Power equipment maintenance operation virtual simulation system and training method

Abstract

The invention discloses a power equipment maintenance operation virtual simulation system and training method, and the system comprises a virtual simulation platform which is used for loading a power equipment virtual environment and a maintenance task; the data acquisition module is used for acquiring operation data in real time; the intelligent fault diagnosis module is used for dynamically generating a fault scene according to the operation data and optimizing a fault simulation and diagnosis algorithm through machine learning; the feedback module is used for providing operation feedback for students in real time according to the operation data; the data analysis platform is used for analyzing the operation data and generating a personalized training scheme; the training cost is greatly reduced, and the risk of equipment damage caused by misoperation is reduced. Meanwhile, the repeatability of the virtual environment enables the trainee to practice repeatedly until the trainee masters various skills skillfully, thereby remarkably improving the training efficiency and quality.

Description

Technical Field

[0001] The invention belongs to the technical field of electric power equipment maintenance operation training, and relates to an electric power equipment maintenance operation virtual simulation system and a training method. Background Art

[0002] As a key link in the power industry, the importance of power equipment maintenance is self-evident. This operation is not only related to the safe operation of power equipment, but also directly affects the stability and reliability of the power system. Since power equipment usually works in a complex and changeable environment, such as high voltage, high temperature and other extreme conditions, this places extremely high demands on the professional skills and emergency handling capabilities of maintenance personnel.

[0003] The traditional training model for power equipment maintenance mainly relies on classroom explanations and on-site operations. Although classroom explanations can systematically impart theoretical knowledge, they lack practical operation links, making it difficult for trainees to truly master maintenance skills. On-site operations can provide valuable practical experience, but are strictly limited by time, location, and equipment conditions. More importantly, on-site operations have high safety risks, especially when dealing with high-risk equipment such as high voltage and high temperature, the safety of operators becomes a major problem.

[0004] In addition, the traditional training model also has the problem of insufficient interactivity and personalization. Classroom explanations often adopt a "one-size-fits-all" teaching method, which fails to provide targeted training based on the actual abilities and needs of trainees. This lack of personalization in training not only reduces the training effect, but also wastes valuable teaching resources. Although on-site operations can reflect personalized teaching to a certain extent, their popularity and scale are limited due to safety risks and equipment conditions.

[0005] From a cost perspective, traditional power equipment maintenance training also faces great pressure. On-site operations require a lot of equipment maintenance costs and personnel scheduling costs, while classroom teaching requires paying teacher salaries and venue rental costs. These high costs not only increase the financial burden of enterprises, but also restrict the popularization and large-scale development of training. Summary of the invention

[0006] The purpose of the present invention is to solve the technical problem that the existing training system in the prior art is still difficult to achieve efficient, safe and personalized training, and to provide a virtual simulation system and training method for power equipment maintenance operations.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a virtual simulation system for power equipment maintenance operations, comprising: Virtual simulation platform, used to load virtual environment and maintenance tasks of power equipment; Data collection module, used to collect students' operation data in real time; Intelligent fault diagnosis module, which is used to dynamically generate fault scenarios based on trainees’ operation data combined with the virtual simulation platform, and optimize fault simulation and diagnosis through machine learning; Feedback module, used to provide students with real-time operational feedback based on their operations; Data analysis platform, used to analyze trainees' operation data and generate personalized training plans.

[0008] Furthermore, the virtual simulation platform provides students with interaction with the virtual environment through a virtual reality device, and the virtual reality device includes a VR helmet and a handle.

[0009] Further, the trainee's operation data includes operation time, operation steps and operation accuracy.

[0010] Furthermore, the intelligent fault diagnosis module dynamically adjusts the type and complexity of the fault scenario according to the trainee's operating performance and provides personalized fault simulation.

[0011] Furthermore, providing operation feedback to the trainee in real time according to the trainee's operation means giving feedback on the trainee's operation in the form of images or voice.

[0012] Furthermore, it also includes a collaborative work module, which is used to support multiple students to collaboratively complete tasks in a virtual environment.

[0013] Furthermore, it also includes a multi-sensory interaction module, which is used to provide tactile, sound and visual feedback to enhance the trainees' training experience.

[0014] A second aspect of the present invention provides a virtual simulation training method for power equipment maintenance operations, comprising the following steps: Trainees select maintenance tasks and enter the virtual simulation environment; Students operate according to the task requirements, and the system monitors and collects students' operation data in real time; Dynamically generate fault scenarios based on trainees’ operational data and optimize fault simulation and diagnosis through machine learning; Provide real-time operational feedback based on trainees’ operational data; Generate personalized training plans based on trainees' operation data.

[0015] Furthermore, the fault scenarios are dynamically generated according to the trainees' operation data, and a GAN network is used to generate the fault scenarios.

[0016] Furthermore, providing operation feedback in real time based on the trainee's operation data is specifically as follows: Provide operational feedback to trainees through images, voice or text; If the trainee operates correctly, feedback will be given through voice or graphic prompts; If the trainee makes an error, the feedback module points out the problem and provides suggestions for improvement.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a virtual simulation system for power equipment maintenance operations. By using a virtual environment of power equipment loaded on a virtual simulation platform, trainees can participate in maintenance tasks in an immersive way. This highly realistic simulation environment not only enhances the fun of learning, but also enables trainees to be familiar with and master the complex structure and working principle of power equipment in a risk-free environment. The data acquisition module can accurately capture and record each step of the trainee's operation in the virtual environment, providing a detailed data basis for the subsequent formulation of personalized training programs. This helps to accurately assess the trainee's skill level and discover blind spots and deficiencies in operation. The intelligent fault diagnosis module generates a variety of fault scenarios in real time based on the trainee's operation data, which not only tests the trainee's adaptability, but also makes the fault simulation closer to the real situation and the diagnostic algorithm more accurate and efficient through continuous optimization of machine learning. This process greatly promotes the improvement of trainees' fault diagnosis and troubleshooting capabilities. The feedback module provides operational feedback to trainees in real time, helping them to immediately recognize the correctness and errors in the operation and adjust the learning strategy in time. Combined with the data analysis platform's in-depth analysis of operational data, the system can tailor personalized training plans for each student, ensuring that the training content is both in line with the student's current skill level and can effectively promote their skill growth.

[0018] Furthermore, in this virtual simulation training method for power equipment maintenance operations, trainees enter a highly realistic virtual simulation environment. This immersive experience greatly enhances the interactivity and practicality of the training. Trainees can simulate real operations in a risk-free environment and effectively improve their skill levels. By real-time monitoring and collecting trainees' operation data, the system can accurately grasp the trainees' learning progress and skill mastery, providing strong support for subsequent training. Dynamically generating fault scenarios based on trainees' operation data is an innovation that makes training closer to the real working environment. Through the continuous optimization of machine learning algorithms, fault simulation and diagnosis algorithms can more accurately reflect the characteristics of actual equipment failures, thereby helping trainees better master the skills of fault diagnosis and elimination. It can provide real-time operational feedback based on trainees' operation data. This instant feedback mechanism helps trainees to promptly discover and correct errors in operation, thereby accelerating skill improvement. At the same time, the system can also adjust the training content and difficulty in real time based on feedback data to ensure that the training always remains in the trainees' optimal learning range. By deeply analyzing the trainees' operation data, a personalized training plan can be generated. This customized training method not only meets the trainees' learning habits and skill levels, but also dynamically adjusts the training strategy based on the trainees' progress to ensure the maximization of the training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a diagram of the architecture of a virtual simulation system for power equipment maintenance operations according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a virtual simulation system for power equipment maintenance operations according to Embodiment 1 of the present invention; Figure 3 This is a feedback mechanism diagram of a virtual simulation system for power equipment maintenance work according to Embodiment 1 of the present invention; Figure 4 This is a flow chart of a virtual simulation training method for power equipment maintenance operations according to Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings: Example 1 This embodiment provides a virtual simulation system for power equipment maintenance operations. The system adopts virtual reality technology, artificial intelligence algorithms and multi-sensory interactive feedback technology to solve the problems existing in traditional power equipment maintenance training, such as high safety risks, high training costs, low training efficiency, lack of personalization and interactivity, etc.

[0025] like Figure 1 As shown in the figure, the architecture of the system includes a virtual simulation platform, a data acquisition module, an intelligent fault diagnosis module, a feedback module, a multi-sensory interaction module, a collaborative operation module and a data analysis platform, which can provide trainees with a highly realistic and interactive training experience. Trainees enter the virtual environment of power equipment through the virtual simulation platform, in which the maintenance operation of power equipment is simulated. The system collects trainees' operation data in real time and provides guidance through the feedback module. The intelligent fault diagnosis module dynamically generates fault scenarios based on the trainees' operation performance, adjusts the difficulty and complexity in real time, and gradually optimizes the fault simulation and diagnosis algorithm through machine learning. The multi-sensory interaction module enhances the trainees' sense of immersion and operational reality through tactile, sound and visual feedback, further improving the quality of training.

[0026] The core components of the virtual simulation system for power equipment maintenance operations include: 1. Virtual simulation platform: This platform uses 3D modeling technology to build detailed virtual models of power equipment, including substation equipment, generator sets, and transmission lines. Trainees enter the virtual environment through virtual reality equipment (such as VR helmets, handles, etc.) to perform maintenance tasks on power equipment. The virtual simulation platform supports simulating various states of equipment in actual operation, such as environmental parameters such as equipment temperature, humidity, and noise, thereby providing trainees with a more realistic operating experience.

[0027] 2. Data collection module: This module uses motion capture technology and sensors to collect students' operation data in real time, including operation duration, accuracy, operation steps, etc. Through high-precision data collection, the system can fully understand the students' operation behavior and transmit the data to the data analysis platform for subsequent processing. The data collection module ensures that the system can monitor the students' operation process in real time and provide feedback to help students continuously optimize their skills.

[0028] 3. Intelligent fault diagnosis module: The intelligent fault diagnosis module is the innovative core of the present invention. This module can generate and adjust fault scenarios related to trainees' operations in real time through artificial intelligence algorithms, such as GAN. The system automatically adjusts the type, difficulty and complexity of the fault according to the accuracy and repair efficiency of the trainees' operations to improve the trainees' emergency response and fault diagnosis capabilities. The module continuously optimizes the fault generation and diagnosis process through the Model-Agnostic Meta-Learning (MAML) framework combined with causal reasoning guidance, making subsequent training more accurate and personalized, in line with the trainees' learning progress and ability level.

[0029] 4. Feedback module: This module is used to provide real-time feedback on the trainees’ operation behaviors. It provides timely operation feedback in the form of images, voice or text. For example, Figure 3 As shown in the figure, if the trainee operates correctly, the system will give a voice or graphic prompt; if the operation is wrong, the system will point out the problem and provide improvement suggestions. The real-time and accuracy of this module ensure that trainees can get feedback at the moment of error, so as to continuously improve their operating skills.

[0030] 5. Multi-sensory interaction module: Through multi-sensory interactions such as tactile feedback, sound feedback and visual enhancement, trainees can get a more realistic training experience. The tactile feedback device simulates the physical properties of the equipment such as weight, hardness and vibration, the sound feedback device provides the sound of normal operation and failure of the equipment, and the visual enhancement device can simulate emergency failure environments such as fire and smoke to enhance the trainees' immersion and emergency handling capabilities. The multi-sensory interaction module greatly improves the realism of the training and helps trainees to conduct repeated training in a realistic virtual environment.

[0031] 6. Collaborative work module: The collaborative work module supports multiple trainees to conduct collaborative training in a virtual environment. Trainees can complete teamwork tasks in the same virtual environment, simulating maintenance work that requires teamwork in actual work. Through this module, trainees can improve their teamwork ability and achieve real-time operation synchronization and communication interaction in a virtual environment.

[0032] 7. Data Analysis Platform: This platform collects and analyzes students’ operation data and generates personalized learning reports. The data analysis platform automatically adjusts the training content and difficulty based on the students’ operation performance to ensure that each student can train according to their own progress and ability level. The platform can also record the strengths and weaknesses of students and provide targeted improvement suggestions.

[0033] like Figure 2 As shown in the figure, the workflow of the virtual simulation system for power equipment maintenance operations is as follows: First, the trainees enter the virtual simulation environment of the power equipment through the virtual reality device, select the relevant maintenance task and start the operation. The virtual simulation platform loads the corresponding equipment and fault scenarios according to the preset training tasks. The trainees interact with the virtual equipment through the handle and other input devices to perform maintenance operations. The data acquisition module collects the trainees' operation data in real time and transmits it to the data analysis platform for processing.

[0034] The intelligent fault diagnosis module generates corresponding fault scenarios based on the trainee's operation behavior and adjusts the type and difficulty of the fault according to the trainee's repair progress. If the trainee fails to complete an operation correctly, the system will diagnose and adjust the fault scenario in real time to generate a more challenging repair task. Through this dynamic adjustment, trainees can improve their emergency response and fault diagnosis capabilities in different fault scenarios.

[0035] The multi-sensory interaction module provides tactile, sound and visual feedback during the trainee's operation, enhancing the trainee's sense of immersion and operation experience. For example, when the trainee repairs the equipment, the system will simulate the vibration and working noise of the equipment to help the trainee better perceive the details of the operation. The collaborative operation module allows multiple trainees to conduct collaborative training in the same virtual environment to improve teamwork capabilities.

[0036] After completing the training task, the data analysis platform will generate a learning report based on the trainee's operation data, evaluate the trainee's strengths and weaknesses, and provide improvement suggestions. At the same time, the system will adjust the subsequent training content and difficulty based on the trainee's performance to ensure that the trainee is trained at the pace that best suits him or her.

[0037] The most innovative technical feature of this system is the intelligent fault diagnosis module. By combining artificial intelligence algorithms and machine learning technology, the system can generate and adjust fault scenarios in real time according to the trainees' operating behaviors, dynamically adjust the difficulty and complexity of training, and thus improve the trainees' fault diagnosis and emergency response capabilities. This module not only provides personalized feedback based on operating performance, but also gradually optimizes fault simulation and diagnosis algorithms through machine learning to ensure that subsequent training tasks are more accurate and meet the trainees' ability requirements. This intelligent and dynamic fault diagnosis and feedback mechanism significantly improves the effectiveness of training, helps trainees perform more accurate fault repair operations in a virtual environment, and ultimately enhances their emergency response capabilities in actual work.

[0038] The advantages of this system are reflected in many aspects. First, the system combines virtual simulation technology with multi-sensory interactive equipment to enable trainees to repeatedly conduct operational training in a safe virtual environment and obtain a highly realistic training experience. Secondly, the intelligent fault diagnosis module can diagnose the trainees' operating behavior in real time, and provide personalized feedback and improvement suggestions to help trainees continuously optimize their operating skills. The system greatly reduces training costs and avoids the high costs of equipment and personnel in traditional training models. In addition, the data analysis platform combines the trainees' operating data to provide each trainee with a personalized training plan, ensuring that each trainee can receive the most suitable training according to their own progress and ability, greatly improving the efficiency and effectiveness of training.

[0039] In summary, the virtual simulation system for power equipment maintenance operations provided in this embodiment solves various problems existing in traditional training through the integration of multiple advanced technologies, and provides a safe, efficient, economical and personalized solution for training on power equipment maintenance operations.

[0040] Example 2 This embodiment provides a virtual simulation training method for power equipment maintenance operations, which aims to improve the operating skills and fault diagnosis capabilities of power equipment maintenance personnel through virtual simulation technology and intelligent feedback mechanism. This method is based on the virtual simulation system for power equipment maintenance operations described in Example 1, and combines technical features such as intelligent fault diagnosis, real-time operation feedback, and personalized training programs to provide an efficient and personalized training method, overcoming the shortcomings of traditional training methods such as high risk, high cost, lack of interaction and personalization.

[0041] Method steps, such as Figure 4 As shown: Step 1: Trainees select maintenance tasks and enter the virtual simulation environment During the training process, trainees first enter the virtual simulation environment of power equipment maintenance operations through virtual reality equipment (such as VR helmets and handles). Trainees can choose to perform specific maintenance tasks, such as maintenance of substation equipment, inspection of power lines, etc. The virtual simulation platform loads the power equipment and fault scenarios related to the selected task, and automatically adjusts the difficulty and complexity of the task according to the trainee's level.

[0042] Step 2: Real-time data collection and operation behavior monitoring After the trainee starts to perform the task, the data acquisition module collects the trainee's operation data in real time through motion capture devices and sensors. These data include information such as the trainee's operation time, operation steps, and operation accuracy. The data acquisition module not only monitors the trainee's operation behavior, but also transmits the collected operation data to the data analysis platform in real time, providing a basis for subsequent intelligent fault diagnosis and personalized feedback.

[0043] Step 3: Intelligent fault diagnosis and fault scenario generation Based on the trainee's operation performance, the intelligent fault diagnosis module dynamically generates and adjusts fault scenarios. The system automatically adjusts the fault type and difficulty based on the trainee's operation accuracy and efficiency, ensuring that each trainee can train based on their skill level. For example, if the trainee makes an operation error, the system will simulate a related fault scenario and provide specific operation suggestions. If the trainee operates properly, the system will provide positive feedback and gradually increase the difficulty of the task according to the trainee's progress.

[0044] Step 4: Real-time operational feedback and personalized recommendations When the trainee is operating, the feedback module will provide feedback based on the trainee's real-time performance. If the trainee's operation is correct, the system will give positive feedback through graphics, voice, etc.; if the trainee's operation is wrong, the system will point out the error in time and provide improvement suggestions. The feedback module automatically adjusts the content and method of feedback based on the trainee's performance to ensure that the trainee can understand the problems in the operation and correct them. The feedback provided by the system includes not only the correctness of the trainee's operation, but also the judgment and handling ability of equipment failure.

[0045] Step 5: Multi-sensory interaction and immersive experience Throughout the training process, the multi-sensory interaction module provides trainees with an immersive operating experience. The module uses tactile feedback devices to simulate the physical characteristics of the equipment, such as vibration and weight, and sound feedback devices to simulate the sound effects of the equipment when it is running. Visual enhancement devices provide special effects such as smoke and sparks when the equipment fails. The multi-sensory interaction module enhances the realism of the training, allowing trainees to complete maintenance tasks in an environment close to reality, thereby improving the practical application value of the training.

[0046] Step 6: Collaboration and team training When a maintenance task requires teamwork, trainees can use the collaborative work module to participate in the task in a virtual environment with other trainees. Multiple trainees collaborate in the same virtual scene and complete the maintenance task through real-time operation synchronization and communication interaction. This virtual collaborative training helps trainees improve their teamwork and coordination skills and simulates teamwork tasks in an actual work environment.

[0047] Step 7: Data analysis and generation of personalized training plans After the trainees complete the training tasks, the data analysis platform will analyze the trainees' operation data and generate a detailed learning report. The report includes various indicators such as the trainees' operation performance, fault diagnosis ability, processing speed, etc., and provides personalized training plans based on the analysis results. The data analysis platform adjusts the subsequent training content and difficulty by comparing the trainees' current performance with the target level to ensure that the trainees can receive training at the pace that best suits them.

[0048] The training method of the present invention can automatically generate personalized training plans based on the trainees' operation data and performance through a data analysis platform and an intelligent fault diagnosis module, ensuring that each trainee can be trained according to his or her ability level. Trainees operate in a virtual environment, free from the limitations of actual equipment and on-site conditions, avoiding the high risks and high costs of traditional training. The system can provide feedback based on the trainees' real-time operation performance and dynamically adjust the difficulty of tasks to improve the trainees' emergency handling and fault diagnosis capabilities. Through multi-sensory feedback such as touch, sound and vision, trainees can train in a more realistic virtual environment to improve the operating experience and training effect. Through the collaborative work module, trainees can conduct collaborative training with others in a virtual environment, simulate real-life team collaboration scenarios, and enhance teamwork capabilities. It meets the needs of the power industry for high-quality training, especially in the training of complex power equipment fault diagnosis and repair skills, and provides an effective solution.

[0049] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A virtual simulation system for power equipment maintenance operations, characterized in that: include: Virtual simulation platform, used to load virtual environment and maintenance tasks of power equipment; Data acquisition module, used to collect operation data in real time; Intelligent fault diagnosis module, which is used to dynamically generate fault scenarios based on operation data combined with the virtual simulation platform, and optimize fault simulation and diagnosis through machine learning; A feedback module, used to provide operation feedback in real time based on operation data; Data analysis platform, used to analyze operational data and generate personalized training programs.

2. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: The virtual simulation platform interacts with the virtual environment through a virtual reality device, and the virtual reality device includes a VR helmet and a handle.

3. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: The operation data includes operation duration, operation steps and operation accuracy.

4. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: The intelligent fault diagnosis module dynamically adjusts the type and complexity of the fault scenario according to the trainee's operating performance and provides personalized fault simulation.

5. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: The real-time operation feedback provided according to the operation data is performed in the form of images or voice.

6. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: It also includes a collaborative operation module, which is used to support collaborative operations in a virtual environment.

7. The virtual simulation system for power equipment maintenance operation according to claim 1 is characterized in that: It also includes a multi-sensory interaction module, which is used to provide tactile, sound and visual feedback to enhance the training experience.

8. A virtual simulation training method for power equipment maintenance operation, characterized in that: The following steps are involved: Load the maintenance task and enter the virtual simulation environment; Real-time monitoring and collection of operation data; Dynamically generate fault scenarios based on operational data and optimize fault simulation and diagnosis through machine learning; Provide operational feedback in real time based on operational data; Generate personalized training plans based on operational data.

9. The virtual simulation training method for power equipment maintenance operation according to claim 7 is characterized in that: The fault scenarios are dynamically generated according to the operation data, and a GAN network is used to generate the fault scenarios.

10. The virtual simulation training method for power equipment maintenance operation according to claim 7, characterized in that: The real-time operation feedback is provided according to the operation data, specifically: Provide operational feedback in the form of images, voice or text; If the operation is correct, feedback will be given through voice or graphic prompts; If an operation is incorrect, the feedback module points out the problem and provides suggestions for improvement.

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