Rail transit engineering simulation training system based on virtual reality and artificial intelligence

Abstract

The invention discloses a rail transit engineering simulation practical training system based on virtual reality and artificial intelligence, and relates to the field of rail transit engineering, and the system comprises a virtual reality scene module which constructs a whole-process virtual scene of a subway station from planning to completion; the task simulation module is used for designing a station construction task, and the task simulation module further comprises an operation guidance unit and a tool use training unit; the data acquisition and analysis module is used for acquiring student operation data and analyzing the student operation data to obtain an analysis report; and the feedback and evaluation module is used for providing real-time feedback according to the analysis report and generating an operation progress report.

Description

Technical Field

[0001] The present invention relates to the field of rail transit engineering, and in particular to a rail transit engineering simulation training system based on virtual reality and artificial intelligence. Background Art

[0002] In the traditional rail transit engineering training mode, it mainly relies on classroom theory explanation, animation demonstration, on-site observation and a small amount of specific task operation. However, this method has obvious shortcomings in simulating the real construction environment. Taking the construction of subway stations as an example, its construction project has many complex characteristics: the project is large-scale, involving a large amount of manpower and material resources; the investment amount is extremely high, and the resource management requirements are strict; the structural safety responsibility is heavy, and any slight mistake may lead to serious consequences; the construction process is numerous and complex, covering multiple links from foundation construction to equipment installation; the node procedures for hidden acceptance are extremely complicated, requiring practitioners to have a deep understanding of construction details and standards.

[0003] These characteristics require rail transit industry practitioners to have solid professional skills and rich on-site practical experience. However, traditional practical operation training faces many difficulties: on the one hand, it is difficult to fully present the complex construction site due to the strict restrictions of site conditions; on the other hand, the limited equipment cannot meet the needs of a large number of students for diversified operation training at the same time, and the purchase and maintenance costs of equipment are high. More importantly, there are significant safety risks in on-site operations, which prevents students from fully participating in practice in real scenarios, resulting in a serious lack of practical training opportunities.

[0004] Virtual reality technology has evolved from early 3D graphics displays to a higher level of immersive experience systems. High-precision motion capture technology (such as gesture recognition and full-body tracking) and 3D modeling can provide trainees with a close-to-real operating experience. In addition, tactile feedback technology in VR devices is gradually being used to improve the realism of operations. The new virtual reality engine can generate highly customizable training scenarios, and the system can quickly develop different rail transit operation or construction scenarios through scenario templates. These scenarios can be flexibly expanded through modular design to meet different practical training needs.

[0005] Therefore, there is an urgent need for a rail transit engineering simulation training system based on virtual reality and artificial intelligence to collect students' learning data through task simulation, so as to provide a basis for future teaching analysis and personalized guidance. Summary of the invention

[0006] To solve the above problems, this application provides a rail transit engineering simulation training system based on virtual reality and artificial intelligence, which mainly restores the entire construction process of subway stations, provides students with an immersive learning experience through virtual reality technology, and enables students to intuitively understand each link of subway station construction in a virtual environment. The system will also collect students' learning data through task simulation to provide a basis for future teaching analysis and personalized guidance.

[0007] The rail transit engineering simulation training system based on virtual reality and artificial intelligence provided in the present application includes a virtual reality scene module: the virtual reality scene module constructs a virtual scene of the entire process of a subway station from planning to completion; Task simulation module: the task simulation module is used to design station construction tasks, and the task simulation module also includes an operation guidance unit and a tool use training unit; Data collection and analysis module: The data collection and analysis module is used to collect student operation data and analyze the student operation data to obtain an analysis report; Feedback and evaluation module: The feedback and evaluation are used to provide real-time feedback based on the analysis report and generate an operation progress report.

[0008] Preferably, the method for constructing the virtual reality scene module specifically includes constructing an outdoor venue virtual unit, constructing an indoor structure virtual unit and constructing an effect enhancement unit, and the outdoor venue virtual unit and the indoor structure virtual unit are fused and corrected to obtain the virtual reality scene module.

[0009] Preferably, the specific contents of the construction of the outdoor venue virtual unit are: Using VR immersive virtual reality simulation technology, for outdoor venue simulation The GPS module carried by a high-precision drone is used to photograph the construction red line range around the subway station and record the precise location of each measuring point to obtain the initial image; The initial images are stitched together to generate an orthophoto terrain model, and the orthophoto terrain model is denoised using 3ds Max or Blender; Import the orthophoto terrain model into the GIS platform, integrate the orthophoto terrain model with the site's geographic information data, and use the coordinate calibration tool to unify the coordinate system of the 3D model and GIS data to achieve precise spatial alignment; Preferably, the contents of the construction of the indoor structure virtual unit are: Obtain design drawings and use Auto Desk Revit 3D information simulation software to build an internal 3D virtual model based on the design drawings; Write an automated plug-in in Python to lightweight the internal 3D virtual model and export it to FBX format; The content of the effect enhancement unit is as follows: Real-time rendering technology is used to increase lighting and weather effects, a particle system is used to simulate dynamic elements in the environment, and OpenAL three-dimensional audio technology is used to integrate the collected sound effects into the virtual scene.

[0010] Preferably, the specific contents of the task simulation module are: Build a Transformer model, input the construction organization of subway station construction into the Transformer model, and obtain the knowledge graph of subway station construction; Convert nodes in the knowledge graph into low-dimensional vector representations through techniques such as word embedding or graph embedding; Use the Word2Vec word embedding method to vectorize the names and descriptions of construction processes, or use the GraphEmbedding algorithm to embed the nodes of the entire knowledge graph; A convolutional neural network variant algorithm is used to extract and learn features from the knowledge graph, and then the construction task processes of subway stations are trained.

[0011] Preferably, the specific contents of the data collection and analysis module include: The data acquisition and analysis module is equipped with a sensor; Recording the operation data of students in the virtual environment, wherein the operation data includes real-time data and result data, wherein the real-time data includes hand movements and body postures collected by sensors, and the result data includes task operation time, task accuracy, and task completion percentage; Identify missing values, outliers, duplicate values, and erroneous values ​​in operational data and clean them; The cleaning process includes counting missing values ​​and erroneous values, confirming the knowledge graph location of the missing values ​​and marking them, eliminating abnormal values ​​and duplicate values, and generating an elimination table.

[0012] Preferably, the specific contents of the feedback and evaluation module include: The feedback and evaluation module is preset with operational specification criteria; Using deep learning models and rule engines, the operation data after cleaning is quickly analyzed and compared with the operation specifications, and real-time detection and feedback are provided; The completion status of each task is obtained according to the learning report, and an evaluation report is generated. The evaluation report includes the task success rate, number of errors, task efficiency and real-time feedback.

[0013] Preferably, the completion status of each task is obtained according to the learning report, and the specific content of the generated evaluation report includes professional ability evaluation and management ability evaluation: The professional capability assessment includes construction process assessment, construction operation assessment, hidden engineering assessment, independent task assessment, emergency task assessment, knowledge reserve assessment, and practical application assessment; The specific contents of the management capability assessment include construction planning and organizational capability assessment, resource management and utilization, team collaboration and communication, risk management and emergency response, construction progress control, construction quality management, decision-making and problem solving, and multi-task management capabilities.

[0014] Preferably, the specific evaluation method during the evaluation process is: The assessment is divided into items, with preset scoring criteria. The mastery of different station construction tasks is scored separately to obtain separate scores and form a detailed scoring report so that students can clearly understand which areas need improvement.

[0015] The grade assessment is based on the accumulation of individual scores to obtain a comprehensive score, which is then divided into grades including "excellent", "good", "qualified", and "needs improvement", to help students understand their overall level of professional ability.

[0016] In summary, compared with traditional technologies, the rail transit engineering simulation training system based on virtual reality and artificial intelligence of the present invention truly restores the station construction process. Through virtual reality technology, students can experience the real construction process of subway stations and master the core skills of each stage.

[0017] In addition, through virtual simulation tasks, the system can collect a large amount of student operation data. Teachers can optimize teaching based on the data and provide students with personalized learning guidance.

[0018] In addition, the virtual simulation system enables students to repeatedly practice construction operations in a safe environment, reducing safety issues that may arise in actual construction.

[0019] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a system module diagram of a rail transit engineering simulation training system based on virtual reality and artificial intelligence in the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0022] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0023] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0024] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0025] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0026] like Figure 1 As shown, the present invention provides a rail transit engineering simulation training system based on virtual reality and artificial intelligence. The present invention focuses on restoring the construction process of subway stations, provides students with an immersive learning experience through virtual reality technology, combines artificial intelligence with data analysis, and provides students with real-time feedback and personalized learning suggestions, helping students accumulate operational experience in station construction and providing valuable data support for the teaching process.

[0027] Virtual reality scene module: the virtual reality scene module constructs a virtual scene of the entire process of a subway station from planning to completion; By using virtual reality (VR) technology to build a highly simulated rail transit engineering environment, trainees can perform real operations in a 3D virtual space, simulating complex tasks such as train dispatching, track maintenance, and train operation. This highly immersive experience can enhance trainees' operational capabilities, realistically simulating real-life scenarios in a simulated environment, and enabling trainees to adapt to the real working environment more quickly. During the operation, trainees can interact as if they were in a real scene, and through tactile feedback devices (such as handles, gloves, etc.), the sense of reality during operation is enhanced, improving learning efficiency and memory effects.

[0028] Furthermore, the method for constructing the virtual reality scene module specifically includes constructing an outdoor venue virtual unit, constructing an indoor structure virtual unit and constructing an effect enhancement unit, and the outdoor venue virtual unit and the indoor structure virtual unit are fused and corrected to obtain the virtual reality scene module.

[0029] Furthermore, the specific contents of the construction of the outdoor venue virtual unit are as follows: Use VR immersive virtual reality simulation technology to simulate outdoor venues; The GPS module carried by a high-precision drone is used to photograph the construction red line range around the subway station and record the precise location of each measuring point to obtain the initial image; The initial images are stitched together to generate an orthophoto terrain model, and the orthophoto terrain model is denoised using 3ds Max or Blender; Import the orthophoto terrain model into the GIS platform, integrate the orthophoto terrain model with the site's geographic information data, and use the coordinate calibration tool to unify the coordinate system of the 3D model and GIS data to achieve precise spatial alignment; Furthermore, the contents of the construction of the indoor structure virtual unit are: Obtain design drawings and use Auto Desk Revit 3D information simulation software to build an internal 3D virtual model based on the design drawings; Write an automated plug-in in Python to lightweight the internal 3D virtual model and export it to FBX format; The content of the effect enhancement unit is as follows: Real-time rendering technology is used to increase lighting and weather effects, a particle system is used to simulate dynamic elements in the environment, and OpenAL three-dimensional audio technology is used to integrate the collected sound effects into the virtual scene.

[0030] Real-time rendering technology is used to add rich lighting and weather effects based on a complete subway station model. By studying and simulating the changing laws of natural lighting in different time periods, the lighting effects in the virtual scene are made more realistic and natural, while simulating a variety of weather conditions to enhance the visual realism of the scene.

[0031] A well-configured day and night cycle and weather system allows users to experience scene changes at different times and weather conditions in virtual scenes. This system needs to work with light and shadow effects and dynamic element simulation to achieve a more realistic scene effect.

[0032] Use particle systems to simulate dynamic elements in the environment, such as wind, dust, vehicles and pedestrian flows, etc. Through precise physical models and parameter settings, add vivid dynamic effects to virtual scenes, making the scenes more vivid and lively.

[0033] Environmental sounds are collected on-site around the station, such as vehicles driving, people talking, and natural wind sounds. The collected sound effects are integrated into the virtual scene using OpenAL 3D audio technology. Through precise spatial positioning and sound propagation algorithms, spatialized sound effects are achieved, so that the sound changes naturally with the user's position, further enhancing the user's immersion in the virtual scene.

[0034] Introduce a physics engine (such as PhysX in Unity or Chaos in Unreal) to set detailed physical properties for each element of the orthophoto terrain model (including buildings, roads, trees, etc.), such as rigidity properties. By simulating collision detection and terrain response, users can interact with the site environment in a natural and realistic way in the virtual scene, achieving effects such as collision and terrain feedback, greatly increasing the authenticity and fun of the scene.

[0035] Task simulation module: The task simulation module is used to design station construction tasks. The task simulation module also includes an operation guidance unit and a tool use training unit.

[0036] Furthermore, the specific contents of the task simulation module are as follows: Build a Transformer model, input the construction organization of subway station construction into the Transformer model, and obtain the knowledge graph of subway station construction; Convert nodes in the knowledge graph into low-dimensional vector representations through techniques such as word embedding or graph embedding; Use the Word2Vec word embedding method to vectorize the names and descriptions of construction processes, or use the GraphEmbedding algorithm to embed the nodes of the entire knowledge graph; A convolutional neural network variant algorithm is used to extract and learn features from the knowledge graph, and then the construction task processes of subway stations are trained.

[0037] Based on the knowledge graph, select the construction process chain with a clear sequence and simple interdependence. For example, select the continuous and logically clear processes from site cleaning, surveying and laying out to foundation cushion pouring. The tasks composed of these processes have fewer steps in actual operation, which are easy for students to understand and complete, so that students can become familiar with the basic construction process and operating specifications.

[0038] Present it to students in the form of a virtual scene. In the virtual reality environment, clearly mark the construction equipment, materials and operation areas involved in each construction process. Provide students with detailed operation guides, including the specific steps, operation methods and precautions of each process. At the same time, set up a simple prompt system so that students can get corresponding help information when they encounter difficulties during the operation. In this way, students can complete simple tasks in a relatively relaxed environment, enhance their self-confidence and initial mastery of the construction process.

[0039] Tasks of medium difficulty require increasing the complexity and diversity of construction processes. Select multiple interrelated branches of construction processes from the knowledge graph and introduce some simple conditional constraints and resource allocation problems. For example, in the construction of the main structure of the station, processes such as concrete pouring, formwork construction and steel bar binding are involved at the same time, and the supply time limit of concrete, the selection of different types of formwork, and the matching of steel bar specifications and quantity must be considered. Such tasks require students to not only master the operation of a single process, but also learn to coordinate the relationship between multiple processes and complete construction tasks under certain resource constraints.

[0040] The tasks are also published in a virtual scene, but the details and complexity of the scene are higher than those of simple tasks. In addition to providing basic operation instructions, the details of the prompt information are reduced to encourage students to think and solve problems independently. Some dynamic factors are set in the tasks, such as temporary changes in construction conditions (such as the impact of weather changes on concrete pouring), and students are required to adjust the construction plan and process arrangement according to the knowledge they have learned and the actual situation. At the same time, after the task is completed, a detailed evaluation report is provided to point out the advantages and disadvantages of students in the process of task execution to help them improve.

[0041] High-difficulty tasks should simulate real and complex construction scenarios, involving a large number of construction processes, complex resource allocation, strict safety and quality requirements, and unforeseen emergencies. Integrate the processes of multiple construction stages from the knowledge graph, including the entire process of preliminary preparation, main construction, equipment installation and commissioning. Introduce complex allocation problems of multiple resource types (manpower, materials, equipment), such as collaborative arrangements for workers of different types of work, supply plans and inventory management of multiple materials, and scheduling of large-scale construction equipment. At the same time, set up complex safety hazards and quality control nodes, as well as possible emergencies (such as unknown underground obstacles, construction equipment failures, etc.), requiring students to comprehensively consider and formulate response strategies.

[0042] Issue tasks in a highly realistic virtual scene that should restore the real subway station construction environment as much as possible, including complex geological conditions, surrounding environmental impacts, etc. Provide limited initial information and require students to collect and analyze more data on their own to develop construction plans. Reduce or even do not provide operation prompts, allowing students to rely entirely on their own knowledge and experience to complete the task. During the execution of the task, monitor the students' operations in real time, and dynamically adjust various factors in the scene according to their decisions and actions, such as the time and severity of emergencies. After the task is completed, conduct a comprehensive and in-depth evaluation, including the rationality of the construction plan, the accuracy of process execution, the efficiency of resource allocation, the effectiveness of safety and quality control measures, and other dimensions, and provide students with detailed feedback and improvement suggestions. At the same time, set up communication and discussion sessions between students to promote knowledge sharing and experience accumulation.

[0043] Data collection and analysis module: The data collection and analysis module is used to collect student operation data and analyze the student operation data to obtain an analysis report; By integrating artificial intelligence (AI) algorithms, the system can monitor students' operating behaviors in real time, analyze students' operating data, identify problems in operation, and provide immediate feedback and personalized guidance. Traditional training systems cannot provide personalized coaching, but this patented system can provide customized training suggestions based on students' operating performance through AI technology, making up for students' operational weaknesses. This intelligent feedback mechanism significantly improves students' learning outcomes, helps students master skills faster, and reduces operational errors. Intelligent feedback can also dynamically adjust the difficulty of training tasks according to students' learning progress.

[0044] Furthermore, the specific contents of the data collection and analysis module include: The data acquisition and analysis module is equipped with a sensor; Recording the operation data of students in the virtual environment, wherein the operation data includes real-time data and result data, wherein the real-time data includes hand movements and body postures collected by sensors, and the result data includes task operation time, task accuracy, and task completion percentage; Identify missing values, outliers, duplicate values, and erroneous values ​​in operational data and clean them; The cleaning process includes counting missing values ​​and erroneous values, confirming the knowledge graph location of the missing values ​​and marking them, eliminating abnormal values ​​and duplicate values, and generating an elimination table.

[0045] Feedback and evaluation module: The feedback and evaluation are used to provide real-time feedback based on the analysis report and generate an operation progress report.

[0046] Furthermore, the specific contents of the feedback and evaluation module include: The feedback and evaluation module is preset with operational specification criteria; Using deep learning models and rule engines, the operation data after cleaning is quickly analyzed and compared with the operation specifications, and real-time detection and feedback are provided; The completion status of each task is obtained according to the learning report, and an evaluation report is generated. The evaluation report includes the task success rate, number of errors, task efficiency and real-time feedback.

[0047] Furthermore, the completion status of each task is obtained according to the learning report, and the specific content of the generated evaluation report includes professional ability evaluation and management ability evaluation: The professional capability assessment includes construction process assessment, construction operation assessment, hidden engineering assessment, independent task assessment, emergency task assessment, knowledge reserve assessment and practical application assessment; Construction process assessment specifically refers to the understanding of construction steps and process coherence assessment. The understanding of construction steps assesses whether students can clearly describe the specific steps of each process of subway station construction, such as foundation excavation, support structure installation, equipment commissioning, etc. Assessment is conducted through question-and-answer assessment or online test. Process coherence assessment is to assess whether students understand the sequence and logical relationship between each construction step and whether they can reasonably connect each construction link. Scoring is based on the completion of multi-step tasks in the simulation scenario.

[0048] Construction operation assessment specifically refers to the mastery of construction key points and operation specifications and skill levels. The mastery of construction key points assesses whether students are proficient in the key points of each construction stage, such as quality control of foundation construction, stability of structural connection points, etc. The system confirms whether students can identify and execute these key points through task checks.

[0049] Operational standards and skill levels refer to students' performance in operations, including whether construction tools are used correctly and whether the operations comply with construction standards, especially the assessment of core operational skills such as concrete pouring and steel bar binding.

[0050] The hidden project assessment is divided into the understanding of hidden projects and the ability to independently complete hidden tasks. To understand hidden projects, students need to understand what belongs to hidden projects, such as foundation pipelines, structural connection nodes, etc. Through task simulation, students are assessed on whether they can correctly identify hidden projects and complete the acceptance. When assessing students' acceptance of hidden projects, whether they strictly follow the current construction specifications, the system will record the students' operating steps in the acceptance task and conduct comparative analysis.

[0051] The ability to independently complete hidden tasks includes independent operation completion: assessing whether students can independently complete specific construction tasks under the guidance of the system, such as wall pouring or equipment installation. The scoring criteria include whether the correct steps are followed and whether the correct construction methods are used. The quality of hidden task results: assessing the results after students complete the task, such as the quality of welding, the accuracy of construction, the stability of equipment installation, etc. The system will conduct virtual inspections on the completed tasks and give scores.

[0052] Emergency Task Assessment: Problem identification and analysis: During the virtual construction process, the system will simulate some unexpected situations, such as insufficient materials, equipment failure, etc. Students need to quickly identify the problem and take measures. Evaluate whether students can use their professional knowledge to quickly analyze the root cause of the problem.

[0053] Solution execution: evaluates whether students can choose and execute the correct solution when dealing with emergencies. The system will give a comprehensive score based on their response speed, scientific nature of the solution, and effectiveness of execution.

[0054] Knowledge reserve assessment: Recognition and selection of materials: Students need to correctly select construction materials in tasks, such as choosing appropriate concrete mix ratios, steel bar types, etc. The system evaluates students' mastery of engineering materials by simulating the material selection process.

[0055] Equipment operation and maintenance: Assess whether students can correctly use construction equipment (such as mixers, welding equipment, etc.) and understand the maintenance requirements of the equipment to ensure construction safety and good operating condition of the equipment.

[0056] Practical application evaluation Mastery of theoretical knowledge: Through the system's built-in theoretical test module, the system assesses students' mastery of theoretical knowledge related to subway station construction, including basic knowledge such as structural mechanics and material properties.

[0057] Combination of theory and practice: When completing actual construction tasks, students are assessed on whether they can flexibly apply theoretical knowledge to actual operations. For example, whether they can adjust the construction design according to load requirements, or adjust the excavation depth according to different soil conditions.

[0058] The specific contents of the management capability assessment include construction planning and organizational capability assessment, resource management and utilization, team collaboration and communication, risk management and emergency response, construction progress control, construction quality management, decision-making and problem solving, and multi-task management capabilities.

[0059] Furthermore, the specific evaluation methods during the evaluation process are as follows: The assessment is divided into items, with preset scoring criteria. The mastery of different station construction tasks is scored separately to obtain separate scores and form a detailed scoring report so that students can clearly understand which areas need improvement.

[0060] The grade assessment is based on the accumulation of individual scores to obtain a comprehensive score, which is then divided into grades including "excellent", "good", "qualified", and "needs improvement", to help students understand their overall level of professional ability.

[0061] Through the combination of virtual reality and AI technology, trainees can conduct simulated training of high-risk operations in a virtual environment without having to bear high costs or risks in a real environment. Traditional rail transit engineering training often needs to be carried out on real tracks, which is costly and has certain safety hazards. The present invention provides a safe and low-cost training platform that can reduce training risks and save a lot of resources. Trainees can repeatedly practice operations in a virtual environment, master complex technical operations, and ensure that they can perform tasks efficiently and safely in a real working environment.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A rail transit engineering simulation training system based on virtual reality and artificial intelligence, characterized in that: include: Virtual reality scene module: the virtual reality scene module constructs a virtual scene of the entire process of a subway station from planning to completion, and the virtual reality scene module includes an outdoor site virtual unit and an indoor structure virtual unit; Task simulation module: the task simulation module is used to design station construction tasks, and the task simulation module also includes an operation guidance unit and a tool use training unit; Data collection and analysis module: The data collection and analysis module is used to collect student operation data and analyze the student operation data to obtain an analysis report; Feedback and evaluation module: The feedback and evaluation are used to provide real-time feedback based on the analysis report and generate an operation progress report.

2. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 1, characterized in that: The method for constructing a virtual reality scene module specifically includes constructing an outdoor venue virtual unit, constructing an indoor structure virtual unit and constructing an effect enhancement unit. The outdoor venue virtual unit and the indoor structure virtual unit are fused and corrected to obtain the virtual reality scene module.

3. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 2, characterized in that: The specific contents of the construction of the outdoor venue virtual unit are as follows: Using VR immersive virtual reality simulation technology, for outdoor venue simulation The GPS module carried by a high-precision drone is used to photograph the construction red line range around the subway station and record the precise location of each measuring point to obtain the initial image; The initial images are stitched together to generate an orthophoto terrain model, and the orthophoto terrain model is denoised using 3ds Max or Blender; Import the orthophoto terrain model into the GIS platform, integrate the orthophoto terrain model with the site's geographic information data, and use the coordinate calibration tool to unify the coordinate system of the 3D model and GIS data to achieve precise spatial alignment.

4. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 3, characterized in that: The contents of the construction of the indoor structure virtual unit are: Obtain design drawings and use Auto Desk Revit 3D information simulation software to build an internal 3D virtual model based on the design drawings; Write an automated plug-in in Python to lightweight the internal 3D virtual model and export it to FBX format; The content of the effect enhancement unit is as follows: Real-time rendering technology is used to increase lighting and weather effects, a particle system is used to simulate dynamic elements in the environment, and OpenAL three-dimensional audio technology is used to integrate the collected sound effects into the virtual scene.

5. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 4, characterized in that: The specific contents of the task simulation module are: Build a Transformer model, input the construction organization of subway station construction into the Transformer model, and obtain the knowledge graph of subway station construction; Convert nodes in the knowledge graph into low-dimensional vector representations through techniques such as word embedding or graph embedding; Use the Word2Vec word embedding method to vectorize the names and descriptions of construction processes, or use the GraphEmbedding algorithm to embed the nodes of the entire knowledge graph; A convolutional neural network variant algorithm is used to extract and learn features from the knowledge graph, and then the construction task processes of subway stations are trained.

6. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 5, characterized in that: The specific contents of the data collection and analysis module include: The data acquisition and analysis module is equipped with a sensor; Recording the operation data of students in the virtual environment, wherein the operation data includes real-time data and result data, wherein the real-time data includes hand movements and body postures collected by sensors, and the result data includes task operation time, task accuracy, and task completion percentage; Identify missing values, outliers, duplicate values, and erroneous values ​​in operational data and clean them; The cleaning process includes counting missing values ​​and erroneous values, confirming the knowledge graph location of the missing values ​​and marking them, eliminating abnormal values ​​and duplicate values, and generating an elimination table.

7. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 6, characterized in that: The specific contents of the feedback and evaluation module include: The feedback and evaluation module is preset with operational specification criteria; Using deep learning models and rule engines, the operation data after cleaning is quickly analyzed and compared with the operation specifications, and real-time detection and feedback are provided; The completion status of each task is obtained according to the learning report, and an evaluation report is generated. The evaluation report includes the task success rate, number of errors, task efficiency and real-time feedback.

8. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 7, characterized in that: Based on the learning report, the completion status of each task is obtained, and the specific content of the evaluation report generated includes professional ability evaluation and management ability evaluation: The professional capability assessment includes construction process assessment, construction operation assessment, hidden engineering assessment, independent task assessment, emergency task assessment, knowledge reserve assessment, and practical application assessment; The specific contents of the management capability assessment include construction planning and organizational capability assessment, resource management and utilization, team collaboration and communication, risk management and emergency response, construction progress control, construction quality management, decision-making and problem solving, and multi-task management capabilities.

9. A rail transit engineering simulation training system based on virtual reality and artificial intelligence according to claim 8, characterized in that: The specific evaluation methods during the evaluation process are as follows: The assessment is divided into items, with preset scoring criteria. The mastery of different station construction tasks is scored separately to obtain separate scores and form a detailed scoring report so that students can clearly understand which areas need improvement.

10. Grade assessment: a comprehensive score is obtained by adding up the individual scores, and the comprehensive score is divided into grades, including "excellent", "good", "qualified", and "needs improvement", to help students understand their overall level of professional ability.

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