CN-121998803-A - AI-based intelligent teaching system and method for four-electric system of high-speed railway

CN121998803ACN 121998803 ACN121998803 ACN 121998803ACN-121998803-A

Abstract

The invention relates to the technical field of railway maintenance education, in particular to an intelligent teaching system and method for a four-electric system of a high-speed railway based on AI, comprising a teaching data acquisition module for acquiring teaching process data, student learning track and industry fault data; the virtual simulation modeling module fuses the BIM model and the technical specification text to construct a four-electric system twin body; the AI intelligent analysis engine builds a student ability portrait by a machine learning algorithm to generate a personalized learning path and intelligent answering, the virtual-real teaching interaction module builds a digital intelligent training scene, performs three-dimensional visual error correction and remote guidance through real-time action recognition and outputs training data, the cross-professional fault simulation module performs cross-professional fault linkage simulation deduction through an association rule mining algorithm and outputs a deduction result, and the teaching effect evaluation module builds a binary evaluation system combining procedural and real operation skill assessment and outputs teaching effect data. Therefore, the problems of solidification, low accuracy and the like of teaching strategies in the prior art are solved.

Inventors

TIAN BAOCHUN
ZHANG XIAOYING
ZHANG DONG
CUI JUNQIANG
ZHANG SHAN

Assignees

呼和浩特职业技术大学

Dates

Publication Date: 20260508
Application Date: 20260304

Claims (10)

1. The intelligent teaching system for the four-electric system of the high-speed railway based on the AI is characterized by comprising a teaching data acquisition module, a virtual simulation modeling module, an AI intelligent analysis engine, a virtual-real teaching interaction module, a cross-professional fault simulation module and a teaching effect evaluation module, The teaching data acquisition module is used for acquiring teaching process data, student learning tracks and industry real fault data; The virtual simulation modeling module is used for fusing BIM model and railway technical specification text data based on the teaching process data, the student learning track and the industry real fault data to construct a digital twin body of the four-electric system of the high-speed railway; The AI intelligent analysis engine is used for constructing a student ability portrait through a machine learning algorithm based on the digital twin analysis teaching data, generating a personalized learning path and providing intelligent answering; The virtual-real teaching interaction module is used for constructing a personalized digital intelligent training scene according to the student capacity portrait and the personalized learning path, carrying out VR immersion type equipment disassembly and assembly training, carrying out three-dimensional visual error correction and AR remote dynamic teaching guidance through real-time action recognition, and outputting student training data; The cross-specialized fault simulation module is used for performing cross-specialized fault linkage simulation and dynamic deduction through an association rule mining algorithm based on the trainee training data, and outputting a fault deduction result; The teaching effect evaluation module is used for establishing a binary evaluation system combining procedural assessment and practical skill assessment according to the fault deduction result and the practical training data of the students, and quantitatively outputting teaching effect data.
2. The intelligent teaching system of the four-electric system of the high-speed railway based on the AI is characterized in that the teaching data acquisition module comprises a teaching process data acquisition unit, a learning track tracking unit and a fault data integration unit, wherein the teaching process data acquisition unit is used for acquiring classroom interaction data, practical training operation behavior data and theoretical examination answering data in real time, the learning track tracking unit is used for recording learning duration of students, knowledge point mastering progress, practical training operation repetition frequency and error operation types, and the fault data integration unit is used for summarizing real fault cases, fault diagnosis flows and maintenance treatment data of railway power supply, signal, communication and traction power supply professions.
3. The intelligent teaching system of the four-electric system of the high-speed railway based on the AI is characterized in that the virtual simulation modeling module comprises a twin body construction unit and a specification fusion unit, wherein the twin body construction unit is used for restoring the equipment layout, the pipeline trend and the electric connection relation of the four-electric system of the high-speed railway based on a BIM model, fusing teaching process data, student learning track and industry real fault data to construct a high-precision digital twin body, and the specification fusion unit is used for extracting equipment parameter standards, construction process requirements and fault handling criteria in a railway technical specification text and embedding constraint conditions of the digital twin body.
4. The intelligent teaching system of the four-electric system of the high-speed railway based on the AI is characterized by comprising a capability portrait construction unit, a learning path generation unit and an intelligent answering unit, wherein the capability portrait construction unit is used for analyzing learning tracks and practical training data of students through a decision tree algorithm and a neural network model, quantifying weak links of knowledge points and short plates of practical operation skills, constructing student capability portraits, the learning path generation unit is used for dynamically generating personalized learning paths comprising knowledge point completion, targeted practical training and fault simulation exercise based on the student capability portraits and industry post requirements, and the intelligent answering unit is used for responding to theoretical questions and practical operation confusion of students based on a railway professional knowledge base and a natural language processing algorithm, and providing accurate answering analysis and reference data recommendation.
5. The intelligent teaching system for the four-electric system of the high-speed railway is based on the AI, and is characterized in that the virtual-real teaching interaction module comprises a personalized real training scene building unit, a VR immersive training unit, an action recognition error correction unit and an AR remote guidance unit, wherein the personalized real training scene building unit is used for generating an adaptive real training scene for disassembling and assembling the four-electric system and operation and maintenance according to student capacity portraits and learning paths, the VR immersive training unit is used for providing a real equipment virtual disassembling and assembling environment and performing component disassembly and assembly training through a virtual hand model, the action recognition unit is used for capturing student real training actions in real time through a skeleton key point recognition algorithm and comparing a standard operation flow to perform three-dimensional visual error correction, and the AR remote guidance unit is used for remotely providing real-time voice guidance, operation step prompts and virtual labels through the AR and recording student real training data comprising operation accuracy, completion duration and error frequency.
6. The intelligent teaching system of the four-electric system of the high-speed railway based on the AI is characterized in that the cross-specialty fault simulation module comprises a fault association analysis unit, a linkage simulation unit and a deduction result output unit, wherein the fault association analysis unit is used for identifying coupling association relations of different specialty faults through an association rule mining algorithm based on trainee practical training data, the linkage simulation unit is used for simulating a conduction path and an influence range of the cross-specialty faults to conduct dynamic deduction and emergency treatment simulation, and the deduction result output unit is used for outputting fault evolution processes, treatment key nodes and scheme rationality evaluation fault deduction results.
7. The intelligent teaching system of the high-speed railway four-electric system based on the AI is characterized in that the teaching effect evaluation module comprises a procedural assessment unit, an actual operation skill assessment unit and an evaluation fusion unit, wherein the procedural assessment unit is used for quantifying knowledge point mastery degree, learning attitude and autonomous learning ability based on student learning tracks, classroom interaction data and staged practical training performance, the actual operation skill assessment unit is used for evaluating equipment operation proficiency, fault diagnosis accuracy and cross-professional collaborative handling ability according to fault deduction results and practical training operation data, and the evaluation fusion unit is used for fusing the procedural assessment and the actual operation skill assessment results to generate quantified teaching effect data comprising skill achievement rate, capability improvement range and post adaptation degree.
8. A method applied to the AI-based intelligent teaching system for four-electric systems of high-speed railway of any one of claims 1-7, the method comprising: Acquiring teaching process data, a student learning track and industry real fault data; based on the teaching process data, the student learning track and the industry real fault data, fusing BIM model and railway technical specification text data, constructing a digital twin body of a four-electric system of a high-speed railway, constructing a student capability image through a machine learning algorithm, generating a personalized learning path according to the student capability image and providing intelligent answering; constructing a personalized intelligent training scene according to the student capability portrait and the personalized learning path, performing VR immersive equipment disassembly and assembly training, performing three-dimensional visual error correction and AR remote dynamic teaching guidance through real-time action recognition, outputting student training data, performing cross-professional fault linkage simulation and dynamic deduction through an association rule mining algorithm, and outputting a fault deduction result; And establishing a binary evaluation system combining procedural assessment and practical skill assessment according to the fault deduction result and the practical training data of the students, and quantitatively outputting teaching effect data.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the AI-based intelligent teaching method of four-electric systems of a high-speed railway of claim 8.
10. A computer readable storage medium having stored thereon a computer program or instructions, which when executed, implements an AI-based intelligent teaching method for four-electric systems of a high-speed railway according to claim 8.

Description

AI-based intelligent teaching system and method for four-electric system of high-speed railway Technical Field The invention relates to the technical field of railway maintenance education, in particular to an intelligent teaching system and method for a four-electric system of a high-speed railway based on AI. Background With the progress of the railway network to intellectualization and high speed, higher standards are provided for four-electric system operation and maintenance training in the aspects of real-time state perception, dynamic skill adaptation, complex scene handling, high-efficiency capacity improvement and the like, namely, real-time linkage of teaching contents and on-site working conditions is required to be realized, diversified fault scenes are required to be responded quickly, wide-area teaching suitability is required to be provided, a full skill pedigree from basic principles to complex fault investigation is covered, stability of long-period training effects is required to be ensured, long-term retention of knowledge skills is ensured, the utilization efficiency of teaching resources is required to be improved, and personalized capacity cultivation is realized. Therefore, there is a need for an adaptive teaching system capable of sensing the learning state of a learner in real time, dynamically optimizing the pushing of teaching contents, and rapidly adapting to complex railway scenes, so as to ensure the efficient development of four-electric system operation and maintenance training in diversified railway scenes. However, the traditional four-electric system teaching has inherent defects that the teaching strategy is solidified, training is carried out only according to unified course outline and fixed real operation projects, multi-dimensional information such as trainee capability data, site load characteristics and environmental parameters are not fused, complex and changeable railway operation and maintenance requirements are difficult to adapt, the teaching accuracy is low, the skill conversion efficiency is insufficient, the reduction degree of the teaching hardware on simulation scenes such as strong electromagnetic interference, wide-temperature-range fluctuation and continuous vibration of a railway is low, the reality of the real operation training is greatly reduced due to equipment performance drift and signal transmission delay, the perception and coping capability of a trainee on site working conditions are influenced, the teaching resource management mode is rough, passive knowledge infusion energy consumption is high, active skill training lacks dynamic suitability, not only teaching resource waste is caused, learning fatigue of the trainee is easy to be aggravated, the design of a redundancy teaching path is avoided, the single teaching module fault can cause training interruption, the annual average fault influence time is long, the whole technology teaching accuracy faces the severe requirements on the cultivation period of the operation staff, and the teaching accuracy is difficult to use of the teaching resources. Disclosure of Invention The application provides an AI-based intelligent teaching system and method for a four-electric system of a high-speed railway, which are used for solving the problems of solidification, low accuracy and the like of teaching strategies in the prior art. The embodiment of the first aspect of the application provides an AI-based intelligent teaching system for a four-electric system of a high-speed railway, which comprises a teaching data acquisition module, a virtual simulation modeling module, an AI intelligent analysis engine, a virtual-real teaching interaction module, a cross-professional fault simulation module and a teaching effect evaluation module; the intelligent teaching system comprises a teaching data acquisition module, a virtual simulation modeling module, an AI intelligent analysis engine, a virtual reality teaching interaction module and a training effect evaluation module, wherein the teaching data acquisition module is used for acquiring teaching process data, student learning tracks and industry real fault data, the virtual simulation modeling module is used for fusing BIM model and railway technical specification text data based on the teaching process data, the student learning tracks and the industry real fault data, the AI intelligent analysis engine is used for analyzing the teaching data based on the digital twin, constructing student ability images through a machine learning algorithm to generate individualized learning paths and provide intelligent answering, the virtual reality teaching interaction module is used for constructing individualized real scenes according to the student ability images and the individualized learning paths, performing VR immersion equipment disassembly training, performing three-dimensional visual correction and AR remote dynamic teaching guidance through real-time action r