Safety tool comprehensive training operation system based on VR technology

Abstract

The invention discloses a safety tool comprehensive training operation system based on a VR technology, and the system can simulate a plurality of real accident situations, and allows students to observe and experience the whole process of sudden accidents in a virtual environment. Through the immersive accident simulation, students can deeply understand dangers possibly caused by wrong operations or nonstandard behaviors, so that the safety awareness of the students is enhanced, and the sensitivity to potential risks is formed. And in the virtual scene, the trainee can repeatedly perform high-risk operation training without worrying about equipment damage or personnel safety problems. The repeatable operation training enables students to be more confident when mastering high-risk operation, and ensures that the students can easily deal with various complex conditions in a real working environment.

Description

Technical Field

[0001] The present invention belongs to the field of comprehensive training of safety tools. Specifically, it relates to a comprehensive training operation system for safety tools based on VR technology. Background Art

[0002] Currently, the training of safety tools mainly relies on theoretical learning and on-site follow-up. In theoretical learning, most trainees obtain safety knowledge through textbooks, teaching materials, or explanatory videos. However, this method lacks the interactive experience of actual operation, and it is difficult for trainees to combine theoretical knowledge with actual operation. In on-site follow-up learning, although trainees can observe the actual operations of others, due to the complexity and risk of the operation site, trainees cannot directly use the equipment for operation training for a long time, resulting in a lack of practical experience. Especially in high-risk environments, trainees usually can only conduct limited observations and cannot operate the equipment themselves, thus lacking an in-depth understanding of the equipment.

[0003] In addition, for safety reasons, accident scenarios cannot be deliberately set up in training for trainees to experience and respond to in person. This not only prevents trainees from truly feeling the potential dangers in operation but also deprives them of the opportunity to react quickly and handle problems in emergencies. Since the safety awareness cannot be strengthened by simulating real dangerous situations, trainees are difficult to be sufficiently alert and capable of coping with possible future accidents.

[0004] These limitations make it difficult for current training to achieve a true safety warning effect. Trainees often still lack sufficient psychological preparation and operation skills when facing the actual working environment. Ultimately, the limitations of this training method not only affect the safety awareness of trainees but also greatly reduce the training effect, resulting in trainees easily ignoring potential risks in actual work, thus increasing the likelihood of accidents.

[0005] In view of this, the present invention is specifically proposed. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a comprehensive training operation system for safety tools based on VR technology, solving the problems raised in the above background art.

[0007] To solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0008] A comprehensive training operation system for safety tools based on VR technology, comprising: a virtual scene construction module for constructing a virtual environment and safety tool models based on the usage scenarios of safety tools;

[0009] A virtual scenario simulation module for conducting simulations in the constructed virtual scenarios and, based on different safety requirements in each virtual scenario, combining relevant safety tools for training;

[0010] An interactive teaching module for enabling users to perform role-playing and operating practice of safety tools in a virtual scenario through a simulation device;

[0011] A real-time feedback module for monitoring and recording the user's behavior characteristics in real time according to the user's operation behavior in the virtual scenario, providing instant feedback, and correcting and guiding the user's incorrect operations;

[0012] An evaluation module for generating a personalized evaluation report based on the user's operation records and behavior characteristics, pointing out the strengths and weaknesses in the operations, and providing corresponding improvement suggestions according to the report content.

[0013] Optionally, when constructing a virtual environment based on the usage scenarios of safety tools, first, collect and analyze the actual data of the target scenario, including building drawings, equipment information, and scenario parameters. Then, use 3DMAX to perform 3D modeling on each scenario. Next, add corresponding materials, textures, and lighting effects to the elements in the virtual scenario. Finally, add safety-related dynamic elements to the scenario;

[0014] When constructing the safety tool models, first, determine the types of safety tools to be simulated. Then, use 3DMAX to create 3D models for each tool. Next, according to the functional characteristics of each tool, add corresponding physical characteristics and interaction behaviors;

[0015] Finally, integrate the 3D models of the virtual environment and safety tools into the virtual scenario and debug the interaction function between the virtual environment and the tools.

[0016] Optionally, when conducting simulations in the constructed virtual scenarios and, based on different safety requirements in each virtual scenario, combining relevant safety tools for training, first, load the corresponding virtual scenario according to the task requirements. Then, set corresponding safety operation requirements according to the specific risks in the scenario. Subsequently, the user obtains the required safety tools in the scenario according to the task requirements. After obtaining the tools, the user starts to perform operations according to the guidance of the virtual task, simulating real risk situations during the operation to test the user's response ability in emergency situations.

[0017] Optionally, the steps for enabling users to perform role-playing and operating practice of safety tools in a virtual scenario through a simulation device are as follows:

[0018] Provide users with the option to choose different roles in a virtual scenario. Each role corresponds to different job responsibilities and operation requirements. The user performs corresponding tasks in the virtual scenario according to the selected role. After entering the scenario, the user obtains and checks the required safety tools according to the role tasks;

[0019] Subsequently, a corresponding task scenario is automatically generated according to the role selected by the user. The user participates in the task scenario through the first-person perspective. After entering the specific task scenario, the system provides task guidance to clarify the current operation objectives and operation processes. Moreover, during the task execution process, the system monitors the user's operation behaviors in real time.

[0020] Optionally, according to the user's operation behaviors in the virtual scenario, the steps of real-time monitoring and recording the user's behavior characteristics, providing instant feedback, and correcting and guiding the user's incorrect operations are as follows:

[0021] Collect the user's operation data in the virtual scenario in real time through virtual reality devices. Then, use machine learning algorithms to analyze the collected data and extract the user's key behavior characteristics and compare it with the preset standard operation process S to automatically detect whether the user has operation behaviors that do not meet safety requirements. Its expression is: where, represents the characteristic value of the th step in the standard operation, is a function used to detect the user's deviation from the standard operation. When , it returns 1, otherwise it returns 0;

[0022] When an incorrect operation is detected, instant feedback is provided to the user through a feedback generation algorithm. The feedback forms include visual cues, sound alarms, or tactile feedback. Next, based on the feedback generation algorithm, the system generates specific correction instructions to guide the user to adjust according to the correct operation steps to ensure that their operations meet safety standards.

[0023] After the user adjusts their operations according to the instructions, use the machine learning algorithm again to evaluate the user's behavior characteristics to confirm whether their operations fully meet safety requirements. Meanwhile, continuously record the user's operation data.

[0024] Optionally, when using machine learning algorithms, first, collect data on the standard operation process, specifically including the correct usage methods of tools, operation sequences, working postures, and safety distances. Then, extract the trajectory data of hand and body movements, the usage methods of tools, the time and sequence of operations, and the relative positions in the working environment from the user's operation data. Next, use these extracted feature data to train a machine learning model and find the optimal classification boundary through a support vector machine to identify the patterns of correct and incorrect operations. Its expression is: , where is the weight vector, indicating the importance of each feature, is the bias value, defining the boundary of classification, the extracted feature vector;

[0025] When the user performs an operation, the action data of the user is collected in real time through sensors, and the machine learning algorithm processes these data to extract the real-time operation features of the user. Then, these real-time feature data are compared with the trained model to determine whether the current operation conforms to the predefined standard operation process. If the user's operation deviates from the standard operation specification, this deviation will be automatically identified and marked as an incorrect operation.

[0026] Optionally, when using the feedback generation algorithm, first, the system presets three feedback forms including visual, auditory, and tactile. Then, the system classifies the errors into different levels according to the severity of the incorrect operation. When receiving the user's incorrect operation, the error is first classified, and the feedback form matching the error level is selected. The system dynamically adjusts the feedback intensity according to the number of times and severity of the user's repeated errors, and its expression is , where represents the error at time the feedback intensity of, represents the initial feedback intensity, represents the adjustment coefficient of the feedback intensity, represents the user at time within the number of times of repeating the

[0027] Subsequently, based on the feedback generation algorithm, step-by-step guidance or a complete operation demonstration is provided to help the user correct the error and restore to the standard operation process. If the user continuously makes the same incorrect operation, the virtual assistant or animation demonstration is automatically triggered to provide the user with visual and voice guidance on the correct operation.

[0028] Optionally, the steps to generate a personalized evaluation report based on the user's operation records and behavior characteristics, point out the strengths and weaknesses in the operation, and provide corresponding improvement suggestions according to the report content are as follows:

[0029] First, the operation data of the user is collected and recorded in real time through the virtual reality device. The data includes the user's hand movements, the use of tools, the operation sequence, the operation time, and the errors occurring during the task execution. Then, the system extracts the key behavior characteristics from the collected operation data. These behavior characteristics include operation accuracy, task completion time, the accuracy of tool use, and the working posture.

[0030] Subsequently, the system analyzes the user's operation records, identifies the strengths and weaknesses in the user's operations, and generates a personalized evaluation report based on these analyses. The content of the report includes the user's operation advantages, weak links, task completion rate, and error rate. According to the deficiencies in the evaluation report, improvement suggestions are provided, and the suggestions include specific operation improvement plans and training tasks for the user's weak links.

[0031] Finally, a personalized training plan is designed according to the user's weak links, and the user is recommended to carry out targeted operation exercises.

[0032] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0033] This system can simulate a variety of real accident scenarios, allowing trainees to observe and experience the whole process of sudden accidents in a virtual environment. Through this immersive accident simulation, trainees can deeply understand the dangers that may be brought by incorrect operations or non-standard behaviors, thereby strengthening their safety awareness and forming sensitivity to potential risks. Moreover, in the virtual scenario, trainees can repeatedly conduct high-risk operation training without worrying about equipment damage or personnel safety issues. This repeatable operation training makes trainees more confident in mastering high-risk operations and also ensures that they can calmly handle various complex situations in the real working environment.

[0034] The following further describes in detail the specific implementation manners of the present invention with reference to the accompanying drawings. Description of the Drawings

[0035] The accompanying drawings in the following description are only some embodiments. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0036] Figure 1 It is a block diagram of the comprehensive training operation system.

[0037] It should be noted that these drawings and text descriptions are not intended to limit the scope of the concept of the present invention in any way, but to illustrate the concept of the present invention to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0038] Now, the present invention will be further described in detail with reference to the accompanying drawings.

[0039] Please refer to Figure 1As shown in the figure, in this embodiment, a comprehensive training operation system for safety tools based on VR technology is provided, including a virtual scene construction module for constructing a virtual environment and safety tool models based on the usage scenarios of safety tools; the virtual environment construction includes scenarios such as high-voltage substations, high-rise building exteriors, toxic gas workshops, and machining production lines; the safety tool construction includes electrical insulation safety tools, safety protection tools, high-altitude operation tools, safety sign tools, lifting tools, etc. For the construction of the safety tools mentioned above, further, the electrical insulation tools include high-voltage voltage detectors, insulating caps, insulating gloves, insulating boots, insulating pliers, etc., which provide necessary electrical insulation protection during operations on live equipment or potentially live equipment; the safety protection tools include safety helmets, protective glasses, gas masks, protective clothing, anti-static clothing, etc., which are devices used to protect the head, eyes, respiratory system, and body parts from external injuries; the high-altitude operation tools include safety belts, safety self-locking devices, various types of ladders, etc., which are devices to prevent falls from heights; the lifting tools include wire ropes, hooks, pulleys, etc., which are devices to ensure the safe handling of heavy objects;

[0040] It should be noted that: high-precision 3D modeling is carried out on various scenarios using 3DMAX software. By collecting architectural drawings, parameters, and equipment information of scenarios such as high-voltage substations, high-rise building exteriors, toxic gas workshops, and machining production lines, the actual data sizes of the scenarios and equipment are obtained and then model building is carried out to ensure that the operation scenarios, equipment appearance, and functions are consistent with the real objects.

[0041] Furthermore, in this system, in order to achieve high-precision and high-fidelity virtual modeling, the model construction of different scenarios and tools is carried out in steps:

[0042] When constructing the high-voltage substation model, first, according to the plan view and elevation view of the substation, basic structures such as fences, gates, and main control rooms are built using geometric bodies (such as cuboids and cylinders). Subsequently, equipment models such as transformers and switch cabinets are imported and precisely arranged according to the design drawings. In order to enhance the detailed performance of the model, Boolean operations or spline tools are used to process the complex connection parts between the equipment, and detailed elements such as pipes and cables are added to the model. Finally, by adjusting the materials and textures, the visual effect and authenticity of the model are improved.

[0043] When modeling the high-rise building exterior wall, first, use AEC extension tools to quickly build the wall model, or simulate the wall structure by manually creating geometric bodies. After adjusting the size, shape, and position of the wall according to the design drawings, detailed elements such as windows, balconies, and decorative lines are added to make the exterior wall model more realistic. Appropriate materials and textures are assigned to the model to further enhance its visual effect.

[0044] When modeling a toxic gas workshop, first, create the basic layout of the workshop according to the floor plan and process flow diagram of the workshop, including the equipment area, passageways, safety exits, etc. Then, import or create models of toxic gas treatment equipment, ventilation systems, and alarm devices, paying special attention to the division of safety areas and the addition of warning signs. To enhance the dynamic effect of the scene, use a particle system to simulate the diffusion process of toxic gases (this link may involve post-rendering and special effects production).

[0045] When modeling a machining production line, first, create the basic layout of the production line according to the process flow diagram of the production line, such as workbenches, conveyor belts, and robots. Subsequently, model each piece of equipment in detail (such as machine tools, cutting machines, welding machines), and use Boolean operations or spline tools to handle the connections between equipment. Simulate the dynamic operation of the equipment through keyframe animation or path animation, such as the movement of the conveyor belt or the execution of operations by the robot.

[0046] When modeling electrical insulation safety tools, first, use geometric tools (such as cuboids, cylinders) to create the basic shape of the tools. According to the actual structure of the tools, add details such as handles, insulation layers, and connecting components, and assign materials and textures (such as rubber, plastic, or insulating materials) to ensure the realism of the models.

[0047] When modeling safety protection tools, first, model the basic structures such as protective masks, protective clothing, and safety shoes. Then, according to the functional requirements of the tools, add components such as reflective strips, ventilation holes, and protective nets. Ensure that the models conform to ergonomic design to improve the comfort and operational convenience of use.

[0048] When modeling high-altitude operation tools, first, create the basic support structures of high-altitude tools such as scaffolding, ladders, and lifting platforms, and add key components such as locking devices, anti-slip pedals, and safety ropes. To enhance the dynamic effect of training, use animation tools to simulate the use process of the tools (such as the unfolding and folding of ladders).

[0049] When modeling safety sign tools, first, use 2D graphic tools to draw the basic shapes of safety signs (such as circles, triangles). Add safety warning words, symbols, or patterns to the signs, and assign eye-catching colors and materials. To enhance visibility, light sources can be added to the scene to highlight the signs.

[0050] When modeling lifting tools, first, construct the basic structures of cranes, hoists, etc., including boom arms, hooks, counterweights, etc. Subsequently, simulate the working state of the transmission system (such as the winding and release of wire ropes). Finally, add load models (such as goods, containers) to test the performance and stability of the lifting tools in different scenarios.

[0051] The virtual scenario simulation module is used to conduct simulations in the constructed virtual scenarios and, based on different safety requirements in each virtual scenario, combine relevant safety tools for training; the virtual scenarios include high-voltage line inspection, chemical warehouse operation, high-altitude line maintenance, and construction site safety warning.

[0052] Furthermore, in the high-voltage line inspection simulation, design the operation steps of using an insulating rod for voltage testing.

[0053] Import the scene model of high-voltage line inspection in 3DMAX, including transmission lines, towers, substation and other ancillary facilities, as well as houses, trees, etc. that cross and span. Set appropriate lighting and materials to truly reflect the on-site environment.

[0054] Import the safety tools for high-voltage line inspection in 3DMAX, including insulating rods, voltage detectors, grounding wires, insulating gloves, identification plates, etc. Pay attention to the detail processing of the models, such as the material differentiation of the insulating part and the metal part of the insulating rod, and the specific structure of the voltage detector and the grounding wire.

[0055] Import or create a 3D model of the operator and set up a skeletal animation system for it to simulate real human movements.

[0056] In the inspection scenario, place the insulating rod and the voltage detector in the hands or near the operator, ensuring that the position is reasonable and easy to access. Use the constraint tools or animation system of 3DMAX to simulate the actions of the operator picking up the insulating rod and the voltage detector.

[0057] Design the action sequence of the operator using the insulating rod to operate the voltage detector, including precautions such as wearing insulating gloves and keeping a safe distance from the high-voltage line, and steps such as using the three-step voltage testing method to conduct voltage testing according to the regulations. Pay attention to simulating the safe distance and action specifications during the voltage testing process to ensure compliance with the power industry standards.

[0058] Through the changes in materials and lighting, and possible special effects production (such as electric spark effects), intuitively display the results of the voltage testing process. You can add text descriptions or voice prompts to further explain the voltage testing steps and precautions.

[0059] Furthermore, in the high-voltage line inspection simulation, design the operation steps of connecting and hanging the grounding wire.

[0060] Place the reel or storage location of the grounding wire in the scene to ensure that the operator can easily take out the grounding wire. Design the action sequence of the operator unfolding the grounding wire, including steps such as untying the reel and straightening the grounding wire.

[0061] Simulate the operation of an operator using an insulating rod to assist in fixing one end of the grounding wire to a tower or the grounding grid. Note to follow the principle of "connecting the grounding end first and then the conductor end". Design the action sequence for the operator to bypass the other end of the grounding wire around the high-voltage line and fix it properly to ensure reliable grounding.

[0062] After completing the connection and hanging of the grounding wire, design the actions and expressions of the operator for inspection and confirmation, such as checking the fastening degree of the grounding wire, the safe distance from surrounding objects, etc. Text descriptions or voice prompts can be added to emphasize the importance of grounding and the necessity of inspection and confirmation.

[0063] Use the rendering function of 3DMAX to perform high-quality rendering output on the designed scene to generate realistic image or video files. Adjust the rendering settings such as resolution, lighting effects, material textures, etc. as needed to achieve the best visual effects.

[0064] Combine each action sequence into a complete inspection simulation animation to ensure a smooth process and compliance with actual operation specifications. Add background music and commentary to enhance the expressiveness and educational significance of the animation.

[0065] An interactive teaching module for allowing users to perform role-playing and operation practice of safety tools in a virtual scenario through a simulation device;

[0066] A real-time feedback module that, based on the user's operation behaviors in the virtual scenario, monitors and records the user's behavior characteristics in real time, provides instant feedback, and corrects and guides the user's incorrect operations;

[0067] An evaluation module that generates a personalized evaluation report based on the user's operation records and behavior characteristics, points out the strengths and weaknesses in the operations, and provides corresponding improvement suggestions according to the report content.

[0068] In this embodiment, when constructing a virtual environment based on the usage scenarios of safety tools, first, collect and analyze the actual data of the target scenario, including architectural drawings, equipment information, and scene parameters. Then, use 3DMAX to perform 3D modeling on each scene. Next, add corresponding materials, textures, and lighting effects to the elements in the virtual scene. Finally, add safety-related dynamic elements to the scene;

[0069] When constructing the safety tool models, first, determine the types of safety tools to be simulated. Safety tools include electrical insulation tools, safety protection tools, high-altitude operation tools, safety sign tools, and lifting tools. Electrical insulation tools include high-voltage voltage detectors, insulating gloves, and insulating boots. Then, use 3DMAX to create 3D models for each tool. Next, according to the functional characteristics of each tool, add corresponding physical characteristics and interaction behaviors;

[0070] Finally, integrate the 3D models of the virtual environment and safety tools into the virtual scene and debug the interaction function between the virtual environment and the tools.

[0071] In this embodiment, when simulating in the constructed virtual scene and training in combination with relevant safety tools according to different safety requirements in each virtual scene, first, load the corresponding virtual scene according to the task requirements. Then, set the corresponding safety operation requirements according to the specific risks in the scene. Subsequently, the user obtains the required safety tools in the scene according to the task requirements. After obtaining the tools, the user starts to execute the operation according to the guidance of the virtual task, and simulates the real risk situation during the operation to test the user's response ability in an emergency.

[0072] The steps of allowing the user to perform role-playing and operation practice of safety tools in the virtual scenario through the simulation device in this embodiment are as follows:

[0073] Provide the user with the choice of different roles in the virtual scenario. Each role corresponds to different job responsibilities and operation requirements, and the user executes the corresponding tasks in the virtual scene according to the selected role. After entering the scene, the user obtains and checks the required safety tools according to the role tasks;

[0074] Subsequently, automatically generate the corresponding task scenario according to the role selected by the user. The user participates in the task scenario from the first-person perspective. After entering the specific task scenario, the system provides task guidance for the user, clarifies the current operation objectives and operation processes, and monitors the user's operation behavior in real time during the task execution.

[0075] The steps of monitoring and recording the user's behavior characteristics in real time according to the user's operation behavior in the virtual scene, providing instant feedback, and correcting and guiding the user's incorrect operations in this embodiment are as follows:

[0076] Collect the operation data of the user in the virtual scene in real time through the virtual reality device, including behavioral characteristics such as the user's hand movements, tool usage methods, operation sequences, and operation times. Then, use machine learning algorithms to analyze the collected data and extract the key behavioral characteristics of the user , and compare it with the preset standard operation process S to automatically detect whether the user has operation behaviors that do not meet safety requirements. Its expression is: , where represents the feature value of the th step in the standard operation, is a function used to detect the user's deviation from the standard operation. When , it returns 1, otherwise it returns 0;

[0077] When a wrong operation is detected, feedback is provided to the user immediately through a feedback generation algorithm. The forms of feedback include visual cues, sound alerts, or tactile feedback. Next, based on the feedback generation algorithm, the system generates specific corrective instructions to guide the user to adjust according to the correct operation steps to ensure that their operations comply with safety standards.

[0078] After the user adjusts their operations according to the instructions, the machine learning algorithm is used again to evaluate the user's behavioral characteristics to confirm whether their operations fully meet the safety requirements. At the same time, the operation data of the user is continuously recorded, including the type of error, the corrective situation, and the operation performance.

[0079] In this embodiment, when using the machine learning algorithm, first, data on the standard operation process is collected, specifically including the correct usage of tools, the operation sequence, the working posture, and the safety distance. Next, trajectory data of hand and body movements, the usage of tools, the time and sequence of operations, and the relative positions in the working environment are extracted from the user's operation data. Then, these extracted feature data are used to train the machine learning model, and the optimal classification boundary is found through a support vector machine to identify the patterns of correct and wrong operations. Its expression is: , where is the weight vector, represents the importance of each feature, is the bias value, defining the boundary of classification, the extracted feature vector;

[0080] When the user performs an operation, the action data of the user is collected in real time through sensors. The machine learning algorithm processes these data to extract the real-time operation characteristics of the user. Next, these real-time feature data are compared with the trained model to determine whether the current operation conforms to the predefined standard operation process. If the user's operation deviates from the standard operation specification, this deviation will be automatically identified and marked as a wrong operation. Using the support vector machine (SVM) in the machine learning algorithm to train the extracted feature data can effectively find the optimal classification boundary between correct and wrong operations. This data-driven model training method can improve the accuracy of the system in identifying the user's operation patterns, ensuring that it can accurately judge whether the user's operations meet the safety standards in complex operation scenarios. This model-based analysis method greatly improves the intelligence level of the training system, making the training process more reliable. The support vector machine identifies the correct and wrong operations of the user by finding the optimal classification boundary of the feature data. It can automatically identify operation deviations and make judgments based on the actual operation performance of the user. This method avoids human intervention, and the system can independently learn the patterns of operation specifications, so as to flexibly judge in different operation scenarios and ensure that each operation step conforms to the predefined standard operation process.

[0081] In this embodiment, when using the feedback generation algorithm, first, the system presets three feedback forms including vision, audition, and touch. Then, the system classifies errors into different levels according to the severity of incorrect operations. When receiving the user's incorrect operation, the system first classifies the error and selects a feedback form that matches the error level. The system dynamically adjusts the feedback intensity according to the number of times and severity of the user's repeated errors. The expression is , where represents the error at time the feedback intensity of, represents the initial feedback intensity, represents the adjustment coefficient of the feedback intensity, represents that the user repeats the within the time the number of times of making an error;

[0082] Subsequently, based on the feedback generation algorithm, step-by-step guidance or a complete operation demonstration is provided to help the user correct the error and restore to the standard operation process. If the user continuously makes the same incorrect operation, the virtual assistant or animation demonstration is automatically triggered to provide the user with visual and voice guidance on the correct operation. When the user continuously makes the same incorrect operation, the system can automatically trigger the virtual assistant or animation demonstration to provide the user with visual and voice guidance on the correct operation in a more vivid form. The virtual assistant can not only provide the user with immediate corrective suggestions but also help the user understand the key points in complex tasks by simulating the correct operation steps. This continuous operation guidance can reduce the user's learning blind spots, strengthen the mastery of the correct operation process, and effectively improve the learning efficiency.

[0083] The steps of generating a personalized evaluation report according to the user's operation records and behavior characteristics in this embodiment, pointing out the strengths and weaknesses in the operation, and providing corresponding improvement suggestions according to the report content are as follows:

[0084] First, the operation data of the user is collected and recorded in real time through the virtual reality device. The data includes the user's hand movements, the use of tools, the operation sequence, the operation time, and the errors that occur during the task execution. Then, the system extracts the key behavior characteristics from the collected operation data. These behavior characteristics include operation accuracy, task completion time, the accuracy of tool use, and the operation posture.

[0085] Subsequently, the system analyzes the user's operation records, identifies the strengths and weaknesses in the user's operations, and generates a personalized assessment report based on these analyses. The content of the report includes the user's operation advantages, weak links, task completion rate, and error rate. According to the deficiencies in the assessment report, improvement suggestions are provided, and the suggestions include specific operation improvement plans and training tasks for the user's weak links. Three feedback forms, namely visual, auditory, and tactile, are preset. The combination of different sensory feedbacks can help users more quickly and intuitively detect incorrect operations. Visual feedback can guide users to correct operations through graphics or highlighting, auditory feedback helps users understand the severity of errors through voice prompts or warning sounds, and tactile feedback such as vibration or force feedback directly reminds users of operation mistakes. This multi-dimensional feedback can enhance the user's learning experience and enable them to make adjustments and improvements more effectively under multi-sensory stimuli.

[0086] Finally, a personalized training plan is designed according to the user's weak links, and users are recommended to conduct targeted operation exercises. This system can provide real-time operation feedback and personalized improvement suggestions based on the operation data of each trainee, ensuring that trainees can correct mistakes in a timely manner and improve their operation levels during the training process. Through virtual reality devices and sensors, the system accurately captures the operation details of each trainee, such as key behavior data like hand movements, device usage methods, operation sequences, and time. After analyzing these data, the system can identify the strengths and weaknesses of trainees in their operations and provide targeted feedback in real time to help trainees make adjustments and improvements during the operation process. By classifying the severity of incorrect operations, the errors are divided into different levels and corresponding feedback forms are matched. This mechanism ensures the accuracy of the feedback. Minor errors may only trigger simple visual or auditory prompts, while serious errors may activate multiple feedback forms simultaneously to strengthen the warning effect. This hierarchical feedback mechanism can avoid over-reminding or interference, ensuring that trainees receive appropriate and accurate guidance during the training process, thereby improving the training efficiency.

[0087] Based on the feedback generation algorithm, the system can provide users with detailed step-by-step guidance or complete operation demonstrations to help them correct mistakes in training and restore to the standard operation process. This not only enhances users' understanding of correct operations but also provides a step-by-step optimized learning path, preventing users from feeling confused in complex tasks. Through step-by-step guidance, users can correct mistakes gradually, while complete operation demonstrations help users obtain comprehensive operation guidance when errors persist, ensuring a more efficient learning effect.

[0088] In addition, the system is not limited to single feedback, but can continuously track the operation performance of the trainees and generate a comprehensive personalized training report. This report not only records the operation performance of the trainees each time, but also analyzes in detail the indicators such as the trainees' skill mastery in different scenarios, operation accuracy, and error frequency. Through these data analyses, the system can help the trainees identify recurring errors and weak skill points, and formulate personalized improvement suggestions accordingly. For example, for frequently occurring errors, the system can arrange specific intensive training tasks to enable the trainees to gradually overcome the weak links through repeated operations and practices.

[0089] The present invention is not limited to the above embodiments. Anyone should know that structural changes made under the inspiration of the present invention, as long as they have the same or similar technical solutions as the present invention, fall within the protection scope of the present invention. The technologies, shapes, and structures not described in detail in the present invention are all well-known technologies.

Claims

1. A comprehensive training operating system for safety tools based on VR technology, characterized in that: include: A virtual scene construction module is used to construct a virtual environment and a safety tool model based on the use scenario of the safety tool; Virtual scenario simulation module, used to simulate in constructed virtual scenarios and conduct training based on different safety requirements in each virtual scenario and in combination with relevant safety tools; Interactive teaching module, which allows users to role-play and practice the operation of safety tools in virtual situations through simulation devices; The real-time feedback module monitors and records the user's behavior characteristics in real time according to the user's operation behavior in the virtual scene, provides instant feedback, and corrects and guides the user's incorrect operation; The evaluation module generates a personalized evaluation report based on the user's operation records and behavioral characteristics, points out the strengths and weaknesses in the operation, and provides corresponding improvement suggestions based on the report content.

2. According to claim 1, a VR-based safety tool comprehensive training operating system is characterized in that: When building a virtual environment based on the use scenarios of safety tools, first, collect and analyze the actual data of the target scene, then use 3DMAX to build 3D models for each scene, then add corresponding materials, textures and lighting effects to the elements in the virtual scene, and finally, add dynamic elements related to safety to the scene; When building a safety tool model, first determine the type of safety tool to be simulated, then use 3DMAX to create a 3D model for each tool, and then add the corresponding physical characteristics and interactive behaviors according to the functional characteristics of each tool; Finally, the 3D models of the virtual environment and safety tools are integrated into the virtual scene and the interaction function between the virtual environment and the tools is debugged.

3. According to the VR technology-based safety tool comprehensive training operating system of claim 1, it is characterized in that: When simulating in the constructed virtual scenes and conducting training based on the different safety requirements in each virtual scene and combining with relevant safety tools, first, load the corresponding virtual scene according to the task requirements, then set the corresponding safety operation requirements according to the specific risks in the scene, and then the user obtains the required safety tools in the scene according to the task requirements. After obtaining the tools, the user starts to perform the operation according to the instructions of the virtual task, simulating real risk situations during the operation to test the user's ability to respond in emergency situations.

4. According to the VR technology-based safety tool comprehensive training operating system of claim 1, it is characterized in that: The steps for users to role-play and practice the operation of safety tools in a virtual situation through a simulation device are as follows: Provide users with different role choices in virtual scenarios. Each role corresponds to different job responsibilities and operation requirements. Users perform corresponding tasks in virtual scenarios according to the selected roles. After entering the scenario, users obtain and check the required safety tools according to the role tasks. Subsequently, the corresponding task scenario is automatically generated based on the role selected by the user. The user participates in the task situation from a first-person perspective. After entering the specific task situation, the system provides the user with task guidance, clarifies the current operation goals and operation procedures, and monitors the user's operation behavior in real time during the task execution.

5. According to the VR technology-based safety tool comprehensive training operating system of claim 1, it is characterized in that: According to the user's operation behavior in the virtual scene, the user's behavior characteristics are monitored and recorded in real time, and immediate feedback is provided. The steps for correcting and guiding the user's wrong operation are as follows: The user's operation data in the virtual scene is collected in real time through virtual reality devices, and then the collected data is analyzed using machine learning algorithms to extract the user's key behavioral characteristics. , and compare it with the preset standard operating procedure S to automatically detect whether the user has any operating behavior that does not meet the safety requirements. The expression is: ,in, Indicates the standard operation The characteristic value of the step, is a function used to detect when a user deviates from standard operation. When it is, it returns 1, otherwise it returns 0; When an incorrect operation is detected, the feedback generation algorithm provides immediate feedback to the user. Next, based on the feedback generation algorithm, the system generates specific correction instructions to guide the user to make adjustments according to the correct operation steps to ensure that their operation meets safety standards; After the user adjusts the operation according to the instructions, the machine learning algorithm is used again to evaluate the user's behavioral characteristics to confirm whether the operation has fully complied with safety requirements. At the same time, the user's operation data is continuously recorded.

6. The VR-based safety tool comprehensive training operating system according to claim 5 is characterized in that: When using machine learning algorithms, first, collect data on standard operating procedures. Then, extract the trajectory data of hand and body movements, the use of tools, the time and sequence of operations, and the relative position in the working environment from the user's operation data. Then, use these extracted feature data to train the machine learning model and use the support vector machine to find the optimal classification boundary to identify the patterns of correct and incorrect operations. The expression is: ,in, is the weight vector, Indicates the importance of each feature. is the bias value, defining the boundary of the classification and the extracted feature vector; When the user performs an operation, the user's action data is collected in real time through sensors. The machine learning algorithm processes this data and extracts the user's real-time operation features. Then, these real-time feature data are compared with the trained model to determine whether the current operation complies with the predefined standard operating procedures. If the user's operation deviates from the standard operating specifications, this deviation will be automatically identified and marked as an incorrect operation.

7. The VR-based safety tool comprehensive training operating system according to claim 5 is characterized in that: When using the feedback generation algorithm, first, three feedback forms are preset: visual, auditory, and tactile. Then, the system divides the errors into different levels according to the severity of the wrong operation. When receiving the user's wrong operation, the error is first classified and the feedback form that matches the error level is selected. The system dynamically adjusts the intensity of the feedback according to the number of times the user repeats the error and the severity. The expression is: ,in, Indicates an error In time The feedback strength, represents the initial feedback strength, Indicates the adjustment factor of feedback strength, Indicates that the user is at time Repeat offender Number of errors; Subsequently, based on the feedback generation algorithm, a complete operation demonstration is provided to help users correct errors and return to the standard operating process. If the user continues to make the same incorrect operation, the virtual assistant is automatically triggered to provide the user with visual and voice guidance for the correct operation.

8. The VR-based safety tool comprehensive training operating system according to claim 1 is characterized in that: The steps to generate a personalized evaluation report based on the user's operation records and behavior characteristics, point out the strengths and weaknesses in the operation, and provide corresponding improvement suggestions based on the report content are as follows: First, the user's operation data is collected and recorded in real time through the virtual reality device. Then, the system extracts key behavior features from the collected operation data. Subsequently, the system analyzes the user's operation records, identifies the strengths and weaknesses in the user's operations, and generates a personalized evaluation report based on these analyses. According to the deficiencies in the evaluation report, it provides improvement suggestions. Finally, it designs a personalized training plan based on the user's weak links and recommends that the user conduct targeted operation exercises.

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