teacher-configured templates. The deployment process visualization unit provides real-time feedback on deployment status and results, and the integrated data acquisition unit enables convenient access to the standardized resources, ensuring a safe and efficient student training environment. The system boasts high efficiency in deployment and resource acquisition. The administrator-side system resource and data management module provides unified review and management of software data resources and container image resources within the platform. The operation log monitoring unit records and retrieves key user operations, monitoring system resource usage in real time to ensure the platform's overall stable and orderly operation. The newly added AI-assisted learning and big data practice module serves as the core support for practical training. Through five functional units, it provides targeted AI-assisted support for big data training environment configuration, database design, simulated data creation, full-process data analysis, and programming practice, covering the entire process of big data practice and programming learning. Each module is interconnected and works collaboratively, first completing data integration and environment deployment in the early stages of training, then ensuring platform and data security through operation and maintenance management, and finally relying on AI-assisted capabilities to achieve full-process training support for data analysis and big data practice, forming a complete training closed loop from resource preparation, environment deployment, operation and maintenance management to practical learning.

[0026] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the description and drawings above. However, any modifications, alterations, and variations made by those skilled in the art without departing from the scope of the present invention using the disclosed technical content are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, and variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A dynamic construction system for a multi-database training environment based on Docker, comprising a common basic support module, a teacher-side multi-software data integration and environment configuration module, a student-side environment simulation deployment and data acquisition module, an administrator-side system resource and data management module, and an AI-assisted learning and big data practice module. The modules work together to achieve full-process support for multi-software data collection and organization, cross-system environment simulation deployment, front-end data integration, data analysis, and AI-assisted learning.

2. The Docker-based multi-database training environment dynamic construction system according to claim 1, characterized in that, The common infrastructure support module includes a unified login and permission management device, a system navigation and global configuration unit, and a cross-system interaction adaptation component. The unified login and permission management device supports account and password login and verification code login, integrates a JWTToken authentication mechanism, and dynamically loads corresponding function menus and operation permissions according to user roles after login. The system navigation and global configuration unit includes a top navigation bar, a left role-based sidebar, and a global configuration panel. The top navigation bar displays the system name, message notifications, and personal center. The left sidebar supports collapsing, expanding, and custom sorting. The global configuration panel supports theme switching, language switching, and deployment environment preview style settings. The cross-system interaction adaptation component encapsulates Docker API front-end interaction logic, multi-system deployment protocol adaptation interfaces, and database connection adaptation components. It supports deployment in multiple system environments such as Windows and Linux, adapts to different system terminal styles, file directory structures, and environment variable configurations, and establishes communication links between the front-end and back-end container services and multi-software database services.

3. The dynamic construction system for a multi-database training environment based on Docker according to claim 1, characterized in that, The teacher-side multi-software data integration and environment configuration module includes a multi-software data collection unit, a data organization and processing unit, and a cross-system environment simulation deployment and configuration unit. The multi-software data collection unit supports uploading system configuration files, database structure files, and dependency package lists of various teaching software such as software A and software B, provides online search functions, can obtain official data resources of publicly available teaching software, and supports data format verification. The data organization and processing unit categorizes and archives the collected multi-software data by software type, version, and data category. It automatically extracts key information such as software name, version number, database type, core fields, and dependent components, generating a standardized data list. It also supports manual editing to supplement data details and delete redundant data. The cross-system environment simulation deployment and configuration unit supports selecting target deployment system types such as Windows and Linux. It visualizes the hardware parameters, database connection parameters, and dependent component versions required for software operation, associates the organized software data with database resources, generates a reusable environment deployment template, and provides a deployment effect preview function.

4. The Docker-based multi-database training environment dynamic construction system according to claim 1, characterized in that, The student-side environment simulation deployment and data acquisition module includes an environment simulation deployment operation unit, an integrated data acquisition unit, and a deployment process visualization unit. The environment simulation deployment operation unit supports selecting environment deployment templates shared by teachers, triggering the simulation deployment process with one click, and displaying deployment steps, progress bars, and current status in real time on the front end. It supports deployment pause, rollback, and redeployment operations, as well as abnormal prompts during the simulation deployment process. The integrated data acquisition unit is directly linked to the teacher's organized multi-software data and database resource package, supports online preview of data lists and database table structures, and provides local export functionality; the deployment process visualization unit displays the status of each stage of deployment in the form of flowcharts and progress bars, marks key configuration parameters, provides real-time feedback on deployment results, and provides deployment log viewing functionality.

5. The Docker-based multi-database training environment dynamic construction system according to claim 1, characterized in that, The administrator-side system resource and data management module includes a software data resource library management device, a system deployment environment configuration unit, and an operation log monitoring unit. The software data resource library management device reviews the software data uploaded by teachers, stores multiple software system configurations and database resources according to software type and version, supports resource retrieval, update, and deletion operations, and sets resource storage limits. The system deployment environment configuration unit sets the type of deployment system that can be simulated, the upper limit of hardware resources, the data storage duration, manages Docker container image resources, and configures the communication parameters between the front-end and back-end services; the operation log monitoring unit records key operations such as user data upload, environment configuration, and simulated deployment, supports searching logs by user, time, and operation type, and displays the system resource usage status in real time.

6. A dynamic construction system for a multi-database training environment based on Docker, as described in claim 1, wherein the AI-assisted learning and big data practice module includes an AI-assisted environment configuration unit, an AI-assisted database design unit, an AI-assisted simulated data creation unit, an AI-assisted data analysis unit, and an AI-assisted programming unit; the AI-assisted environment configuration unit supports automatically recommending suitable big data components and hardware resource configuration schemes based on user-input training requirements, and generating environment configuration scripts; the AI-assisted database design unit supports inputting database requirements in natural language, automatically generating table structure design schemes, field constraints, and index design suggestions, and supports manual editing and optimization; the AI-assisted simulated data creation unit supports setting data volume, data type, and field association rules, automatically generating simulated datasets that conform to business scenarios, and supports data export and direct import into the training environment; the AI-assisted data analysis unit covers the entire process of data collection, data cleaning, data mining, data visualization, and data storage, providing automated analysis tools and step guidance, and supporting the generation of analysis reports; the AI-assisted programming unit provides code generation, syntax correction, logic optimization, and comment supplementation functions, and supports commonly used training programming languages ​​such as SQL and Python.

7. A method for dynamically constructing a multi-database training environment based on Docker, applied to the dynamic construction system for a multi-database training environment based on Docker as described in any one of claims 1-6, characterized in that, Includes the following steps: S1. System login and initialization phase: Users log in through the unified login and permission control device. The system dynamically loads the function menu according to the role. Administrators set the configurable software type and resource limit through the system deployment environment configuration unit and maintain Docker container image resources. S2, Teacher-side Data Integration and Environment Configuration Stage: Teachers upload or retrieve the system configuration and database resources of the software required for teaching through the multi-software data collection unit; classify and archive the data through the data organization and sorting unit and generate a standardized data list; and select the target system type, configure deployment parameters, associate data resources and generate an environment deployment template through the cross-system environment simulation deployment configuration unit. S3, Student-side Environment Simulation Deployment Phase: Students select the deployment template shared by the teacher through the environment simulation deployment operation unit, start the simulation deployment with one click, view the deployment progress and status through the deployment process visualization unit, and preview or export the organized software data and database resources online through the integrated data acquisition unit. S4. During the deployment result confirmation and problem troubleshooting stage, students view the deployment results and logs displayed in the visualization unit of the deployment process. If any abnormalities occur, they can adjust the deployment operation or reselect the template according to the prompts. Teachers can simultaneously view the students' deployment status. S5. In the system operation and maintenance and data management phase, the administrator reviews and maintains the software data resources in the platform through the software data resource library management device, retrieves user operation records through the operation log monitoring unit, and the system automatically retains data integration and deployment related data for subsequent traceability. S6. In the data analysis and big data practice and AI-assisted learning stage, users obtain a suitable big data environment configuration solution through the AI-assisted environment configuration unit, complete table structure design through the AI-assisted database design unit, generate the dataset required for training through the AI-assisted simulation data creation unit, complete the entire process of data collection, cleaning, mining, visualization and storage through the AI-assisted data analysis unit, and obtain support for code generation, error correction and optimization through the AI-assisted programming unit to complete the big data and programming training tasks.

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