Vocational ability training system based on artificial intelligence technology

Abstract

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

Description

Technical Field

[0001] The present invention is a vocational ability training system based on artificial intelligence technology, belonging to the technical field of vocational education. Background Art

[0002] In today's career development landscape, professional development for both employed and employed individuals faces numerous challenges. Currently, most common professional development methods rely on traditional face-to-face instruction, which has significant shortcomings in many areas and struggles to meet the rapidly changing demands of the modern workplace.

[0003] From the perspective of personalized training, the traditional face-to-face model makes it difficult for trainers to account for the individual differences of numerous trainees. Due to the lack of effective technical means to accurately analyze students' mastery of basic theoretical knowledge, practical skills, and learning preferences, it is impossible to customize training content and methods for different students. This results in some students being unable to effectively improve their professional abilities due to training content that is too difficult or too easy, and their enthusiasm is dampened. For example, in practical skills training in some emerging industries, due to the rapid pace of technological changes, traditional training methods cannot promptly adjust training content to the students' actual conditions, making it difficult for students to keep pace with industry developments.

[0004] In terms of training resources, the traditional model relies primarily on paper textbooks and a small amount of local multimedia materials, which are limited and updated slowly. In emerging industries (such as artificial intelligence and new energy vehicle manufacturing), where technology is rapidly evolving, traditional textbooks are unable to keep up with the latest theoretical knowledge and practical skills. Statistics show that the update cycle for traditional textbooks often takes several years, while the technology update cycle in these industries may only be a few months. This results in students being exposed to outdated knowledge and skills, seriously affecting their professional competitiveness.

[0005] Traditional training models are limited to fixed locations (such as classrooms and training grounds) and timelines. Due to busy schedules and fragmented timelines, employees struggle to attend training at specific times and locations as required by traditional training models. Consequently, the consistency and integrity of training cannot be guaranteed. For example, project workers often need to travel or work overtime, making it difficult for them to attend training courses at fixed times and locations.

[0006] Traditionally, training effectiveness assessments have primarily relied on theoretical exam scores, a simplistic and one-sided approach. This approach fails to fully and deeply reflect a trainee's mastery of comprehensive professional competencies, including practical skills, knowledge application, and problem-solving abilities. For example, in certain occupations requiring hands-on experience (such as mechanical maintenance and electronic circuit debugging), theoretical exams alone cannot accurately assess a trainee's practical skills.

[0007] With the gradual application of artificial intelligence technology in education, while some platforms have introduced features such as intelligent Q&A and simple online testing, these features lack systematic and in-depth integration. Intelligent Q&A often relies on fixed answers, failing to provide in-depth analysis and guidance based on individual student situations. Integration with modern office applications (such as DingTalk, WeChat for Work, and Feishu) remains superficial, failing to fully leverage their widespread availability to improve training efficiency. The application of digital humans in training is still in its exploratory stages, with limited application scenarios, enabling only simple course presentations. This lacks in-depth interaction with students and targeted training, hindering the interactive, engaging, and professional advantages of digital humans in professional skills training.

[0008] There are four major pain points in traditional vocational training:

[0009] 1. Lack of personalization:

[0010] It is difficult for teachers to take into account the differences among students, such as the fragmented learning needs of in-service personnel;

[0011] Unified training leads to low efficiency and mismatch between theory and practice;

[0012] 2. Resource lag:

[0013] Paper textbooks are updated slowly. For example, the knowledge iteration speed in AI and new energy fields is >6 months / time.

[0014] 3. Scene rigidity:

[0015] Fixed venues / time constraints for working people, >70% of students interrupt their studies due to work conflicts;

[0016] 4. One-sided evaluation:

[0017] Only written examinations are used to assess practical skills and problem-solving abilities;

[0018] Current status of AI technology application: The existing intelligent question-answering system only implements keyword matching and cannot dynamically adapt to differences in students' abilities.

[0019] In summary, the existing vocational skills training methods and platforms for employed and employed people have defects in training models, resources, scenarios, evaluation and technology integration. There is an urgent need for an innovative, comprehensive and targeted vocational skills training system based on artificial intelligence technology to solve these problems. Summary of the Invention

[0020] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a professional ability training system based on artificial intelligence technology to solve the problems raised in the above background technology.

[0021] In order to achieve the above-mentioned object, the present invention is implemented through the following technical solutions: a vocational ability training system based on artificial intelligence technology, comprising:

[0022] User portrait modeling module: Generates dynamic personal capability maps based on user historical data and capability assessment models;

[0023] Knowledge graph engine: Integrates industry capability standards, job requirement data, and a real-time updated professional skills knowledge base to form a structured knowledge network;

[0024] AI training recommendation module: uses reinforcement learning algorithms, combined with user profiles and knowledge graphs, to dynamically generate personalized training paths;

[0025] Digital Human Application Module: This module uses virtual reality and NLP technology to build simulated work scenarios, allowing users to complete training through natural conversations and operations.

[0026] Real-time feedback and correction module: uses multimodal data to analyze user performance and generate instant guidance suggestions.

[0027] Specifically, a microservice architecture with separated front-end and back-end is adopted to ensure maintainability and scalability; the front-end uses Python as the development language, and builds an interactive interface adapted to multiple terminals based on the MonsterUI framework; the back-end is divided into independent service modules for user management, training content management, model training and application, which interact through RESTful API and rely on MySQL database for data storage and management; at the same time, it integrates big data and machine learning technologies, and connects with DingTalk and enterprise WeChat office applications to build a multi-scenario learning ecosystem.

[0028] Specifically, the user portrait modeling module includes a comprehensive database model, a teacher model, and a student model; the AI ​​training recommendation module includes AI customer service, AI question bank, AI teaching assistant, AI Q&A, and AI voice.

[0029] Specifically, the comprehensive database model serves as a knowledge and skills reserve center, collecting multi-type corpus data, including multimedia resources related to basic theoretical knowledge, examination questions, teaching materials, training policies, and physical photos and models related to practical skills training, and classifying and labeling these data. Assume that the data after classification and labeling is represented as a set D = {d1, d2, ..., d n}′, where d i Represents the i-th annotated data item, which is trained through the open large model platform. The training process can be simplified as function T rain (D) integrates knowledge after training and provides a quick query interface to provide comprehensive knowledge and skills support for professional ability training.

[0030] Specifically, the teacher model includes submodules such as training content planning, training method selection, and support and help provision. The training content planning submodule selects adaptive content from the comprehensive database model based on the trainee training data fed back by the student model. For trainees with weak basic theoretical knowledge, targeted basic theoretical explanation materials are pushed; for trainees with low practical proficiency in skills, more practical cases and practice tasks are arranged. Suppose the trainee training data is a vector S = (s1, s2, ..., s m ), the content set in the comprehensive database model is C = {c1, c2, ..., c k The process of screening and adapting content can be expressed as the function SelectContent(S, C). The training method selection submodule combines the student's learning style and calls the corresponding training resources. The support and help provision submodule parses the student's questions through the natural language processing algorithm, obtains the answers from the comprehensive database model, and pushes them.

[0031] Specifically, the student model records the student status through the knowledge level assessment submodule and the training status recording submodule. The knowledge level assessment submodule uses machine learning algorithms to analyze the student's data on basic theoretical knowledge answers, skill practice tests, etc., and evaluates the student's mastery of each knowledge and skill module. Suppose the answer data is the matrix Q = [q ij ], where i represents the student number and j represents the question number. The knowledge level assessment result is obtained through the algorithm Evaluate(Q). The training status recording submodule records the student's training time, progress, question content, interaction status and other data. Combined with the evaluation results, a training status report is generated to provide a basis for the teacher model adjustment strategy.

[0032] Specifically, AI customer service: The front-end has voice and text interaction portals. After receiving students' questions, they are transmitted to the back-end via AJAX. The back-end calls the natural language processing and large model query interface to retrieve answers from the comprehensive database model and provide feedback.

[0033] AI question bank: Based on the question bank of the training center, after students select a learning module, the backend will filter the questions based on their learning situation and generate a personalized practice set;

[0034] AI teaching assistant: Configure independent instances for students, track learning behavior, provide differentiated answers to different questions based on learning history, record problems and plan learning progress, and customize and push courses, materials, and exercises to provide one-on-one tutoring.

[0035] AI Q&A: The front-end has a voice Q&A portal, which converts speech into text and transmits it to the back-end. The back-end uses natural language processing and large-scale model queries to retrieve information such as courses and training policies. Voice synthesis provides feedback to students, making it easy to obtain information.

[0036] AI Voice: The front end has a function entrance, which converts knowledge into voice playback based on the learning plan and content, supports voice command control, combines with AI Q&A assistant to answer questions, and uses multi-sensory memory to break through geographical restrictions.

[0037] Specifically, the Digital Human application module includes Digital Human customization: calling the Digital Human open platform interface to customize the "Gold Medal Teacher" image, collecting voice samples from professional teachers, and generating interactive voice through speech synthesis and training algorithms;

[0038] Digital human live broadcast connection: Connect to the open live broadcast platform and configure live broadcast parameters through the open API. During the live broadcast, the digital human teaches according to the training script generated by the large model and receives real-time feedback from live students. The backend monitors the message through WebSocket and drives the digital human to respond with voice and action after parsing by the natural language processing algorithm, realizing in-depth interaction.

[0039] Beneficial effects of the present invention:

[0040] 1. The student model accurately depicts the student's learning progress, abilities, interests, and style. The teacher model dynamically adjusts training content and methods accordingly, achieving truly "one-on-one" personalized training. Compared to the traditional "one-size-fits-all" face-to-face teaching model, this model can fully adapt to the differences of students, improve learning efficiency and motivation, and address the lack of personalization.

[0041] 2. A comprehensive database model is constructed by integrating multi-type, massive corpus data. The resource quantity and update speed far exceed those of traditional textbooks, adapting to the knowledge iteration needs of emerging industries. Furthermore, multi-terminal deployment and integration with office applications break free from the constraints of fixed locations and time. Students can learn anytime, anywhere, meeting the needs of fragmented and flexible learning and overcoming resource and scenario limitations.

[0042] 3. The student model integrates knowledge level assessment and learning progress records to generate comprehensive learning status reports, replacing traditional single-exam assessments. Teachers and students can obtain real-time feedback and adjust teaching and learning strategies in a timely manner, ensuring continuous improvement in learning outcomes and addressing the problem of one-sided assessments.

[0043] 4. Deeply integrate big data, machine learning, digital humans, and office application technologies. Digital humans enable the transition from simple presentations to deeply interactive training; collaborating with office applications expands the convenience of training scenarios. Compared to existing shallow intelligent applications, this builds a systematic and immersive learning ecosystem, enhances the fun and efficiency of training, and addresses the issue of insufficient technological integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0045] Figure 1This is a system architecture diagram of a vocational ability training system based on artificial intelligence technology in the present invention; DETAILED DESCRIPTION

[0046] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0047] See also Figure 1 The present invention provides a technical solution: a vocational ability training system based on artificial intelligence technology, comprising:

[0048] User portrait modeling module: Generates a dynamic personal capability map based on the user's historical data (learning trajectory, test results, etc.) and capability assessment model; the user portrait modeling module includes a comprehensive database model, a teacher model, and a student model;

[0049] The comprehensive database model serves as a knowledge and skills reserve center and collects multi-type corpus data, including multimedia resources related to basic theoretical knowledge (such as course videos, animated presentations, etc.), examination questions (actual questions of professional qualification examinations, internal enterprise assessment questions, etc.), teaching materials (professional theoretical books, industry standard documents, etc.), training policies (national and local vocational training policy documents), and physical photos and models related to practical skills training (actual operation equipment pictures, process flow models, etc.). These data are classified and labeled (for example, according to the subject classification of basic theoretical knowledge, the job classification of practical skills, and the difficulty level, knowledge points, etc.). Assume that the data after classification and labeling is represented as a set D = {d1, d2, ..., d n}′, where d i It represents the i-th labeled data item and is trained through the open large model platform. The training process can be simplified as the function Train(D). After training, it integrates knowledge and provides a fast query interface (such as connecting to MySQL to realize knowledge retrieval), providing comprehensive knowledge and skills support for professional ability training.

[0050] The teacher model includes submodules such as training content planning, training method selection, and support and help provision. The training content planning submodule selects adaptive content from the comprehensive database model based on the trainee training data fed back by the student model (such as basic theoretical knowledge level, practical skills proficiency, learning interests, etc.). For trainees with weak basic theoretical knowledge, targeted basic theoretical explanation materials are pushed; for trainees with low practical skills proficiency, more practical cases and practice tasks are arranged. Suppose the trainee training data is a vector S = (s1, s2, ..., s m ), the content set in the comprehensive database model is C = {c1, c2, ..., c kThe process of selecting suitable content can be represented by the function SelectContent(S, C). The training method selection submodule combines the student's learning style (such as visual, practical, theoretical, etc.) and calls the corresponding training resources. The support and help provision submodule parses the student's questions through the natural language processing algorithm, obtains the answers from the comprehensive database model and pushes them (such as through email, real-time messaging, etc.).

[0051] The student model records the student status through the knowledge level assessment submodule and the training status recording submodule. The knowledge level assessment submodule uses machine learning algorithms (such as the decision tree algorithm and random forest algorithm in Scikit-learn) to analyze the data of students answering basic theoretical knowledge questions and practical skills tests, and evaluates the students' mastery of each knowledge and skill module (such as divided into four levels: excellent, good, medium, and weak). Suppose the answer data is the matrix Q = [q ij ], where i represents the student number and j represents the question number. The knowledge level assessment result is obtained through the algorithm Evaluate(Q). The training status recording submodule records the student's training time, progress, question content, interaction status and other data. Combined with the evaluation results, a training status report is generated to provide a basis for the teacher model adjustment strategy.

[0052] Knowledge graph engine: Integrates industry capability standards, job requirement data, and a real-time updated professional skills knowledge base to form a structured knowledge network;

[0053] AI training recommendation module: uses reinforcement learning algorithms, combined with user portraits and knowledge graphs, to dynamically generate personalized training paths (such as courses, practical training tasks); the AI ​​training recommendation module includes AI customer service, AI question bank, AI teaching assistant, AI Q&A, and AI voice.

[0054] AI customer service: The front-end has voice (WebSpeechAPI) and text interaction portals. After receiving students' questions, they are transmitted to the back-end via AJAX. The back-end calls the natural language processing and large model query interface to retrieve answers from the comprehensive database model and provide feedback;

[0055] AI Question Bank: Based on the question bank of the training center (which stores question types, difficulty levels, and knowledge points by category), after students select a learning module, the backend will filter questions (basic questions or extended questions) based on their learning situation (student model) and generate a personalized practice set.

[0056] AI teaching assistants: Configure independent instances for students, track learning behavior, and provide differentiated answers (with pictures, text, or case examples) based on learning history (student model) for different questions. They also record questions and plan learning progress, and customize and push courses, materials, and exercises to provide one-on-one tutoring.

[0057] AI Q&A: The front-end has a voice Q&A portal, which converts speech into text and transmits it to the back-end. The back-end uses natural language processing and large-scale model queries to retrieve information such as courses and training policies. Voice synthesis provides feedback to students, making it easy to obtain information.

[0058] AI Voice: The front end has a functional entrance, which converts knowledge into voice playback (using speech synthesis algorithm) based on the learning plan and content (student model, training content module), supports voice command control (pause, questioning), combines with AI Q&A assistant to answer questions, and assists memory with multiple senses, breaking through geographical restrictions.

[0059] Digital Human Application Module: This module uses virtual reality (VR) and NLP technologies to build simulated work scenarios (e.g., digital humans simulating business negotiations, intelligent agents completing technical troubleshooting, etc.), allowing users to complete training through natural conversations and operations. The Digital Human Application Module includes Digital Human customization: The module uses the Digital Human open platform interface to customize the "Gold Medal Teacher" image, collects voice samples from professional teachers, and generates interactive speech through speech synthesis and training algorithms.

[0060] Digital human live broadcast connection: connect to open live broadcast platforms (such as DingTalk), configure live broadcast parameters (scheduled broadcast, course association, interactive settings) through open APIs. During the live broadcast, the digital human teaches according to the training script generated by the large model and receives real-time feedback from live students (questions, messages). The backend monitors messages through WebSocket, and after parsing by natural language processing algorithms, drives the digital human to respond with voice and actions to achieve in-depth interaction.

[0061] Real-time feedback and correction module: Analyzes user performance using multimodal data (voice, operation logs, expression recognition) and generates instant guidance suggestions;

[0062] The system utilizes a microservices architecture with separate front-end and back-end functionality to ensure maintainability and scalability. The front-end is developed in Python, using the MonsterUI framework to create an interactive interface compatible with multiple terminals (mobile and web). The back-end is divided into independent service modules for user management, training content management, model training, and application development. These modules interact via a RESTful API, relying on a MySQL database for data storage and management. Furthermore, the system integrates big data and machine learning technologies (using SDKs to access large model specifications such as DeepSeek), and integrates with DingTalk and WeChat Work applications to build a multi-scenario learning ecosystem. The technology stack includes: front-end: MonsterUI; back-end: Python + Flask; AI layer: Scikit-learn decision tree.

[0063] Working process:

[0064] System initialization: Complete the construction of front-end and back-end architecture, database initialization (create data tables such as students, resources, model training, etc.), large model corpus data collection and training, and realize digital human customization and docking open platform configuration.

[0065] Student Usage Process: Students log in through multiple terminals (WeChat official account, mobile app, website) and use various functional modules (such as intelligent learning assistant for questions and test assistant for practice) to conduct learning. The system collects learning data in real time and updates the student model. The teacher model dynamically adjusts the training content and methods based on the student model. Digital humans interact with training through live broadcasts and other means, forming a closed loop of "learning-teaching-evaluation".

[0066] Data flow and processing: Learning behavior data is collected, cleaned (to remove errors and duplicate data), converted (to a unified format), and then stored in a database; machine learning algorithms analyze the data and optimize models (such as recommendation algorithms and knowledge assessment algorithms); large models continuously learn corpus data, iterate knowledge reserves, and ensure dynamic optimization of system functions.

[0067] advantage:

[0068] (1) Personalized and in-depth training

[0069] The student model accurately depicts students' learning progress, abilities, interests, and learning styles. The teacher model dynamically adjusts training content (e.g., delivering tailored knowledge to students of different levels) and methods (including text, graphics, and hands-on instruction), achieving truly personalized, one-on-one training. Compared to the traditional, one-size-fits-all approach of face-to-face instruction, this approach fully accommodates student differences, improves learning efficiency and motivation, and addresses the issue of insufficient personalization.

[0070] (2) Learning resources and scenario expansion

[0071] By integrating a vast amount of multi-type corpus data to build a comprehensive database model, the volume and update speed of resources far surpass those of traditional textbooks, adapting to the knowledge iteration needs of emerging industries. Furthermore, multi-terminal deployment (mobile, web) and integration with office applications transcend fixed locations and time constraints, allowing students to learn anytime, anywhere, meeting the needs of fragmented and flexible learning and addressing resource and scenario limitations.

[0072] (3) Learning effect evaluation and feedback optimization

[0073] The student model integrates knowledge level assessment (multi-dimensional analysis of knowledge mastery) with learning progress records (full-process tracking of learning behavior) to generate comprehensive learning status reports, replacing traditional single-exam assessments. Teachers and students can obtain real-time feedback, allowing them to adjust teaching and learning strategies promptly, ensuring continuous improvement in learning outcomes and addressing the issue of one-sided assessments.

[0074] (4) Technological integration and interactive innovation

[0075] Deeply integrating big data, machine learning, digital humans, and office application technologies. Digital humans enable a shift from simple presentations to deeply interactive training (responding to students' questions and adjusting the teaching cadence in real time). Collaborating with office applications expands the convenience of training scenarios. Compared to existing shallow intelligent applications, this builds a systematic, immersive learning ecosystem, enhancing the fun and efficiency of training and addressing the lack of technological integration.

[0076] Large models (such as DeepSeek) can be replaced with other mature large models (such as Baidu's and Alibaba Cloud's). As long as they possess knowledge retrieval, natural language processing, and model training capabilities, and can adapt to system data formats and interface specifications, they can implement functions such as knowledge storage, student question answering, and training content recommendations. The MonsterUI front-end framework can be replaced with mainstream frameworks such as Vue.js and React. These frameworks also feature rich components and responsive design capabilities. By adjusting the front-end development code, similar multi-terminal adaptive interactive interfaces can be achieved, ensuring student operational experience and functional access. In addition to DingTalk, integration can be expanded to include more office applications (such as Tencent Meeting, Feishu, and WeChat for Work) or social platforms (such as WeChat Video Account, Bilibili, and Douyin). By calling corresponding open APIs, digital human live streaming and student interaction can be implemented, further expanding the coverage of training scenarios and meeting the usage habits of different user groups. Digital human training can go beyond simple theoretical training and can directly utilize the full corpus of professional skills training courses. Although the initial training difficulty and data volume increase, it can improve the digital human's adaptability to complex knowledge training and shorten the training content adaptation cycle. However, it is necessary to balance the training cost and effect.

[0077] Multi-model collaborative architecture: The construction and collaborative mechanism of the comprehensive database model, teacher model, and student model realizes a closed loop of knowledge reserve, personalized training strategy generation, and accurate characterization of student status.

[0078] In-depth application of digital humans: The full-process technical implementation of digital humans, from customization (sound, graphics, movements) to in-depth interaction with office applications (DingTalk live broadcast), breaks through the limitations of traditional digital human training.

[0079] Multi-technology integration and data flow: The integration of technologies such as big data collection and cleaning, machine learning algorithm optimization (recommendation, evaluation), multi-terminal and office application docking, as well as the dynamic flow and processing of learning data between modules, ensure the intelligent operation of the system.

[0080] System architecture and model: Multi-model (comprehensive database, teachers, students) collaborative professional ability training system architecture; model construction method (including corpus data classification and annotation, training parameter setting); data interaction and strategy adjustment mechanism between models.

[0081] Digital human application: Digital human customization method based on open platform (sound, graphics, and movement training process); live interactive technology for connecting digital humans with office applications (DingTalk) (including parameter configuration, message monitoring and response algorithms).

[0082] Functional module implementation: AI intelligent learning assistant's multiple interactive entrances (voice, text) and personalized learning planning algorithm; AI test assistant's test question screening and wrong question analysis feedback mechanism; data association and collaborative workflow between various functional modules.

[0083] Data processing and security: methods for collecting, cleaning, converting, and storing learning behavior data; data security mechanisms that combine encryption technology (SSL / TLS, database encryption) and access control (RBAC).

[0084] Although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A vocational ability training system based on artificial intelligence technology, characterized by include: User portrait modeling module: Generates dynamic personal capability maps based on user historical data and capability assessment models; Knowledge graph engine: Integrates industry capability standards, job requirement data, and a real-time updated professional skills knowledge base to form a structured knowledge network; AI training recommendation module: uses reinforcement learning algorithms, combined with user profiles and knowledge graphs, to dynamically generate personalized training paths; Digital Human Application Module: This module uses virtual reality and NLP technology to build simulated work scenarios, allowing users to complete training through natural conversations and operations. Real-time feedback and correction module: uses multimodal data to analyze user performance and generate instant guidance suggestions.

2. The artificial intelligence-based vocational skills training system according to claim 1, characterized in that: A microservice architecture with separated front-end and back-end is adopted to ensure maintainability and scalability; the front-end is developed in Python, and an interactive interface suitable for multiple terminals is built based on the MonsterUI framework; the back-end is divided into independent service modules for user management, training content management, model training and application, which interact through RESTful API and rely on MySQL database for data storage and management; at the same time, it integrates big data and machine learning technologies, and connects with DingTalk and enterprise WeChat office applications to build a multi-scenario learning ecosystem.

3. The artificial intelligence-based vocational skills training system according to claim 1, characterized in that: The user portrait modeling module includes a comprehensive database model, a teacher model, and a student model; the AI ​​training recommendation module includes AI customer service, AI question bank, AI teaching assistant, AI Q&A, and AI voice.

4. The artificial intelligence-based vocational skills training system according to claim 3, characterized in that: As a knowledge and skills reserve center, the comprehensive database model collects multi-type corpus data, including multimedia resources related to basic theoretical knowledge, examination questions, teaching materials, training policies, and physical photos and models related to practical skills training. These data are classified and labeled. Assume that the data after classification and labeling is represented as a set D = {d1, d2, ..., d n }, where d i It represents the i-th labeled data item and is trained through the open large model platform. The training process can be simplified as the function Train(D). After training, it integrates knowledge and provides a fast query interface to provide comprehensive knowledge and skills support for professional ability training.

5. The artificial intelligence-based vocational skills training system according to claim 3, characterized in that: The teacher model includes submodules such as training content planning, training method selection, and support and help provision. The training content planning submodule selects adaptive content from the comprehensive database model based on the trainee training data fed back by the student model. For trainees with weak basic theoretical knowledge, targeted basic theoretical explanation materials are pushed; for trainees with low practical proficiency, more practical cases and practice tasks are arranged. Suppose the trainee training data is a vector S = (s1, s2, ..., s m ), the content set in the comprehensive database model is C = {c1, c2, ..., c k The process of screening and adapting content can be expressed as the function SelectContent(S, C). The training method selection submodule combines the student's learning style and calls the corresponding training resources. The support and help provision submodule parses the student's questions through the natural language processing algorithm, obtains the answers from the comprehensive database model, and pushes them.

6. The artificial intelligence-based vocational skills training system according to claim 3, characterized in that: The student model records the student status through the knowledge level assessment submodule and the training status recording submodule. The knowledge level assessment submodule uses machine learning algorithms to analyze the data of students answering basic theoretical knowledge questions and practical skills tests to evaluate the students' mastery of each knowledge and skill module. The answer data is set as the matrix Q = [q ij ], where i represents the student number and j represents the question number. The knowledge level assessment result is obtained through the algorithm Evaluate(Q). The training status recording submodule records the student's training time, progress, question content, interaction status and other data. Combined with the evaluation results, a training status report is generated to provide a basis for the teacher model adjustment strategy.

7. The artificial intelligence-based vocational skills training system according to claim 3, characterized in that: AI customer service: The front-end has voice and text interaction portals. After receiving students' questions, they are transmitted to the back-end via AJAX. The back-end calls the natural language processing and large model query interface to retrieve answers from the comprehensive database model and provide feedback; AI question bank: Based on the question bank of the training center, after students select a learning module, the backend will filter the questions based on their learning situation and generate a personalized practice set; AI teaching assistant: Configure independent instances for students, track learning behavior, provide differentiated answers to different questions based on learning history, record problems and plan learning progress, and customize and push courses, materials, and exercises to provide one-on-one tutoring. AI Q&A: The front-end has a voice Q&A portal, which converts speech into text and transmits it to the back-end. The back-end uses natural language processing and large-scale model queries to retrieve information such as courses and training policies. Voice synthesis provides feedback to students, making it easy to obtain information. AI Voice: The front end has a function entrance, which converts knowledge into voice playback based on the learning plan and content, supports voice command control, combines with AI Q&A assistant to answer questions, and uses multi-sensory memory to break through geographical restrictions.

8. The artificial intelligence-based vocational skills training system according to claim 1, characterized in that: The Digihuman application module includes Digihuman customization: calling the Digihuman open platform interface to customize the "Gold Medal Teacher" image, collecting voice samples from professional teachers, and generating interactive voice through speech synthesis and training algorithms; Digital human live broadcast connection: Connect to the open live broadcast platform and configure live broadcast parameters through the open API. During the live broadcast, the digital human teaches according to the training script generated by the large model and receives real-time feedback from live students. The backend monitors the message through WebSocket and drives the digital human to respond with voice and action after parsing by the natural language processing algorithm, realizing in-depth interaction.

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