Virtual reality-based power grid fault simulation training system and method

Abstract

The invention relates to the technical field of power system operation training, and discloses a virtual reality-based power grid fault simulation training system and method, and the system comprises a virtual scene modeling module which is used for constructing a three-dimensional model of power grid equipment and an operation environment thereof, and providing a vivid training environment for subsequent modules; and the interaction and operation module is connected with the virtual scene modeling module and realizes interaction between the trainee and the virtual environment by using a VR head-mounted display, a handle and a motion capture device. By adopting a high-precision three-dimensional modeling technology, PBR material rendering and an LOD optimization strategy, high simulation of power grid equipment and an operation environment is realized, students can experience a vivid visual effect and immersion in a virtual reality scene, and meanwhile, by combining a gesture recognition technology realized by motion capture equipment and a convolutional neural network, the real-time simulation of the power grid equipment and the operation environment is realized. Operation actions of trainees can be accurately captured, a natural and visual interaction mode is provided, and the authenticity and operation experience of the training system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation training, and in particular to a power grid fault simulation training system and method based on virtual reality. Background Art

[0002] The safe and stable operation of the power grid system is the basic guarantee for the normal operation of modern society. The skill level and emergency response capability of power grid operators directly affect the stability and safety of the power system. Therefore, how to improve the professional skills and emergency response capabilities of power grid operators through effective training methods has become an important topic for power grid companies and training institutions.

[0003] Existing power grid training methods usually include traditional classroom theory teaching, on-site practical operation training, multimedia simulation teaching, etc. These training methods can impart basic operation knowledge and skills to a certain extent, but they still have defects due to limitations in operating environment, equipment conditions, safety risks, etc. Specifically, they are: Traditional classroom teaching and multimedia simulation teaching mainly rely on PPT courseware, video materials or 2D / 3D simulation software. Students can only interact with the screen, mouse and keyboard in a limited way and cannot get real operation experience. In power grid operation training, the equipment and operation process involved are complex. Practical operation experience is crucial for students to master skills. Existing training methods cannot effectively simulate the real operation environment, resulting in students' lack of immersion and sense of operation. Existing power grid training systems mostly cover conventional operation scenarios, but have limited simulation capabilities for complex or emergency fault events. In actual power grid operation, operators need to face various sudden faults or emergencies, such as equipment short circuits, overloads, natural disasters, etc., and traditional training systems cannot flexibly and dynamically generate complex scenarios, resulting in trainees lacking emergency response experience when facing real faults. Existing training systems are usually unable to conduct real-time and refined monitoring of trainees' operational behaviors, and trainees' operational errors are difficult to detect and correct in a timely manner. At the same time, the existing system's evaluation method is single, mainly relying on the time to complete the task and simple operation records. It cannot fully reflect the trainees' specific errors, emergency responses and room for improvement during the operation. The lack of real-time feedback and targeted personalized evaluation has limited the trainees' training efficiency and skill improvement effect.

[0004] Based on the above analysis, the existing technology has the following main problems in power grid training: Traditional training methods lack a realistic and immersive operating environment and cannot provide trainees with a close-to-realistic grid equipment operating experience, resulting in poor interactivity and limited learning effects. The existing simulation training system is not capable of multi-scenario simulation and dynamic scenario generation, especially in the simulation of complex and sudden events. The flexibility and fidelity are low and it cannot effectively improve the students' emergency response capabilities. The existing training system lacks real-time operation monitoring and refined feedback mechanisms, and is unable to immediately correct trainees' incorrect operations. The evaluation methods are single and lack specificity, and are unable to provide trainees with personalized improvement suggestions.

[0005] Therefore, those skilled in the art provide a virtual reality-based power grid fault simulation training system and method to solve the problems raised in the above background technology. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a power grid fault simulation training system and method based on virtual reality to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power grid fault simulation training system based on virtual reality, comprising: Virtual scene modeling module, used to build a three-dimensional model of power grid equipment and its operating environment, providing a realistic virtual environment for subsequent modules and laying a foundation for students' operation and system simulation; Interaction and operation module: Based on the three-dimensional environment generated by the virtual scene modeling module, the VR headset, handle and motion capture device are used to enable the students to interact with the virtual environment, and the students' operation data is passed to the scene dynamic simulation and fault generation module; Scenario dynamic simulation and fault generation module: According to the operation data transmitted by the interaction and operation module, the module dynamically generates corresponding operation scenarios and fault events in the virtual environment, and transmits the simulation data to the real-time data acquisition and intelligent feedback module; The real-time data collection and intelligent feedback module connects the scene dynamic simulation and fault generation modules and is responsible for collecting data from trainees during the operation, including operation steps, time, and error information; The personalized assessment and report generation module connects the real-time data collection and intelligent feedback module to analyze the students' performance based on the collected operation data.

[0008] Preferably, the virtual scene modeling module includes: 3D data acquisition and processing unit, used to obtain geometric data and material information of power grid equipment through laser scanning and photogrammetry technology; High-precision 3D modeling unit, used to perform multi-resolution geometric modeling and UV mapping on the collected data, and render the model surface material using PBR material technology; LOD optimization and packaging unit, used to optimize the detail level of 3D models and package them into a format suitable for virtual reality platforms.

[0009] Preferably, the interaction and operation module includes: The gesture recognition unit is used to recognize the student's gestures through convolutional neural networks and long short-term memory networks. The loss function of gesture recognition is defined as: , in, is the predicted hand joint point coordinates, is the real joint point coordinate, The number of hand joints.

[0010] Preferably, the scenario dynamic simulation and fault generation module includes: The multi-scenario generation and management unit is used to build and manage multiple power grid operation scenarios. The scenarios are represented as follows: , in, Represents a scene object, is the set of properties of the object, Represents a scene, n represents an element pair the number of The fault generation unit simulates the fault scenario of the power grid equipment based on the event-driven finite state machine. The set of fault triggering conditions is: ,in, Indicates the kth trigger condition, k represents the set Medium Element the number of The external environment simulation unit simulates the changes in external environmental conditions through fluid dynamics equations. The calculation formula for wind force effects is: , in, is the drag coefficient, is the air density, is the windward area, is the wind speed.

[0011] Preferably, the real-time data collection and intelligent feedback module includes: The data acquisition unit is used to collect the trainees’ operation data in real time through VR headsets, sensors and cameras. The data is recorded as a time series: , in, represents the timestamp of the nth data point, Represents the parameter at time n The value of Represents the parameter at time n The value of , n represents the total number of data points in set D; The rule matching and feedback unit is used to monitor and provide feedback on trainees' operations in real time based on the operation rule library.

[0012] Preferably, the personalized assessment and report generation module includes: The operation data analysis unit evaluates the trainees’ operation accuracy and safety based on a multi-dimensional evaluation loss function; The improvement suggestion generation unit analyzes the trainees' operational weaknesses through cluster analysis and classification decision tree algorithm and generates personalized improvement suggestions.

[0013] Preferably, in the operation data analysis unit, the loss function is defined as: , in, For loss of operational accuracy, For security loss, is the response time loss, , , is the weight coefficient.

[0014] Preferably, the personalized assessment and report generation module generates an assessment report based on the trainee's operation data and assessment results through dynamic template generation technology combined with natural language generation technology.

[0015] A power grid fault simulation training method based on virtual reality comprises the following steps: S1. Load the three-dimensional model of the power grid equipment through the virtual scene modeling module, and determine the corresponding operation scenario according to the training requirements; S2. Trainees enter the virtual power grid scene through VR headsets, and use the interaction and operation module to recognize the trainees’ gestures and movements to complete the operation of the equipment; S3, through the scenario dynamic simulation and fault generation module, dynamically simulate the behavior of the equipment and sudden fault scenarios in the operation scenario; S4. Through real-time data collection and intelligent feedback modules, students’ operations are monitored in real time, and error prompts or voice guidance are provided; S5. Through the personalized evaluation and report generation module, analyze the students' operation data, generate personalized evaluation reports, and provide improvement suggestions based on operational weaknesses.

[0016] Preferably, in step S1, different operating environments and grid equipment configurations are dynamically determined according to the type of training tasks and the trainees' operational proficiency to adapt to different training requirements; In step S2, the system uses the vibration feedback, visual prompts and sound prompts of the VR device's handle to enable the trainee to intuitively feel the device's operating status and action completion status when operating; In the step S3, the most common equipment failure types are predefined based on historical failure data of power grid equipment and expert experience, and failure events are randomly determined or triggered based on the trainee's operating status during the training process; In the step S4, based on the real-time detection of the trainee's operation, combined with the dynamic state of the device and the trainee's operating habits, personalized real-time prompts and error correction suggestions are provided to the trainee; In step S5, the system generates an evaluation report based on the trainee's operation process, analyzes the trainee's operation strategy, identifies the trainee's reaction time when dealing with sudden failures or emergencies, the correctness of the emergency handling strategy, and generates targeted training improvement suggestions.

[0017] The present invention provides a power grid fault simulation training system and method based on virtual reality, which has the following beneficial effects: 1. The present invention achieves a high degree of simulation of power grid equipment and operating environment by adopting high-precision 3D modeling technology, PBR material rendering and LOD optimization strategy, so that trainees can experience realistic visual effects and immersion in virtual reality scenes. At the same time, the gesture recognition technology realized by combining motion capture equipment and convolutional neural network can accurately capture the trainees' operating actions, provide a natural and intuitive interaction method, and greatly improve the authenticity and operating experience of the training system. The application of its highly immersive technology makes the trainees' training effect closer to actual operation, improving the pertinence and practicality of the training.

[0018] 2. The present invention supports the dynamic generation and editing of various power grid operation scenarios through scenario editing tools and event-driven finite state machines, so that the training system can flexibly adapt to different operation tasks and fault scenarios. By integrating historical fault data of power grid equipment and expert experience, the system can simulate various complex and real emergencies, such as equipment short circuit, overload or the impact of natural disasters, to help trainees fully exercise their emergency response and fault handling capabilities in a virtual environment. Its multi-scenario and dynamic scenario training design effectively solves the problems of single scenario and insufficient emergency training in traditional training.

[0019] 3. The real-time data collection and intelligent feedback module of the present invention can monitor and record each step of the trainee's operation in a refined manner. Combined with the feedback algorithm based on rule matching, the system can immediately detect the trainee's erroneous operation and provide real-time prompts and correction suggestions. In addition, through the personalized evaluation module, the system can conduct a comprehensive data analysis of the trainee's operation process and generate a multi-dimensional evaluation report to provide personalized improvement suggestions for the trainee's operation strategy and weak links. The design of real-time feedback and personalized evaluation enables the training system to immediately guide trainees to correct mistakes, and can optimize the training content according to the trainees' actual level, thereby significantly enhancing the training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a system framework diagram of the present invention; Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0021] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0022] The present invention is described in detail below in conjunction with the accompanying drawings: Example: Please refer to the attached Figure 1 and attached Figure 2 , an embodiment of the present invention provides a power grid fault simulation training system based on virtual reality, comprising: Virtual scene modeling module, used to build a three-dimensional model of power grid equipment and its operating environment, providing a realistic virtual environment for subsequent modules and laying a foundation for students' operation and system simulation; Interaction and operation module: Based on the three-dimensional environment generated by the virtual scene modeling module, the VR headset, handle and motion capture device are used to enable the students to interact with the virtual environment, and the students' operation data is passed to the scene dynamic simulation and fault generation module; Scenario dynamic simulation and fault generation module: According to the operation data transmitted by the interaction and operation module, the module dynamically generates corresponding operation scenarios and fault events in the virtual environment, and transmits the simulation data to the real-time data acquisition and intelligent feedback module; The real-time data collection and intelligent feedback module connects the scene dynamic simulation and fault generation modules and is responsible for collecting data from trainees during the operation process, including operation steps, time, and error information; The personalized assessment and report generation module connects the real-time data collection and intelligent feedback module to analyze the students' performance based on the collected operation data.

[0023] Benefits of the virtual scene modeling module: Using LOD optimization strategy to improve rendering efficiency, the virtual scene can run smoothly under high complexity, and can provide highly realistic power grid equipment and environment, so that trainees can get close to real operation experience in virtual reality, thereby enhancing immersion and training effect. At the same time, LOD optimization can significantly reduce the dependence on hardware and improve the fluency of the system and user experience; The benefits of the interaction and operation module make the operation process more intuitive and natural. Its natural interaction method effectively enhances students' sense of immersion and learning interest, promotes the memory and mastery of operation skills, and reduces errors caused by complex interface operations. The benefits of the scenario dynamic simulation and fault generation module are that it can generate a variety of operation scenarios and fault events, and realize dynamic simulation of equipment faults based on the event-driven model, so that the system can flexibly simulate various scenarios in power grid operation, including normal operation and sudden fault scenarios, and help trainees conduct comprehensive emergency handling and equipment operation training in virtual scenarios. Trainees can effectively improve their emergency handling capabilities and ability to cope with complex situations; The benefits of the real-time data collection and intelligent feedback module are that it can continuously collect data during the trainee's operation and generate real-time feedback based on the rule matching algorithm. It can promptly detect and correct the trainee's errors in operation, provide operation guidance and safety tips, and ensure that the trainee can obtain effective operation feedback in real time during the operation, thereby improving operation accuracy, reducing error rates, and enhancing learning effects and safety awareness. The benefits of the personalized assessment and report generation module are that it accurately assesses the students' operational level, emergency handling ability and error tendency, and provides targeted improvement suggestions. Its personalized assessment and feedback mechanism can help students understand their own shortcomings, clarify the direction of improvement, and effectively improve students' learning efficiency and ability development.

[0024] The virtual scene modeling module includes: 3D data acquisition and processing unit, used to obtain geometric data and material information of power grid equipment through laser scanning and photogrammetry technology; High-precision 3D modeling unit, used to perform multi-resolution geometric modeling and UV mapping on the collected data, and render the model surface material using PBR material technology; LOD optimization and packaging unit, used to optimize the detail level of 3D models and package them into a format suitable for virtual reality platforms.

[0025] Benefits of 3D data acquisition and processing unit: Accurately capture the real geometric shape, size, texture and appearance characteristics of the equipment, provide high-precision 3D point cloud data, and photogrammetry technology can obtain surface texture information more meticulously, and reconstruct the 3D data of power grid equipment with high precision and efficiency, laying a solid foundation for subsequent high-precision 3D modeling, greatly reducing the workload and errors of manual modeling; The benefits of the high-precision 3D modeling unit are that it can flexibly adjust the resolution of the model while maintaining high precision, taking into account both the fineness of the model and the operating efficiency of the system. UV mapping ensures the precise positioning of material textures, while PBR material technology can achieve realistic light and shadow and material performance, greatly improving the visual effects of the 3D scene and making students more immersed in the virtual environment. The benefits of the LOD optimization and packaging unit are that the model's level of detail is dynamically adjusted according to the distance between the trainee and the device, thereby reducing the computational burden of rendering, significantly improving the rendering efficiency of the system, reducing hardware requirements, and ensuring a smooth transition of visual effects under near and far perspectives. At the same time, the optimized model is packaged into a format compatible with the virtual reality platform to ensure that the model can be smoothly applied to different VR platforms, allowing the virtual reality training system to achieve a better balance between performance and immersion.

[0026] The interaction and operation modules include: The gesture recognition unit is used to recognize the student's gestures through convolutional neural networks and long short-term memory networks. The loss function of gesture recognition is defined as: , in, is the predicted hand joint point coordinates, is the real joint point coordinate, The number of hand joints.

[0027] Benefits of Gesture Recognition Unit: With gesture recognition technology, students can operate virtual devices without using traditional mouse, keyboard or complicated operating handles, which can improve students' intuitiveness and immersion, allowing them to get started faster and master the essentials of operation; The benefit of the loss function of gesture recognition is that it can directly reflect the training goal of the model, that is, matching the predicted values ​​of the hand joints with the true values, and training with the goal of minimizing the overall error. Improving the accuracy of overall gesture recognition helps the gesture recognition unit maintain a high operating efficiency and response speed in real-time interactive scenarios, and provides strong support for natural interaction in virtual reality power grid training systems.

[0028] The scenario dynamic simulation and fault generation modules include: The multi-scenario generation and management unit is used to build and manage multiple power grid operation scenarios. The scenarios are represented as follows: , in, Represents a scene object, is the set of properties of the object, Represents a scene, n represents an element pair the number of The fault generation unit simulates the fault scenario of the power grid equipment based on the event-driven finite state machine. The set of fault triggering conditions is: , in, Indicates the kth trigger condition, where k represents the set Medium Element the number of The external environment simulation unit simulates the changes in external environmental conditions through fluid dynamics equations. The calculation formula for wind force effects is: , in, is the drag coefficient, is the air density, is the windward area, is the wind speed.

[0029] Benefits of the multi-scenario generation and management unit: It can flexibly and effectively organize and adjust various scenarios of power grid operation. According to actual training needs, it can dynamically load or switch different scenarios, making the training content richer and more diverse, and further improving the practical skills of trainees; The advantage of the fault generation unit is that it can flexibly generate sudden fault events according to the real-time status and operating conditions of the power grid equipment, so that the system can realistically simulate various fault scenarios in the operation of the power grid. Trainees can better exercise their emergency response capabilities and operational response speed in sudden events, and effectively improve their safe operation skills in a real environment; Benefits of the external environment simulation unit: It simulates complex external environmental changes and their impact on power grid equipment, allowing trainees to conduct operational drills under variable climate conditions, thereby improving their operational adaptability in complex environments, making the training content closer to reality, and improving the comprehensiveness and authenticity of the training; The benefits of the scenario representation formula are that the scenario objects and attribute sets are organized in the form of a set, making the scenario management more structured and clear. Each scenario object contains independent attributes, which facilitates the flexible configuration and adjustment of objects and attributes between different scenarios. It can support dynamic scenario switching and adjustment, and enable the system to quickly generate and load new scenarios according to different training needs, thus improving the flexibility of scenario management. The benefits of fault trigger condition set are that the fault trigger condition set is clearly defined, so that the generation of fault events can be flexibly and dynamically judged based on the conditions. Each trigger condition corresponds to a certain operating state of the power grid equipment or a certain change in the external environment. Therefore, the system can generate corresponding fault scenarios according to the trigger conditions, enhancing the accuracy and flexibility of fault simulation. The advantage of the calculation formula for wind force impact is that it can accurately reflect the wind force under different wind speeds and equipment windward areas, and dynamically affect the physical behavior of the equipment, providing trainees with a more realistic external environment simulation, further improving the realism of the training and the adaptability of trainees.

[0030] Real-time data collection and intelligent feedback modules include: The data acquisition unit is used to collect the trainees’ operation data in real time through VR headsets, sensors and cameras. The data is recorded as a time series: , in, represents the timestamp of the nth data point, Represents the parameter at time n The value of Represents the parameter at time n The value of , n represents the total number of data points in set D; The rule matching and feedback unit is used to monitor and provide feedback on trainees' operations in real time based on the operation rule library.

[0031] The benefits of the data acquisition unit are that it can fully monitor the trainees' operating behaviors in the virtual environment, and record important information such as hand positions, changes in viewing angles, and changes in the status of equipment in real time. Its comprehensive data acquisition method ensures that the system can monitor the trainees' operating behaviors in detail, and records data at a high sampling rate and low latency, ensuring the timeliness of feedback and evaluation during operation, which helps to improve the training efficiency of trainees and the response speed of the system; The advantage of the rule matching and feedback unit is that it can quickly determine whether the operation complies with the specification and avoid errors caused by human judgment. The system can immediately generate feedback prompts and provide voice or visual prompts to allow trainees to correct operations in time, reduce the accumulation of errors in training, and ensure the effectiveness and safety of training; The advantage of the time series representation of data records is that it can fully reflect the changes in various states during the trainee's operation, forming a continuous, time-ordered record. Its data representation method is helpful for subsequent analysis and can clearly show the overall changes in the operation process, allowing the system to accurately locate each time point in the operation process, making it easier to trace back the operation history when evaluating the trainee's performance and analyze whether each step of the operation is standardized; The benefit of rule matching is that it can flexibly determine whether the trainees' operations are compliant based on specific conditions. The trigger mechanism of each condition can be set according to the specific specifications, steps and safety standards in the operation, ensuring the accuracy of the training process. It can make real-time judgments and respond quickly during the trainees' operations. When matching non-compliant operations, the system can immediately provide corrective feedback to help trainees correct errors in a timely manner and reduce error accumulation and operations that deviate from norms.

[0032] The personalized assessment and reporting module includes: The operation data analysis unit evaluates the trainees’ operation accuracy and safety based on a multi-dimensional evaluation loss function; The improvement suggestion generation unit analyzes the students' operational weaknesses through cluster analysis and classification decision tree algorithm and generates personalized improvement suggestions; In the operational data analysis unit, the loss function is defined as: , in, For loss of operational accuracy, For security loss, is the response time loss, , , is the weight coefficient; The personalized assessment and report generation module uses dynamic template generation technology combined with natural language generation technology to generate assessment reports based on students' operation data and assessment results.

[0033] The benefits of the operation data analysis unit are that it can comprehensively consider the students' operation performance, focus on the accuracy of the operation, focus on safety and response speed, and reflect the comprehensive ability of the students from multiple angles. Based on the calculation of the loss function, the system can accurately quantify the students' operation errors and provide data support for subsequent personalized improvement suggestions; Benefits of the Improvement Suggestion Generation Unit: Through cluster analysis and classification decision trees, common mistakes or easily overlooked links of trainees can be detected from the operation data, and more accurate improvement directions can be provided to trainees, avoiding the "one-size-fits-all" model in traditional training, and effectively improving trainees' learning efficiency and practical operation ability; Benefits of the personalized assessment and report generation unit: The content and structure of the report can be flexibly adjusted according to the operation data and assessment results of different students, so that each report can accurately reflect the specific situation of the students, and can convert complex assessment results into easy-to-understand text descriptions, helping students to more intuitively understand their performance and improvement directions; The benefits of evaluating the loss function are that the weights of different loss items can be flexibly adjusted according to specific training objectives to form an evaluation result that is more in line with actual training needs, making the evaluation process more scientific and convenient for subsequent statistics and analysis; The benefits of cluster analysis and classification decision tree algorithms are that they can classify students' operation behaviors into different categories and identify the weak links of common characteristics, so as to provide targeted suggestions for different types of students. Classification decision trees can use simple and explainable rules to determine whether students are prone to making mistakes in certain situations, and more intuitively analyze the source of students' problems and provide clear suggestions for improvement. The benefits of dynamic template generation and natural language generation are to dynamically generate report contents with different structures according to different assessment data, ensure that the report content is highly relevant to the specific situation of the trainees, avoid the limitations of a single template, and enable trainees and instructors to easily understand the core content and suggestions of the report, and more effectively formulate the next step of learning and improvement plans.

[0034] A power grid fault simulation training method based on virtual reality comprises the following steps: S1. Load the three-dimensional model of the power grid equipment through the virtual scene modeling module, and determine the corresponding operation scenario according to the training requirements; S2. Trainees enter the virtual power grid scene through VR headsets, and use the interaction and operation module to recognize the trainees’ gestures and movements to complete the operation of the equipment; S3, through the scenario dynamic simulation and fault generation module, dynamically simulate the behavior of the equipment and sudden fault scenarios in the operation scenario; S4. Through real-time data collection and intelligent feedback modules, students’ operations are monitored in real time, and error prompts or voice guidance are provided; S5. Analyze trainees’ operation data through the personalized evaluation and report generation module, generate personalized evaluation reports, and provide improvement suggestions based on operational weaknesses; In step S1, different operating environments and grid equipment configurations are dynamically determined according to the type of training tasks and the trainees’ operational proficiency to adapt to different training requirements; In step S2, the system uses the vibration feedback, visual prompts, and sound prompts of the VR device's handle to enable trainees to intuitively feel the device's operating status and action completion status when operating; In step S3, the most common equipment failure types are predefined based on historical failure data of power grid equipment and expert experience, and failure events are triggered randomly or based on the trainee's operating status during the training process; In step S4, based on real-time detection of trainee operations, combined with the dynamic state of the equipment and the trainee's operating habits, personalized real-time prompts and error correction suggestions are provided to the trainee; In step S5, the system generates an evaluation report based on the trainee's operation process, analyzes the trainee's operation strategy, identifies the trainee's reaction time when dealing with sudden failures or emergencies, the correctness of the emergency handling strategy, and generates targeted training improvement suggestions.

[0035] Step S1 dynamically determines the operating environment and equipment configuration, which can flexibly adapt to different training tasks and trainee levels, thus improving the pertinence and practicality of the training; In step S2, the vibration, visual and sound prompts of the handle enable trainees to perceive the operation status more intuitively, enhancing the operation experience and sense of reality; Step S3 defines common fault types based on fault data and expert experience, flexibly triggers fault events, and improves trainees’ emergency response capabilities and the actual effect of training; Step S4 detects operations in real time and provides personalized prompts and error correction suggestions, which can effectively guide trainees to correct mistakes and strengthen operational standards and safety awareness; The S5 step generates an evaluation report based on operational data, conducts in-depth analysis of emergency response strategies, and provides improvement suggestions to help trainees improve their response speed and handling skills.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A virtual reality-based power grid fault simulation training system, characterized in that: include: Virtual scene modeling module, used to build a three-dimensional model of power grid equipment and its operating environment, providing a realistic virtual environment for subsequent modules and laying a foundation for students' operation and system simulation; Interaction and operation module: Based on the three-dimensional environment generated by the virtual scene modeling module, the VR headset, handle and motion capture device are used to enable the students to interact with the virtual environment, and the students' operation data is passed to the scene dynamic simulation and fault generation module; Scenario dynamic simulation and fault generation module: According to the operation data transmitted by the interaction and operation module, the module dynamically generates corresponding operation scenarios and fault events in the virtual environment, and transmits the simulation data to the real-time data acquisition and intelligent feedback module; The real-time data collection and intelligent feedback module connects the scene dynamic simulation and fault generation modules and is responsible for collecting data from trainees during the operation, including operation steps, time, and error information; The personalized assessment and report generation module connects the real-time data collection and intelligent feedback module to analyze the students' performance based on the collected operation data.

2. A virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The virtual scene modeling module includes: 3D data acquisition and processing unit, used to obtain geometric data and material information of power grid equipment through laser scanning and photogrammetry technology; High-precision 3D modeling unit, used to perform multi-resolution geometric modeling and UV mapping on the collected data, and render the model surface material using PBR material technology; LOD optimization and packaging unit, used to optimize the detail level of 3D models and package them into a format suitable for virtual reality platforms.

3. A virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The interaction and operation module includes: The gesture recognition unit is used to recognize the student's gestures through convolutional neural networks and long short-term memory networks. The loss function of gesture recognition is defined as: ， in, is the predicted hand joint point coordinates, is the real joint point coordinate, The number of hand joints.

4. The virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The scenario dynamic simulation and fault generation module includes: The multi-scenario generation and management unit is used to build and manage multiple power grid operation scenarios. The scenarios are represented as follows: ， in, Represents a scene object, is the set of properties of the object, Represents a scene, n represents an element pair the number of The fault generation unit simulates the fault scenario of the power grid equipment based on the event-driven finite state machine. The set of fault triggering conditions is: ,in, Indicates the kth trigger condition, where k represents the set Medium Element the number of The external environment simulation unit simulates the changes in external environmental conditions through fluid dynamics equations. The calculation formula for wind force effects is: ， in, is the drag coefficient, is the air density, is the windward area, is the wind speed.

5. The virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The real-time data collection and intelligent feedback module includes: The data acquisition unit is used to collect the trainees’ operation data in real time through VR headsets, sensors and cameras. The data is recorded as a time series: ， in, represents the timestamp of the nth data point, Represents the parameter at time n The value of Represents the parameter at time n The value of , n represents the total number of data points in set D; The rule matching and feedback unit is used to monitor and provide feedback on trainees' operations in real time based on the operation rule library.

6. The virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The personalized assessment and report generation module includes: The operation data analysis unit evaluates the trainees’ operation accuracy and safety based on a multi-dimensional evaluation loss function; The improvement suggestion generation unit analyzes the students' operational weaknesses through cluster analysis and classification decision tree algorithm and generates personalized improvement suggestions.

7. A virtual reality-based power grid fault simulation training system according to claim 6, characterized in that: In the operation data analysis unit, the loss function is defined as: ， in, For loss of operational accuracy, For security loss, is the response time loss, , , is the weight coefficient.

8. The virtual reality-based power grid fault simulation training system according to claim 1, characterized in that: The personalized assessment and report generation module generates an assessment report based on the trainees' operation data and assessment results by using dynamic template generation technology combined with natural language generation technology.

9. A virtual reality-based power grid fault simulation training method, according to a virtual reality-based power grid fault simulation training system according to any one of claims 1 to 8, characterized in that: The steps include: S1. Load the three-dimensional model of the power grid equipment through the virtual scene modeling module, and determine the corresponding operation scenario according to the training requirements; S2. Trainees enter the virtual power grid scene through VR headsets, and use the interaction and operation module to recognize the trainees’ gestures and movements to complete the operation of the equipment; S3, through the scenario dynamic simulation and fault generation module, dynamically simulate the behavior of the equipment and sudden fault scenarios in the operation scenario; S4. Through real-time data collection and intelligent feedback modules, students' operations are monitored in real time, and error prompts or voice guidance are provided; S5. Through the personalized assessment and report generation module, analyze the students' operation data, generate personalized assessment reports, and provide improvement suggestions based on operational weaknesses.

10. A virtual reality-based power grid fault simulation training method according to claim 9, characterized in that: In the step S1, different operating environments and grid equipment configurations are dynamically determined according to the type of training tasks and the trainees' operational proficiency to adapt to different training requirements; In step S2, the system uses the vibration feedback, visual prompts and sound prompts of the VR device's handle to enable the trainee to intuitively feel the device's operating status and action completion status when operating; In the step S3, the most common equipment failure types are predefined based on historical failure data of power grid equipment and expert experience, and failure events are randomly determined or triggered based on the trainee's operating status during the training process; In the step S4, based on the real-time detection of the trainee's operation, combined with the dynamic state of the device and the trainee's operating habits, personalized real-time prompts and error correction suggestions are provided to the trainee; In step S5, the system generates an evaluation report based on the trainee's operation process, analyzes the trainee's operation strategy, identifies the trainee's reaction time when dealing with sudden failures or emergencies, the correctness of the emergency handling strategy, and generates targeted training improvement suggestions.

Images