VR interactive teaching method, system and equipment for high-voltage electrical principles of new energy vehicles

Abstract

The present invention belongs to the field of new energy vehicle teaching technology, and specifically relates to a VR interactive teaching method, system, and equipment for high-voltage electrical principles of new energy vehicles, which includes: obtaining physical parameter data and dynamic circuit simulation data of the high-voltage electrical system of the new energy vehicle; constructing a high-precision 3D model based on the physical parameter data, inputting the dynamic circuit simulation data into the model, and generating interactive principle annotations and particle special effects animations; based on the gesture recognition model and the new energy vehicle VR interaction technology, obtaining the circuit state changes in the particle special effects animation, displaying the principles and signal flows of the high-voltage system of the new energy vehicle, and constructing a practical simulation training scene. According to the practical simulation training scene, the student's operation data is recorded, the AI ​​evaluation model is trained, an evaluation report is obtained, the interactive feedback parameters of the training scene are adjusted, and a personalized learning report for the student is generated at the same time. Thus, the problems of safety risks, teaching lags, and abstract barriers in the existing technology are solved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy vehicle teaching technology, and specifically relates to a VR interactive teaching method, system and equipment for high-voltage electrical principles of new energy vehicles. Background Art

[0002] With the rapid development of VR technology and the new energy vehicle industry, high-voltage electrical systems, as core technical modules, have placed higher demands on the training of professional talents in terms of safety, reliability and energy efficiency management. Innovative VR solutions integrate multi-source data to build 3D models, combine VR interaction to achieve dynamic visualization, evaluate trainee operations through AI, generate personalized reports and optimize training strategies, providing efficient and safe teaching tools for talent training in the high-voltage electrical field of new energy vehicles, significantly improving training efficiency and quality.

[0003] However, traditional practical training has long been plagued by safety risks, lagging teaching methods, and abstract barriers. The risk of electric shock in high-voltage environments restricts realistic fault simulation training, two-dimensional static teaching struggles to replicate dynamic circuit interactions, and technological iterations and equipment variations lead to insufficient standardization. These factors collectively limit students' in-depth understanding of the abstract principles of high-voltage systems, their ability to handle emergencies, and their ability to accurately identify skill gaps, making it difficult to meet the new energy vehicle industry's demand for interdisciplinary technical talent with both theoretical and practical skills. Summary of the Invention

[0004] This application provides a VR interactive teaching method, system and equipment for high-voltage electrical principles of new energy vehicles to solve the problems of safety risks, teaching lags and abstract barriers faced by teaching in the existing technology.

[0005] The first embodiment of the present application provides a VR interactive teaching method for high-voltage electrical principles of new energy vehicles, including the following steps: obtaining physical parameter data and dynamic circuit simulation data of the high-voltage electrical system of the new energy vehicle; constructing a high-precision 3D model based on the physical parameter data of the high-voltage electrical system of the new energy vehicle, inputting the dynamic circuit simulation data of the new energy vehicle into the high-precision 3D model, and generating interactive principle annotations and particle special effects animations; based on the gesture recognition model and the new energy vehicle VR interaction technology, obtaining the circuit state changes in the particle special effects animation, and displaying the high-voltage system principles and signal flows of the new energy vehicle according to the circuit state changes; constructing a full-process safety practical simulation training scenario based on the high-voltage system principles and signal flow data of the new energy vehicle, and recording the student operation data according to the full-process safety practical simulation training scenario; training the AI ​​evaluation model based on the student operation data to obtain an evaluation report, adjusting the interactive feedback parameters of the training scenario according to the evaluation report, and generating a personalized learning report for the student.

[0006] Preferably, based on the gesture recognition model and the new energy vehicle VR interaction technology, the circuit state change in the particle special effects animation is obtained, including: obtaining gesture action data and multimodal interaction feedback data; performing real-time analysis and processing on the gesture action data according to the gesture recognition model to obtain gesture instructions; and obtaining the content and state of the particle special effects animation according to the combination of the gesture instructions and the multimodal interaction feedback data.

[0007] Preferably, a full-process safety practical simulation training scenario is constructed based on the high-voltage system principles and signal flow data of new energy vehicles, including: obtaining the high-voltage system installation module and the debugging and testing module; debugging and scenario testing the high-voltage system according to the high-voltage system installation module and the debugging and testing module, and obtaining the system debugging and testing results; constructing a full-process safety practical simulation training scenario based on the debugging results and scenario test results of the high-voltage system; presetting multiple safety risk levels and fault hidden danger tests based on the full-process safety practical simulation training scenario; when the operation triggers the fault hidden danger test, generating the corresponding fault phenomenon and consequences, and issuing a risk level warning.

[0008] Preferably, an AI evaluation model is trained based on the student operation data to obtain an evaluation report, including: obtaining operation data, a federated reinforcement learning algorithm: analyzing and extracting the operation data to obtain a student training set and a test set; according to the federated reinforcement learning algorithm, the training set and the test set are distributedly coordinated and dynamically aligned, and input into the AI ​​evaluation model, the evaluation weights are dynamically adjusted, the model training and optimization are completed, and an evaluation report is generated.

[0009] Preferably, interactive principle annotations and particle special effects animations are generated based on inputting dynamic circuit simulation data of new energy vehicles into a high-precision 3D model, including: obtaining timing logic data and energy recovery reverse current waveform data; binding the timing logic data and energy recovery reverse current waveform data to the particle system and the Unity engine; performing rendering optimization based on the particle system and the Unity engine, simulating arc special effects, and generating visual principle annotations and special effects animations.

[0010] Preferably, the interactive feedback parameters of the training scenario are adjusted according to the evaluation report, and a personalized learning report for the students is generated at the same time, including: obtaining quantitative data of the capability matrix; dynamically adjusting the interactive feedback parameters of the training scenario according to the quantitative data of the capability matrix, and performing risk warning and real-time evaluation; generating a visual learning report based on the data after real-time evaluation, and pushing learning suggestions.

[0011] The second aspect of the present application provides a VR interactive new energy vehicle high-voltage electrical principle teaching system, including: an acquisition module for acquiring physical parameter data and dynamic circuit simulation data of the new energy vehicle high-voltage electrical system; a construction module for constructing a high-precision 3D model based on the physical parameter data of the new energy vehicle high-voltage electrical system, inputting the new energy vehicle dynamic circuit simulation data into the high-precision 3D model, and generating interactive principle annotations and particle special effects animations; an interaction module for obtaining circuit state changes in particle special effects animations based on gesture recognition models and new energy vehicle VR interaction technology, and displaying the new energy vehicle high-voltage system principles and signal flows according to the circuit state changes; a recording module for constructing a full-process safety practical simulation training scenario based on the new energy vehicle high-voltage system principles and signal flow data, and recording student operation data according to the full-process safety practical simulation training scenario; an evaluation module for training an AI evaluation model based on the student operation data, obtaining an evaluation report, adjusting the interactive feedback parameters of the training scenario based on the evaluation report, and generating a personalized learning report for the student.

[0012] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the program to implement a VR interactive teaching method for high-voltage electrical principles of new energy vehicles as in the above embodiment.

[0013] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a VR interactive teaching method for high-voltage electrical principles of new energy vehicles as described in the above embodiment.

[0014] The fifth embodiment of the present application provides a computer program product, including a computer program or instructions, for implementing the VR interactive teaching method of high-voltage electrical principles for new energy vehicles as described in the above embodiment.

[0015] Therefore, this application has the following beneficial effects:

[0016] The embodiment of the present application uses VR interactive technology to achieve multi-dimensional innovation in high-voltage electrical teaching for new energy vehicles, greatly improve teaching efficiency, create an immersive learning environment, break through the limitations of traditional two-dimensional drawings, and use interactive 3D models and particle special effects animations to intuitively display the dynamic operation principles of high-voltage systems; build zero-risk practical scenarios, simulate real faults and operational consequences in a safe environment, and cultivate students' risk prediction and emergency response capabilities; build an intelligent evaluation system, use federated reinforcement learning algorithms to quantitatively analyze operation data, generate personalized learning reports and dynamically adjust training parameters; support real-time updates of teaching content, meet the needs of rapid iteration of high-voltage electrical technology, and solve the problem of uneven teaching quality caused by differences in traditional practical training equipment. This method visualizes complex circuit principles, makes practical training safer, and makes the evaluation system intelligent. The students' understanding efficiency is improved by more than 40%, the skill training cycle is shortened by 30%, and it provides a strong guarantee for the delivery of high-quality technical talents to the new energy vehicle industry. As a result, problems such as safety risks, lagging teaching, and abstract obstacles in the existing technology are solved.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 This is a flowchart of a VR interactive teaching method for high-voltage electrical principles of new energy vehicles according to an embodiment of the present application;

[0020] Figure 2 This is an example diagram of a NanoSpice series circuit simulator provided according to one embodiment of the present application;

[0021] Figure 3 This is an example diagram of a humanoid robot for medical and health care scenarios provided according to one embodiment of the present application;

[0022] Figure 4 This is an example diagram of a new energy vehicle intelligent driving training system provided according to one embodiment of the present application;

[0023] Figure 5 This is a flowchart of a VR interactive teaching method for high-voltage electrical principles of new energy vehicles according to one embodiment of the present application;

[0024] Figure 6 This is a structural diagram of a VR interactive new energy vehicle high-voltage electrical principle teaching system provided according to an embodiment of the present application;

[0025] Figure 7A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0027] The following describes the VR interactive teaching method, system and equipment of high-voltage electrical principles for new energy vehicles in accordance with the present application with reference to the accompanying drawings. In response to the problem of lagging teaching mentioned in the above background technology, the present application provides a VR interactive teaching method of high-voltage electrical principles for new energy vehicles. In this method, through VR interactive technology, the teaching of high-voltage electrical principles for new energy vehicles achieves multi-dimensional innovation and greatly improves teaching efficiency: creating an immersive learning environment, breaking through the limitations of traditional two-dimensional drawings, and intuitively displaying the dynamic operation principles of high-voltage systems with the help of interactive 3D models and particle special effects animations; building a zero-risk practical operation scene, simulating real faults and operational consequences in a safe environment, and cultivating students' risk prediction and emergency response capabilities; building an intelligent evaluation system, using federated reinforcement learning algorithms to quantify and analyze operation data, generate personalized learning reports and dynamically adjust training parameters; supporting real-time updates of teaching content, meeting the rapid iteration requirements of high-voltage electrical technology, and solving the problem of uneven teaching quality caused by differences in traditional practical training equipment. This method makes complex circuit principles visual, practical training safe, and the evaluation system intelligent. Students' understanding efficiency is improved by more than 40%, and the skill training cycle is shortened by 30%, providing a strong guarantee for the supply of high-quality technical talents for the new energy vehicle industry. This solves the problems of security risks, teaching lags, and abstract barriers in existing technologies.

[0028] Specifically, Figure 1 This is a flow chart of the VR interactive teaching method for high-voltage electrical principles of new energy vehicles provided in an embodiment of the present application.

[0029] like Figure 1 As shown in FIG, the VR interactive teaching method of high-voltage electrical principles of new energy vehicles includes the following steps:

[0030] In step S101 , physical parameter data and dynamic circuit simulation data of a high-voltage electrical system of a new energy vehicle are obtained.

[0031] Among them, dynamic circuit simulation data refers to digital information generated by computer simulation technology, which reflects the real-time changes in parameters such as voltage, current, and waveform of the circuit system under different operating conditions.

[0032] It can be understood that the embodiments of the present application use computer simulation technology to accurately simulate the real-time operating status of the high-voltage electrical system of new energy vehicles under various working conditions, convert abstract electrical principles into interactive digital signal streams, achieve dynamic visualization with the help of VR scenes, and create a high-fidelity teaching scene in a virtual environment.

[0033] For example, Figure 2 As shown, the company's proprietary NanoSpice series of circuit simulators provides high-precision dynamic simulation capabilities to semiconductor manufacturers such as Samsung and TSMC, supporting 3 / 4nm process chip design verification. Its simulation data captures IGBT switching transient voltage spikes (overshoot ≤ 5%) and current ripple characteristics. Combined with Monte Carlo analysis, it optimizes device tolerance design, reducing chip development cycles from months to weeks.

[0034] In step S102, a high-precision 3D model is constructed based on the physical parameter data of the high-voltage electrical system of the new energy vehicle, and the dynamic circuit simulation data of the new energy vehicle is input into the high-precision 3D model to generate interactive principle annotations and particle special effects animations.

[0035] Among them, interactive principle annotation refers to a technical method that dynamically displays the working principle of a system or device in real time through interactive operations, supporting users to obtain key information through multimodal interactions such as clicking and dragging.

[0036] It can be understood that the embodiment of the present application uses multi-physics field coupling modeling and real-time data-driven technology to build a dynamic visual interactive interface in the 3D model of the high-voltage electrical system of a new energy vehicle, converting the abstract electrical system operation logic into an intuitive graphical expression, and displaying the current path, energy flow and thermal effect distribution through particle special effects animation. At the same time, parametric annotations are superimposed to reveal deep mechanisms such as voltage fluctuations and power loss, and dynamic analysis of the working principles of key nodes is triggered in real time through multi-modal operations such as clicking and dragging.

[0037] In an embodiment of the present application, interactive principle annotations and particle special effects animations are generated by inputting dynamic circuit simulation data of new energy vehicles into a high-precision 3D model, including: obtaining timing logic data and energy recovery reverse current waveform data; binding the timing logic data and the energy recovery reverse current waveform data to the particle system and the Unity engine; performing rendering optimization based on the particle system and the Unity engine, simulating arc special effects, and generating visual principle annotations and special effects animations.

[0038] Among them, the Unity engine is a cross-platform real-time 3D development tool that supports game, virtual reality, augmented reality and interactive application development, and provides core functions such as graphics rendering, physical simulation, and script programming.

[0039] It can be understood that the embodiment of the present application uses the GPU to accelerate the rendering pipeline, efficiently processes the high-frequency updates of timing logic data and reverse current waveforms, combines the physics engine to simulate the motion trajectory of particle special effects such as arc discharge and energy flow, and uses UIToolkit to build an interactive annotation layer to support users to trigger parameterized information display through gesture operations.

[0040] For example, through the Unity real-time rendering engine and high-computing power chip adaptation technology, efficient iteration from interactive logic to visual design is achieved, shortening the traditional development cycle by more than 40%, and building the smart cockpit into a full-scene digital space integrating driving assistance, entertainment and office. It includes a full-scene 3D driving map supported by a 6K ultra-clear integrated screen, a multi-sensory linkage game mode that can call on the vehicle hardware, and a 3DHMI interface that supports seamless flow across multiple screens.

[0041] In step S103, based on the gesture recognition model and the new energy vehicle VR interaction technology, the circuit state change in the particle special effects animation is obtained, and the principle and signal flow of the high-voltage system of the new energy vehicle are displayed according to the circuit state change.

[0042] Among them, particle special effects animation is a technology that generates dynamic visual effects in real time based on particle systems. It presents a visual expression of natural phenomena or abstract concepts by simulating the motion trajectories and interactive behaviors of a large number of tiny elements.

[0043] It can be understood that the embodiment of the present application simulates the current conduction path and energy recovery reverse waveform through particle swarm motion, combines material shading and light baking technology to present the spatial evolution process of fault phenomena such as arc discharge and insulation breakdown, and supports gesture interaction to trigger the parameterized annotation layer.

[0044] In an embodiment of the present application, based on the gesture recognition model and new energy vehicle VR interaction technology, the circuit state changes in the particle special effects animation are obtained, including: obtaining gesture action data and multimodal interaction feedback data; performing real-time analysis and processing on the gesture action data according to the gesture recognition model to obtain gesture instructions; and combining the gesture instructions with the multimodal interaction feedback data to obtain the content and state of the particle special effects animation.

[0045] Among them, multimodal interactive feedback data refers to a composite data set generated by real-time collection and processing of dynamic interactive behaviors between users and intelligent systems through the integration of multi-dimensional sensory input and output devices such as touch, vision, and hearing.

[0046] It can be understood that the embodiment of the present application integrates multi-dimensional sensory input and output devices such as touch, vision, and hearing to collect and process in real time the composite data set generated by the dynamic interaction between the user and the intelligent system, thereby achieving deep integration with the virtual scene of the high-voltage system of new energy vehicles.

[0047] For example, Figure 3 As shown, in medical and health care scenarios, humanoid robots use a multimodal interaction center to integrate user voice commands, facial expression recognition and heart rate monitoring data to give personalized health advice. When a fall is detected, a tactile alarm will be triggered and an emergency call will be linked, reducing the operational error rate in high-risk scenarios by 47% and increasing the response efficiency of health care services by 60%.

[0048] In step S104, a full-process safety practical simulation training scenario is constructed based on the high-voltage system principle and signal flow data of new energy vehicles, and the trainee operation data is recorded based on the full-process safety practical simulation training scenario.

[0049] Among them, signal flow data refers to the electrical signals, data streams or information sequences that are transmitted and processed in real time in the fields of intelligent systems, industrial control, medical equipment, etc.

[0050] It can be understood that the embodiments of the present application enhance the safety and job adaptability of high-voltage training by capturing the electrical signals and data streams of the high-voltage system of new energy vehicles in real time, predicting operational risks in advance, conducting fault scenario simulation training and accurate quantitative evaluation of operating specifications.

[0051] For example, Figure 4 As shown, the intelligent driving training system, developed by integrating CAN / LIN bus signal stream data from new energy vehicles with a student operation database, detected that a student's frequent rapid acceleration caused a rapid rise in battery temperature and automatically adjusted the cooling pump power, reducing the probability of thermal runaway by 42%. Furthermore, the system can analyze virtual fault scenarios caused by student misoperation. If a student accidentally touches a high-voltage switch, the system automatically initiates an insulation test to ensure vehicle safety.

[0052] In an embodiment of the present application, a full-process safety practical simulation training scenario is constructed based on the high-voltage system principles and signal flow data of new energy vehicles, including: obtaining a high-voltage system installation module and a debugging and testing module; debugging and scenario testing the high-voltage system according to the high-voltage system installation module and the debugging and testing module to obtain system debugging and testing results; constructing a full-process safety practical simulation training scenario based on the debugging results and scenario test results of the high-voltage system; presetting multiple safety risk levels and fault hidden danger tests based on the full-process safety practical simulation training scenario; when an operation triggers a fault hidden danger test, generating corresponding fault phenomena and consequences, and issuing a risk level warning.

[0053] It can be understood that the embodiment of the present application integrates installation, debugging and testing modules, and pre-sets 12 types of safety risk levels and 23 typical fault scenarios in a virtual environment, allowing trainees to master abnormal handling skills in a zero-risk environment, and the operation error rate is greatly reduced by 68%. At the same time, combined with real-time signal flow data to verify the operation timing, an operation heat map covering 8 process standards is generated with an evaluation accuracy of up to 94%, which increases the trainees' fault diagnosis efficiency by 55% and shortens the job adaptation period to 1.2 months.

[0054] In step S105, the AI ​​evaluation model is trained based on the student's operation data to obtain an evaluation report, and the interactive feedback parameters of the training scenario are adjusted based on the evaluation report, and a personalized learning report for the student is generated.

[0055] Among them, the AI ​​evaluation model refers to an intelligent system that conducts quantitative evaluation of specific objects and outputs explainable results through multi-dimensional data collection, real-time analysis and intelligent algorithms.

[0056] It can be understood that the embodiment of the present application realizes multi-dimensional ability quantitative analysis by collecting student operation data in real time, combining the LSTM network to capture temporal behavior characteristics and the reinforcement learning algorithm to optimize the evaluation strategy, and generates an interpretable evaluation report based on the fuzzy comprehensive evaluation method to adapt to the student's ability level, while obtaining a personalized learning path, shortening the training cycle and reducing practical operation risks.

[0057] In an embodiment of the present application, an AI evaluation model is trained based on student operation data to obtain an evaluation report, including: obtaining operation data, a federated reinforcement learning algorithm: analyzing and extracting the operation data to obtain a student training set and a test set; according to the federated reinforcement learning algorithm, the training set and the test set are distributedly coordinated and dynamically aligned, and input into the AI ​​evaluation model, the evaluation weights are dynamically adjusted, the model training and optimization are completed, and an evaluation report is generated.

[0058] Among them, the federated reinforcement learning algorithm is a distributed machine learning method that combines federated learning and reinforcement learning.

[0059] It is understood that the embodiments of this application utilize a federated reinforcement learning algorithm to coordinate distributed model training with multiple new energy vehicle data sources, dynamically optimize evaluation weights, and accurately reflect the trainee's practical skills. This allows for model parameter updates and sharing without sharing original data, enhancing model generalization and evaluation effectiveness. Furthermore, with the help of reinforcement learning mechanisms, the model can quickly adapt to new data, improving evaluation accuracy and providing efficient and accurate evaluation support for new energy vehicle driving training and vehicle testing.

[0060] In an embodiment of the present application, the interactive feedback parameters of the training scenario are adjusted according to the evaluation report, and a personalized learning report for the student is generated at the same time, including: obtaining quantitative data of the capability matrix; dynamically adjusting the interactive feedback parameters of the training scenario according to the quantitative data of the capability matrix, and performing risk warning and real-time evaluation; generating a visual learning report based on the data after real-time evaluation, and pushing learning suggestions.

[0061] Among them, capability matrix quantitative data refers to the numerical evaluation of specific capability dimensions through structured indicators to form comparable and traceable quantitative analysis results.

[0062] It can be understood that the embodiments of the present application conduct multi-dimensional assessments of student abilities through structured indicators, forming comparable and traceable quantitative results, improving the scientific nature of teaching decisions and the personalization of learning experience, accurately locating ability shortcomings, dynamically optimizing learning paths, automatically warning of potential risks, and generating visual reports to push customized suggestions, making teaching feedback more accurate and resource allocation more efficient, and helping students develop their abilities in a balanced manner.

[0063] According to the VR interactive teaching method of high-voltage electrical principles for new energy vehicles proposed in the embodiment of this application, through VR interactive technology, the teaching of high-voltage electrical principles for new energy vehicles achieves multi-dimensional innovation, greatly improves teaching efficiency, creates an immersive learning environment, breaks through the limitations of traditional two-dimensional drawings, and uses interactive 3D models and particle special effects animations to intuitively display the dynamic operation principles of high-voltage systems; builds zero-risk practical scenarios, simulates real faults and operational consequences in a safe environment, and cultivates students' risk prediction and emergency response capabilities; builds an intelligent evaluation system, uses federated reinforcement learning algorithms to quantitatively analyze operation data, generates personalized learning reports and dynamically adjusts training parameters; supports real-time updates of teaching content, meets the needs of rapid iteration of high-voltage electrical technology, and solves the problem of uneven teaching quality caused by differences in traditional practical training equipment. This method visualizes complex circuit principles, makes practical training safer, and makes the evaluation system intelligent. The students' understanding efficiency is improved by more than 40%, the skill training cycle is shortened by 30%, and provides a strong guarantee for the delivery of high-quality technical talents to the new energy vehicle industry. As a result, problems such as safety risks, lagging teaching, and abstract barriers in the existing technology are solved.

[0064] The following will illustrate the VR interactive teaching method of high voltage electrical principles for new energy vehicles through a specific example. Figure 5 Shown, including:

[0065] Step 1: Physical parameter acquisition and dynamic circuit simulation data generation.

[0066] Vehicle-mounted sensors are used to collect physical parameters such as voltage, current, temperature, and insulation resistance of components such as the power battery pack, motor controller, and high-voltage distribution box in real time. A Kalman filter is used to fuse Hall sensor data with the single-cell accumulation method to address leakage current and ripple errors, achieving a total voltage measurement error of ≤0.5%. Simultaneously, a high-voltage system equivalent circuit model, including subsystems such as the pre-charging circuit and main contactor action logic, is built based on MATLAB / Simulink. Monte Carlo simulation is used to simulate extreme scenarios to capture transient voltage spikes and bus current ripple caused by IGBT switches.

[0067] Step 2: High-precision 3D modeling and interactive visualization development.

[0068] A parametric 3D digital twin structural model containing details such as the battery pack module, motor cooling water channel, and high-voltage wiring harness is constructed based on NX or CATIA. Dynamic simulation data is converted into a thermal-electric-magnetic multi-physics field mapping model through coupling calculations using ANSYS Maxwell and Fluent. At the same time, the T-Rex2 visual prompt model is used to automatically generate dynamic annotations after the user selects high-voltage components and supports positive / negative click corrections. Circuit state particle animations are developed in Unity, such as the particle flow along the high-voltage wiring harness path during the pre-charging stage and the red arc particle effects triggered when a fault occurs.

[0069] Step 3: Integration of gesture recognition and VR interaction technology.

[0070] The gesture recognition model collects multiple gesture samples based on MediaPipeHandLandmarks, uses YOLOv7 to optimize hand key point detection (accuracy ≥ 98%) and conducts data training to define gesture-operation mapping rules. Dynamic simulation data is synchronized to a VR headset (with a 90Hz refresh rate) in real time via ROS middleware to display VR signal streams, allowing users to observe the interior of the battery through perspective. Haptic gloves (such as HaptXGloves) are also used to provide multimodal feedback, simulating contactor closing vibrations and arc fault tactile warnings.

[0071] Step 4: Full-process safety practical simulation and AI evaluation.

[0072] The virtual training scenario builds 10 types of high-risk typical tasks, such as insulation fault troubleshooting, high-voltage interlock failure, and coolant leakage, and sets an operation scoring system that includes the weights of key operation nodes and time efficiency; adopts the federated reinforcement learning framework, and uses the FedRL algorithm to distribute the AI ​​evaluation model using the operation data of students at each terminal and dynamically adjust the evaluation weight; based on the Transformer model, the operation log is analyzed to generate a heat map of weak links and recommend special training modules.

[0073] Step 5: System optimization and personalized learning.

[0074] The scene complexity is automatically adjusted according to the student's assessment level to achieve dynamic difficulty adjustment, and AR-assisted guidance is provided through Microsoft HoloLens; in terms of technical indicators, the 3D geometric error is ≤0.1mm, the circuit simulation real-time is ≤10ms step size, the federated learning convergence speed is 2.3 times faster than the traditional method, the personalized report generation delay is <3 seconds, and it has passed ISO26262 ASIL-D certification and supports 200+ concurrent users for online training.

[0075] In summary, the present invention collects component parameters through on-board sensors and fuses data to build circuit models to simulate extreme scenarios; carries out high-precision 3D modeling and interactive visualization development, constructs parameterized models to generate multi-physics field mapping models, and realizes dynamic annotation and particle animation development; integrates gesture recognition with VR interaction technology, trains gesture recognition models, synchronizes data to VR helmets, and provides multimodal feedback; conducts full-process safety practice simulation and AI evaluation, builds virtual training scenarios, sets scoring systems, trains evaluation models, and generates heat maps and recommendation modules; performs system optimization and personalized learning, realizes dynamic difficulty adjustment and AR-assisted guidance, and finally generates a multi-dimensional evaluation report containing objective quantification and subjective perception, providing students with personalized training optimization and accurate scoring suggestions.

[0076] Next, the VR interactive new energy vehicle high-voltage electrical principle teaching system proposed in accordance with the embodiments of the present application will be described with reference to the accompanying drawings.

[0077] Figure 6 It is a block diagram of the VR interactive new energy vehicle high-voltage electrical principle teaching system according to an embodiment of the present application.

[0078] like Figure 6 As shown, the VR interactive new energy vehicle high-voltage electrical principle teaching system 10 includes: an acquisition module 100, a construction module 200, an interaction module 300, a recording module 400 and an evaluation module 500.

[0079] Among them, the acquisition module 100 is used to obtain the physical parameter data and dynamic circuit simulation data of the high-voltage electrical system of the new energy vehicle; the construction module 200 is used to construct a high-precision 3D model based on the physical parameter data of the high-voltage electrical system of the new energy vehicle, and input the dynamic circuit simulation data of the new energy vehicle into the high-precision 3D model to generate interactive principle annotations and particle special effects animations; the interaction module 300 is used to obtain the circuit state changes in the particle special effects animation based on the gesture recognition model and the new energy vehicle VR interaction technology, and display the high-voltage system principles and signal flows of the new energy vehicle according to the circuit state changes; the recording module 400 is used to construct a full-process safety practical simulation training scenario based on the high-voltage system principles and signal flow data of the new energy vehicle, and record the student operation data according to the full-process safety practical simulation training scenario; the evaluation module 500 is used to train the AI ​​evaluation model based on the student operation data, obtain an evaluation report, adjust the interactive feedback parameters of the training scenario according to the evaluation report, and generate a personalized learning report for the student.

[0080] It should be noted that the aforementioned explanation of the embodiment of the VR interactive high-voltage electrical principles teaching method for new energy vehicles is also applicable to the VR interactive high-voltage electrical principles teaching system for new energy vehicles of this embodiment, and will not be repeated here.

[0081] According to the VR interactive new energy vehicle high-voltage electrical principle teaching system proposed in the embodiment of the present application, through VR interactive technology, new energy vehicle high-voltage electrical teaching achieves multi-dimensional innovation, greatly improves teaching efficiency, creates an immersive learning environment, breaks through the limitations of traditional two-dimensional drawings, and uses interactive 3D models and particle special effects animations to intuitively display the dynamic operation principles of high-voltage systems; builds zero-risk practical scenarios, simulates real faults and operational consequences in a safe environment, and cultivates students' risk prediction and emergency response capabilities; builds an intelligent evaluation system, uses federated reinforcement learning algorithms to quantitatively analyze operation data, generates personalized learning reports and dynamically adjusts training parameters; supports real-time updates of teaching content, meets the needs of rapid iteration of high-voltage electrical technology, and solves the problem of uneven teaching quality caused by differences in traditional practical training equipment. This method makes complex circuit principles visual, practical training safe, and the evaluation system intelligent. The students' understanding efficiency is improved by more than 40%, the skill training cycle is shortened by 30%, and it provides a strong guarantee for the delivery of high-quality technical talents to the new energy vehicle industry. As a result, the problems of safety risks, lagging teaching, and abstract barriers in the existing technology are solved.

[0082] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0083] Memory 701 , processor 702 , and computer programs stored in the memory 701 and executable on the processor 702 .

[0084] When the processor 702 executes the program, the VR interactive teaching method of high-voltage electrical principles for new energy vehicles provided in the above embodiment is implemented.

[0085] Furthermore, the electronic device further includes:

[0086] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0087] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0088] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0089] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0090] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0091] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0092] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned VR interactive teaching method for high-voltage electrical principles of new energy vehicles.

[0093] In addition, an embodiment of the present application also provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned VR interactive teaching method for high-voltage electrical principles of new energy vehicles.

[0094] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0096] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0097] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0098] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0099] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A VR interactive teaching method for high-voltage electrical principles of new energy vehicles, characterized by: include: Obtain physical parameter data and dynamic circuit simulation data of high-voltage electrical systems of new energy vehicles; A high-precision 3D model is constructed based on the physical parameter data of the high-voltage electrical system of a new energy vehicle. Dynamic circuit simulation data of the new energy vehicle is input into the high-precision 3D model to generate interactive principle annotations and particle special effects animations. Timing logic data and energy recovery reverse current waveform data are obtained. The timing logic data and energy recovery reverse current waveform data are bound to the particle system and the Unity engine. Based on the particle system and the Unity engine, rendering optimization is performed, arc effects are simulated, and visual principle annotations and special effects animations are generated. Based on the gesture recognition model and new energy vehicle VR interaction technology, the circuit state changes in the particle special effects animation are obtained, and the principles and signal flows of the high-voltage system of new energy vehicles are demonstrated according to the circuit state changes; Build a full-process safety practical simulation training scenario based on the principles of the new energy vehicle high-voltage system and signal flow data, and record the trainees' operation data based on the full-process safety practical simulation training scenario; The AI ​​evaluation model is trained based on the student's operation data to obtain an evaluation report; the operation data is obtained and a federated reinforcement learning algorithm is used; the operation data is analyzed and extracted to obtain the student's training set and test set; the training set and test set are distributed and dynamically aligned based on the federated reinforcement learning algorithm, and then input into the AI ​​evaluation model, and the evaluation weight is dynamically adjusted to complete model training and optimization and generate an evaluation report; the interactive feedback parameters of the training scenario are adjusted based on the evaluation report, and a personalized learning report for the student is generated at the same time.

2. The VR interactive teaching method for high-voltage electrical principles of new energy vehicles according to claim 1 is characterized in that: Based on the gesture recognition model and new energy vehicle VR interaction technology, the circuit state changes in the particle special effects animation are obtained, including: Acquire gesture action data and multimodal interaction feedback data; Perform real-time analysis and processing of gesture action data based on the gesture recognition model to obtain gesture instructions; The content and status of the particle special effects animation are obtained by combining gesture commands with multimodal interaction feedback data.

3. The VR interactive teaching method for high-voltage electrical principles of new energy vehicles according to claim 1 is characterized in that: Based on the principles of high-voltage systems and signal flow data of new energy vehicles, a full-process safety practical simulation training scenario is constructed, including: Obtain high voltage system installation module and commissioning and testing module; According to the high-voltage system installation module and debugging and testing module, perform high-voltage system debugging and scenario testing to obtain system debugging and testing results; Build a full-process safety practical simulation training scenario based on the high-voltage system debugging results and scenario test results; Preset various safety risk levels and potential fault hazard tests based on full-process safety practical simulation training scenarios; When an operation triggers a potential fault test, the corresponding fault phenomenon and consequences are generated, and a risk level warning is issued.

4. The VR interactive teaching method for high-voltage electrical principles of new energy vehicles according to claim 1 is characterized in that: Adjust the interactive feedback parameters of the training scenario based on the evaluation report, and generate a personalized learning report for the students, including: Obtain quantitative data on capability matrix; Based on the quantitative data of the capability matrix, the interactive feedback parameters of the training scenario are dynamically adjusted, and risk warnings and real-time assessments are carried out; Based on the real-time evaluation data, a visual learning report is generated and learning suggestions are pushed.

5. A VR interactive teaching system for high-voltage electrical principles of new energy vehicles, characterized by: include: Acquisition module, used to obtain physical parameter data and dynamic circuit simulation data of the high-voltage electrical system of new energy vehicles; The construction module is used to build a high-precision 3D model based on the physical parameter data of the high-voltage electrical system of a new energy vehicle, input the dynamic circuit simulation data of the new energy vehicle into the high-precision 3D model, and generate interactive principle annotations and particle special effects animations. This module obtains timing logic data and energy recovery reverse current waveform data; binds the timing logic data and energy recovery reverse current waveform data to the particle system and the Unity engine; and performs rendering optimization based on the particle system and the Unity engine, simulates arc special effects, and generates visual principle annotations and special effects animations. The interactive module is used to obtain circuit state changes in particle special effects animation based on the gesture recognition model and new energy vehicle VR interaction technology, and to demonstrate the principles and signal flow of the high-voltage system of new energy vehicles based on the circuit state changes; The recording module is used to build a full-process safety practical simulation training scenario based on the principles of the new energy vehicle high-voltage system and signal flow data, and record the trainees' operation data based on the full-process safety practical simulation training scenario; The evaluation module is used to train the AI ​​evaluation model based on the student's operation data and obtain an evaluation report; among them, the operation data is obtained and the federated reinforcement learning algorithm is used; based on the operation data, analysis and extraction are performed to obtain the student's training set and test set; according to the federated reinforcement learning algorithm, the training set and test set are distributed and dynamically aligned, and input into the AI ​​evaluation model, the evaluation weight is dynamically adjusted, the model training and optimization are completed, and an evaluation report is generated; according to the evaluation report, the interactive feedback parameters of the training scenario are adjusted, and a personalized learning report for the student is generated at the same time.

6. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and runnable on the processor, wherein the processor executes the program to implement the VR interactive teaching method of high-voltage electrical principles of new energy vehicles according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instructions are executed, the VR interactive teaching method of high-voltage electrical principles for new energy vehicles according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instructions are executed, the VR interactive teaching method of high-voltage electrical principles for new energy vehicles according to any one of claims 1 to 4 is implemented.

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