Virtual reality-based power grid training system and method

Abstract

The invention relates to the technical field of power grid training, in particular to a power grid training system and method based on virtual reality, and the system comprises a trainee interface unit which is used for simplifying a trainee use interface and standardizing the operation logic; the training guide unit is used for providing distributed guidance for the trainees on the basis of the use interface and setting a training mode; when the power grid simulation training system is used, interface function simplification and operation high efficiency are facilitated, the cognitive burden of students is reduced, the learning effect and operation experience of power grid simulation training are improved, and the students can quickly master system functions, get familiar with application scenes of key formulas, understand complex operation processes and improve operation skills; the power grid training learning effect is enhanced, students can know the power grid state and the equipment data change in time, continuously updated simulation experience is obtained, the understanding and judgment of the students on the operation of the power grid equipment are improved, and the learning efficiency is improved and the training pertinence is enhanced by formulating a personalized learning scheme.

Description

Technical Field

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

[0002] Virtual reality is a computer technology that creates an immersive three-dimensional environment by simulating sensory experiences such as a user's vision, hearing, and touch. Power grid training based on virtual reality technology can provide systematic training on professional knowledge, skills, and safety education for employees in the power system.

[0003] The patent with the application number CN201610270318.0 records in its specification that "the present invention discloses a power grid simulation training system and method, which relates to the technical field of power systems and aims to improve the training effect of the power grid simulation training system. The power grid simulation training system includes a real-time simulation workstation, a real-time simulator, a model mapping system, and a display system; among them, the real-time simulation workstation is used to establish corresponding device calculation models according to the operation data of each device in the power grid; the real-time simulator is used to obtain the first simulation operation state result from each device calculation model; the model mapping system is used to convert the first simulation operation state result into a second simulation operation state result; the display system is used to update the corresponding device display model according to the second simulation operation state result and display the updated device display model on the graphical interface. The power grid simulation training method corresponds to the power grid simulation training system proposed in the above technical solution. The power grid simulation training system and method provided by the present invention are used for the training of dispatchers in the power system". The above technology achieves the purpose of familiarizing with the operation states of each device in the power grid and improving the power grid training effect through continuous and real-time simulation of each device in the power grid. However, the trainees need a certain amount of time to familiarize themselves with the system operation and its functions, which results in a short-term impact on the training efficiency.

[0004] In summary, researching and developing a power grid training system and method based on virtual reality is still a key problem that urgently needs to be solved in the technical field of power grid training. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art that the above technology achieves the purpose of familiarizing with the operation states of each device in the power grid and improving the power grid training effect through continuous and real-time simulation of each device in the power grid. However, the trainees need a certain amount of time to familiarize themselves with the system operation and its functions, which results in a short-term impact on the training efficiency. The present invention provides a power grid training system and method based on virtual reality.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: The present invention provides a power grid training system based on virtual reality, including: The trainee interface unit is used to simplify the trainee's user interface and standardize the operation logic; The training guidance unit provides distributed guidance for trainees based on the user interface and sets the training mode; The database unit is used to integrate video tutorials and online document libraries and call the video tutorials and online document libraries as teaching resources for the training mode; The deep learning unit displays result data, simulation effects, and operation tips after trainees operate in the training mode, constructs a virtual reality environment that supports multiple trainees to participate simultaneously, and formulates personalized learning methods for each trainee.

[0007] Furthermore, the trainee interface unit includes: The human-computer interaction system is used to create intuitive icons on the display interface, simplify the operation process and unify the operation logic, and reduce the cognitive burden of trainees; The auxiliary module is used to configure the styles of interface elements and information prompts on the user interface so that trainees can quickly locate and use the required functions; The training guidance unit includes: The step-by-step guidance module is used to provide function introductions and operation steps through pop-up windows when trainees click on the icons on the user interface; The teaching module is used to set the training mode by simulating actual task scenarios according to the function introductions and operation steps, guide trainees to practice through distributed hands-on training, and help trainees become familiar with the system and improve their operation skills.

[0008] Furthermore, the database unit includes: The data integration module is used to integrate video tutorials and online document libraries, including the key steps and demonstration scenarios of system operations, and call them as teaching resources for the training mode; The online access module allows trainees to call teaching resources at any time to enhance learning effects and self-help support; The deep learning unit includes: The feedback module is used to display result data, simulation effects, and operation tips after trainees operate in the training mode to help trainees quickly understand the impact of operations; The multi-person interaction module constructs a virtual reality environment that supports multiple trainees to participate simultaneously in the training mode and allows multiple trainees to collaborate in the same power grid scenario; The learning management system uses machine learning technology to analyze the progress and test results of each trainee in the training mode and adjusts the course difficulty and content in a personalized manner.

[0009] Furthermore, the operation processes of the human-computer interaction system and the auxiliary module include: Human-computer interaction system. After the trainee enters the virtual reality-based power grid training system, the display interface is activated. The system loads the appropriate interface settings according to the trainee's identity and displays the power balance formula of the power grid equipment model on the interface: , and the generated power is displayed at each node , load and loss distribution, helping trainees intuitively understand the power balance relationship, and the node voltage formula is displayed in real time: , where A is the voltage of node n, is the conductance matrix, and L q is the load current of node q, is the line current. Through this formula, the voltage distribution and current flow direction between nodes are displayed. The system uses intuitive icons and information prompts to show the main function modules to the trainees, simplifying the process for trainees to find and use the functions. When trainees view the status of power grid equipment, the system uses the voltage drop formula to display the voltage loss on the line: , where ΔV is the voltage loss of the line, S is the complex impedance of the line, is the line current, is the resistance, is the reactance. This formula helps trainees understand the voltage changes on different lines and their impacts. In the power grid power transmission task, the system shows the transmission efficiency formula to the trainees: , where β is the transmission efficiency, is the output power, is the input power. Through this formula, the efficiency of different line configurations of trainees is evaluated; Auxiliary module, according to the operations of trainees, provides the explanations of interface elements and the styles of information prompts in real time, guiding trainees to quickly locate and use the required functions.

[0010] Further, the operation process of the step-by-step guidance module includes: Step-by-step guidance module, entering different functions by clicking the icons on the usage interface. The system uses pop-up windows and prompts to help trainees understand the functions, and shows the core formulas related to the operation of the power grid through the formula explanation window, including but not limited to the line reactance calculation formula: , where is the admittance of the line, is the angular frequency, is the inductance, is the frequency.

[0011] Further, the operation process of the teaching module includes: Teaching module, used to assign simulation task scenarios to trainees, including but not limited to power grid equipment control and fault troubleshooting. The system records the operations of trainees in real time. At the beginning of the task, the system shows the general power flow equation operation formula to the trainees: , where is the complex power, is the voltage, is the line current, and B is the susceptance part in the admittance matrix, which helps trainees understand the power flow direction and power calculation. When a short - circuit situation occurs in the simulation, the system automatically applies the short - circuit current formula: , where is the nodal voltage, is the system equivalent impedance, and I c is the short - circuit current, showing the change of the short - circuit current. During the trainees' practice of reactive power compensation in the power grid, the system shows the reactive power formula: , where is the reactive power, is the voltage, is the reactance. This formula helps trainees understand the generation of reactive power and its importance in stabilizing the power grid. In the power grid frequency control task, the system uses the frequency change formula: , where is the frequency deviation, is the system loss power, is the inertia constant. This formula shows the impact of load mutation on frequency and helps trainees understand the principle of frequency regulation. After the trainees complete the task, the system helps trainees improve their skills through practical operation summaries and conducts repeated practice guidance on important links.

[0012] Furthermore, the operation processes of the data integration module and the online access module include: The data integration module is used to regularly extract the real - time operation data and parameters of each device from the power grid, update them to the database, and store them in the database in matrix form to support real - time power grid simulation. According to the real - time data, the system automatically calculates the power factor of the load: , where is the power factor, is the active power, is the apparent power. The harmonic components in the power grid system are shown to trainees through the harmonic distortion rate formula. P is the reactive power, and the harmonic distortion rate formula: , where is the th harmonic voltage, is the fundamental voltage, and Thd is the harmonic distortion rate. Trainees view the load distribution data through the load distribution coefficient formula. The load distribution coefficient formula: , where is the apparent power of the th node, is the distribution coefficient of this node, It is the sum of the apparent power of all nodes. Through this formula, it helps trainees analyze the load ratios of different nodes and ensures that trainees are exposed to the latest power grid status during training; Online access module: When trainees select the learning content they need on the interface, the online access module retrieves resources such as video tutorials and operation documents for trainees to learn independently. At the same time, the database unit continuously records the learning progress and operation data of trainees to support the deep learning unit for system analysis and personalized learning adjustment.

[0013] Furthermore, the operation processes of the feedback module and the multi-person interaction module include: Feedback module: It is used to display corresponding simulation results, data changes or prompts according to system settings when trainees complete operations, helping trainees immediately understand the impact of their operations on the power grid; Multi-person interaction module: It constructs a virtual reality environment that supports the simultaneous participation of multiple trainees, allowing multiple trainees to participate in operations and decision-making in the same power grid scenario simultaneously to achieve interactive learning. When multiple trainees collaborate, the system calculates the synchronous power formula of the generator: , where is the electromotive force of the generator, is the terminal voltage of the generator, is the synchronous reactance, is the load angle, is the synchronous power of the generator to show the power synchronization effect under the collaboration of multiple trainees. In the multi-trainee collaboration scenario, the system automatically displays the stability margin calculation formula: , where is the stability margin, is the maximum power that the system can transmit, is the current operating power, helping trainees determine whether the system is within the safe and stable range. After an electrical fault occurs, the system applies the electromagnetic transient formula to simulate the recovery process after the fault. The electromagnetic transient formula: , where is the time is the instantaneous current at time is the initial current, is the attenuation coefficient, is the angular frequency, is the phase angle, t is the time, helping trainees understand the dynamic behavior of power grid equipment after a fault.

[0014] Furthermore, the operation process of the learning management system includes: Learning management system: It uses machine learning technology to analyze the progress and test results of each trainee, automatically generating personalized learning plans for trainees, including but not limited to course difficulty, content adjustment, and subsequent recommended learning content. By analyzing the real-time operations of trainees and the results of the formulas used, it recommends the dynamic power formula: , where is the power at time , is the voltage, is the current, is the phase angle, guiding trainees to master the calculation method of dynamic power balance.

[0015] On the other hand, the present invention also provides a power grid training method based on virtual reality, which includes: the following steps: S1. Simplify the trainee's usage interface and standardize the operation logic; S2. Provide distributed guidance for trainees on the basis of the usage interface and set up a training mode; S3. Integrate video tutorials and an online document library and call the video tutorials and the online document library as teaching resources for the training mode; S4. After the trainees operate in the training mode, display result data, simulation effects, and operation tips, build a virtual reality environment that supports multiple trainees to participate simultaneously, and formulate personalized learning methods for each trainee; In step S1, a human-computer interaction system is used to create intuitive icons on the display interface, simplify the operation process and unify the operation logic, and reduce the cognitive burden on trainees; An auxiliary module is used to configure the styles of interface elements and information prompts on the usage interface so that trainees can quickly locate and use the required functions; In step S2, a step-by-step guidance module is used to provide function introductions and operation steps through pop-up windows when trainees click on the icons on the usage interface; A teaching module is used to simulate actual task scenarios according to the function introductions and operation steps to set up a training mode, guide trainees to practice through distributed hands-on training, and help trainees become familiar with the system and improve their operation skills.

[0016] In step S3, a data integration module is used to integrate video tutorials and an online document library, which contains key steps and demonstration scenarios of system operations, and call them as teaching resources for the training mode; Trainees can call teaching resources at any time to enhance learning effects and self-help support; In step S4, a feedback module is used to display result data, simulation effects, and operation tips after trainees operate in the training mode to help trainees quickly understand the impact of operations; A multi-person interaction module builds a virtual reality environment that supports multiple trainees to participate simultaneously in the training mode, allowing multiple trainees to collaborate in the same power grid scenario; A learning management system that uses machine learning technology to analyze the progress and test results of each trainee in the training mode and adjusts the course difficulty and content in a personalized manner.

[0017] Beneficial effects Adopting the technical solution provided by the present invention, compared with the known public technology, it has the following beneficial effects: When the present invention is in use, it is beneficial to simplify the interface function and improve the operation efficiency, reduce the cognitive burden of trainees, improve the learning effect and operation experience of power grid simulation training, help trainees quickly master the system functions, be familiar with the application scenarios of key formulas, understand complex operation processes and improve operation skills, strengthen the learning effect of power grid training, help trainees timely understand the power grid status and changes in equipment data, obtain a continuously updated simulation experience, improve trainees' understanding and judgment of the operation of power grid equipment, and improve the learning efficiency by formulating personalized learning plans and enhance the pertinence of training. Description of the drawings

[0018] Figure 1 It is a system diagram of a power grid training system and method based on virtual reality according to the present invention; Figure 2 It is a flowchart of a power grid training system and method based on virtual reality according to the present invention. Detailed implementation manners

[0019] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, 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 only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0021] The following further describes the present invention in detail with reference to the drawings: Example 1 As Figure 1 shown, the present invention provides a power grid training system based on virtual reality, including: a trainee interface unit for simplifying the trainee's use interface and standardizing the operation logic; Further, a human-computer interaction system for creating intuitive icons on the display interface, simplifying the operation process and unifying the operation logic, and reducing the cognitive burden of trainees; Further, an auxiliary module for configuring the styles of interface elements and information prompts on the use interface, enabling trainees to quickly locate and use the required functions; Further, the operation process of the trainee interface unit includes: A human-computer interaction system. After the trainee enters the power grid training system based on virtual reality, the display interface is started, and the system loads appropriate interface settings according to the trainee's identity, and displays the power balance formula of the power grid equipment model on the interface: , and the generated power , load and loss distribution at each node, helping trainees intuitively understand the power balance relationship, and displaying the node voltage formula in real time: , where A is the voltage of node n, is the conductance matrix, L q is the load current of node q, is the line current. Through this formula, the voltage distribution and current flow direction between nodes are shown. The system shows the main function modules for trainees through intuitive icons and information prompts, simplifying the process for trainees to find and use functions. When trainees view the power grid equipment status, the system uses the voltage drop formula to display the voltage loss on the line: , where ΔV is the voltage loss of the line, S is the complex impedance of the line, is the line current, is the resistance, is the reactance. This formula helps trainees understand the voltage change on different lines and its impact. In the power grid power transmission task, the system shows the transmission efficiency formula to trainees: , where β is the transmission efficiency, is the output power, is the input power. Through this formula, the efficiency of different line configurations by trainees is evaluated; An auxiliary module that provides explanations of interface elements and styles of information prompts in real time according to the operations of trainees, guiding trainees to quickly locate and use the required functions.

[0022] Specifically, after the trainee enters the virtual reality-based power grid training system, the human-computer interaction system automatically loads the interface configuration suitable for the trainee and displays the key formulas of power grid equipment on the interface, including but not limited to the power balance formula, node voltage formula, voltage drop formula, and transmission efficiency formula, and real-time displays the distribution of power, load, and voltage. Through intuitive icons and information prompts, the process of finding and using functions is simplified. At the same time, when the trainee operates, the auxiliary module will automatically provide explanations and prompts for interface elements to help the trainee quickly understand the functions and quickly locate the operations they need, which is conducive to the simplification of interface functions and the high efficiency of operations, reduces the cognitive burden of the trainee, enables the trainee to intuitively master the core formulas and parameters of power grid operation, and improves the learning effect and operation experience of power grid simulation training.

[0023] The training guidance unit provides distributed guidance for the trainee based on the usage interface and sets the training mode; Furthermore, the step-by-step guidance module is used to provide function introductions and operation steps through pop-up windows when the trainee clicks on the icons on the usage interface; Furthermore, the teaching module is used to simulate actual task scenarios according to the function introductions and operation steps to set the training mode, guide the trainee to practice through distributed hands-on training, and help the trainee become familiar with the system and improve operation skills; Furthermore, the operation process of the training guidance unit includes: The step-by-step guidance module enters different functions by clicking on the icons on the usage interface. The system uses pop-up windows and prompts to help the trainee understand the functions and displays the core formulas related to power grid operation through the formula explanation window, including but not limited to the line reactance calculation formula: , where is the admittance of the line, is the angular frequency, is the inductance, is the frequency; The teaching module is used to assign simulated task scenarios to the trainee, including but not limited to power grid equipment control and fault troubleshooting. The system records the trainee's operations in real time. At the beginning of the task, the system shows the generalized power flow equation operation formula to the trainee: , where is the complex power, is the voltage, is the line current, B is the susceptance part in the admittance matrix, which helps the trainee understand the power flow direction and power calculation. When a short-circuit situation is simulated, the system automatically applies the short-circuit current formula: , where is the node voltage, is the system equivalent impedance, I c is the short-circuit current, showing the change of the short-circuit current. During the trainee's reactive power compensation practice in the power grid, the system shows the reactive power formula: , where is reactive power, is voltage, is reactance. This formula helps trainees understand the generation of reactive power and its importance in stabilizing the power grid. In the power grid frequency control task, the system uses the frequency change formula: , where is the frequency deviation, is the system loss power, is the inertia constant. This formula demonstrates the impact of sudden load changes on frequency and helps trainees understand the principle of frequency regulation. After the trainees complete the task, the system helps them improve their skills through practical summaries and provides repeated practice guidance for important links.

[0024] Specifically, the step-by-step guidance module is automatically triggered after the trainee enters the virtual reality-based power grid training system. It gradually introduces the functions through icons and pop-up windows, and at the same time displays the above key formulas. The teaching module assigns simulated task scenarios, including but not limited to power grid control and fault troubleshooting. During the task process, the formulas are displayed to help trainees understand power calculation, reactive power compensation, and frequency regulation in complex power grid operations. In the fault troubleshooting task, the system shows the short-circuit current formula to the trainees, simulates the short-circuit situation of the real power grid and calculates the corresponding current value to help trainees master the analysis of the current impact of short-circuit faults. After the task is completed, the system summarizes the operation results and provides further practice. It is beneficial for trainees to quickly master the system functions, be familiar with the application scenarios of key formulas, understand complex operation processes, and improve operation skills through distributed guidance and practical exercises, strengthening the learning effect of power grid training.

[0025] The database unit is used to integrate video tutorials and online document libraries and call the video tutorials and online document libraries as teaching resources for the training mode; Furthermore, the data integration module is used to integrate video tutorials and online document libraries, including the key steps and demonstration scenarios of system operations, and call them as teaching resources for the training mode; Furthermore, the online access module allows trainees to call teaching resources at any time to enhance the learning effect and self-help support; Furthermore, the operation process of the database unit includes: The data integration module is used to regularly extract the real-time operation data and parameters of each device from the power grid, update them to the database, and store them in the database in matrix form to support real-time power grid simulation. According to the real-time data, the system automatically calculates the power factor of the load: , where is the power factor, is the active power, is the apparent power, which shows the harmonic components in the power grid system to the trainees through the harmonic distortion rate formula. P is the reactive power, and the harmonic distortion rate formula is: , where is the th harmonic voltage, is the fundamental voltage, Thd is the harmonic distortion rate. The trainees can view the load distribution data through the load distribution coefficient formula. The load distribution coefficient formula is: , where is the apparent power of the th node, is the distribution coefficient of this node, is the sum of the apparent powers of all nodes. Through this formula, it helps the trainees analyze the load ratios of different nodes and ensures that the trainees are exposed to the latest power grid status during the training; Online access module. When the trainees select the learning content they need on the interface, the online access module retrieves the resources of video tutorials and operation documents for the trainees to learn independently. At the same time, the database unit continuously records the learning progress and operation data of the trainees to provide support for the deep learning unit for system analysis and personalized learning adjustment.

[0026] Specifically, the power grid data is regularly extracted through the data integration module and stored in matrix form to support real-time simulation, automatically calculate the load power factor and harmonic distortion rate, and analyze the node load ratios according to the load distribution coefficient to ensure that the trainees are exposed to the latest power grid information. The online access module provides video tutorials and documents for the trainees for self-study, records the learning progress of the trainees at the same time, supports personalized learning adjustment, which is conducive to the trainees to timely understand the changes in the power grid status and equipment data, obtain a continuously updated simulation experience, and call learning resources when needed to improve the learning effect and self-learning ability.

[0027] Deep learning unit. After the trainees operate in the training mode, it displays the result data, simulation effect and operation tips, constructs a virtual reality environment that supports multiple trainees to participate simultaneously, and formulates a personalized learning method for each trainee; Furthermore, the feedback module is used to display the result data, simulation effect and operation tips after the trainees operate in the training mode to help the trainees quickly understand the impact of the operation; Furthermore, the multi-person interaction module constructs a virtual reality environment that supports multiple trainees to participate simultaneously in the training mode, allowing multiple trainees to collaborate in the same power grid scenario; Furthermore, the learning management system uses machine learning technology to analyze the progress and test results of each trainee in the training mode and adjusts the course difficulty and content in a personalized way; Furthermore, the operation process of the deep learning unit includes: A feedback module, which is used to display corresponding simulation results, data changes or prompts according to system settings when the trainee completes an operation, helping the trainee immediately understand the impact of their operation on the power grid; A multi-person interaction module, which constructs a virtual reality environment supporting the simultaneous participation of multiple trainees, allowing multiple trainees to simultaneously participate in operations and decision-making in the same power grid scenario to achieve interactive learning. When multiple trainees collaborate, the system calculates the synchronous power formula of the generator: , where is the electromotive force of the generator, is the terminal voltage of the generator, is the synchronous reactance, is the load angle, is the synchronous power of the generator, to show the power synchronization effect under the collaboration of multiple trainees. In the multi-trainee collaboration scenario, the system automatically displays the stability margin calculation formula: , where is the stability margin, is the maximum power that the system can transmit, is the current operating power, helping the trainee judge whether the system is within the safe and stable range. After an electrical fault occurs, the system applies the electromagnetic transient formula to simulate the post-fault recovery process. The electromagnetic transient formula: , where is time is the instantaneous current at time is the initial current, is the attenuation coefficient, is the angular frequency, is the phase angle, t is time, helping the trainee understand the dynamic behavior of power grid equipment after a fault; A learning management system, which uses machine learning technology to analyze the progress and test results of each trainee, and automatically generates a personalized learning plan for the trainee, including but not limited to course difficulty, content adjustment and subsequent recommended learning content. By analyzing the trainee's real-time operations and the results of the formulas used, it recommends the dynamic power formula: , where is time is the power at time is the voltage, is the current, is the phase angle, guiding the trainee to master the calculation method of dynamic power balance.

[0028] Specifically, the feedback module immediately displays data changes and simulation effects after the trainee's operation, helping the trainee understand the impact of the operation on the power grid. The multi-person interaction module creates a virtual environment that supports trainees to collaborate in the same power grid scenario. The system calculates the generator synchronous power and stability margin in real time, helping the trainees evaluate the system stability. In a fault scenario, the system applies electromagnetic transient formulas to simulate the dynamic recovery of equipment, and uses machine learning technology to analyze the trainees' progress and recommend personalized learning content, helping the trainees master the dynamic balance of the power grid, which is conducive to improving the trainees' understanding and judgment of the operation of power grid equipment. Formulating a personalized learning plan improves learning efficiency and enhances the pertinence of training.

[0029] Embodiment 2 Please refer to Figure 2 , Embodiment 2 provides a power grid simulation training method, which includes the following steps: S1. Simplify the trainee's user interface and standardize the operation logic; S2. Provide distributed guidance for the trainee based on the user interface and set the training mode; S3. Integrate video tutorials and an online document library and call the video tutorials and the online document library as teaching resources for the training mode; S4. After the trainee operates in the training mode, display the result data, simulation effect and operation prompt, build a virtual reality environment that supports multiple trainees to participate simultaneously, and formulate a personalized learning method for each trainee; In step S1, a human-computer interaction system is used to create intuitive icons on the display interface, simplify the operation process and unify the operation logic, reducing the cognitive burden on the trainee; An auxiliary module is used to configure the styles of interface elements and information prompts on the user interface, enabling the trainee to quickly locate and use the required functions; In step S2, a step-by-step guidance module is used to provide function introductions and operation steps through pop-up windows when the trainee clicks on the icons on the user interface; A teaching module is used to set the training mode by simulating actual task scenarios according to the function introductions and operation steps, guiding the trainee to practice through distributed hands-on operations, and helping the trainee become familiar with the system and improve operation skills.

[0030] In step S3, a data integration module is used to integrate video tutorials and an online document library, which contain the key steps and demonstration scenarios of system operations, and call them as teaching resources for the training mode; The trainee can call the teaching resources at any time to enhance the learning effect and self-help support; In step S4, a feedback module is used to display the result data, simulation effect and operation prompt after the trainee operates in the training mode, helping the trainee quickly understand the impact of the operation; A multi-person interaction module that constructs a virtual reality environment in the training mode to support the simultaneous participation of multiple trainees and allows multiple trainees to collaborate in the same power grid scenario; A learning management system that uses machine learning technology to analyze the progress and test results of each trainee in the training mode and adjusts the course difficulty and content in a personalized manner.

[0031] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid training system based on virtual reality, characterized in that: include: The student interface unit is used to simplify the student user interface and standardize the operation logic; A training guidance unit, which provides distributed guidance for trainees based on the user interface and sets a training mode; A database unit, used to integrate the video tutorials and the online document library, and call the video tutorials and the online document library as teaching resources of the training mode; In the deep learning unit, after the trainees operate in the training mode, the result data, simulation effects and operation prompts are displayed, and a virtual reality environment that supports the simultaneous participation of multiple trainees is built, and personalized learning methods are developed for each trainee.

2. A virtual reality-based power grid training system according to claim 1, characterized in that: The student interface unit comprises: Human-computer interaction system, used to create intuitive icons on the display interface, simplify the operation process and unify the operation logic, and reduce the cognitive burden of students; An auxiliary module, used to configure the styles of interface elements and information prompts on the user interface, so that trainees can quickly locate and use required functions; The training induction unit includes: A step-by-step guidance module is used to provide function introduction and operation steps through a pop-up window when the trainee clicks an icon on the user interface; The teaching module is used to simulate the actual task scenario to set up the training mode according to the function introduction and operation steps, and guide the trainees to practice the system through distributed guidance, so as to improve their operation skills.

3. A virtual reality-based power grid training system according to claim 1, characterized in that: The database unit includes: A data integration module, used to integrate video tutorials and online document libraries, including key steps and demonstration scenarios of system operation, to be called as teaching resources for the training mode; Online access modules allow students to access teaching resources at any time to enhance learning effects and self-help support; The Deep Learning module includes: A feedback module is used to display result data, simulation effects and operation prompts after the trainees operate in the training mode, so as to help the trainees quickly understand the impact of the operation; A multi-person interaction module, which constructs a virtual reality environment that supports the simultaneous participation of multiple trainees in the training mode, allowing multiple trainees to collaborate in the same power grid scenario; The learning management system uses machine learning technology to analyze the progress and test results of each student in the training model and adjust the course difficulty and content in a personalized manner.

4. A virtual reality-based power grid training system according to claim 2, characterized in that: The operation process of the human-computer interaction system and the auxiliary module includes: Human-computer interaction system, after the trainee enters the virtual reality-based power grid training system, the display interface starts, the system loads the appropriate interface settings according to the trainee's identity, and displays the power balance formula of the power grid equipment model on the interface: , display the generated power at each node ,load and loss The distribution of nodes helps students intuitively understand the power balance relationship and displays the node voltage formula in real time: , where A is the voltage at node n, is the conductivity matrix, L q is the load current at node q, It is the line current. This formula is used to show the voltage distribution and current flow between nodes. The system uses intuitive icons and information prompts to show the main functional modules to students, simplifying the process of students finding and using functions. When students check the status of power grid equipment, the system uses the voltage drop formula to show the voltage loss on the line: , where ΔV is the voltage loss of the line, S is the complex impedance of the line, is the line current, is the resistor, is the reactance. This formula helps students understand the change of voltage on different lines and its impact. In the power transmission task of the power grid, the system shows students the transmission efficiency formula: , where β is the transmission efficiency, is the output power, is the input power, and this formula is used to evaluate the efficiency of different line configurations of students; The auxiliary module provides real-time explanations of interface elements and information prompt styles based on the trainees’ operations, guiding trainees to quickly locate and use the required functions.

5. A virtual reality-based power grid training system according to claim 2, characterized in that: The operation process of the step-by-step guidance module includes: Step-by-step guidance module, by clicking the icons on the user interface to enter different functions, the system uses pop-up windows and prompts to help students understand the functions, and displays the core formulas related to power grid operation through the formula explanation window, including but not limited to the line reactance calculation formula: ,in is the line admittance, is the angular frequency, is the inductor, It's the frequency.

6. A virtual reality-based power grid training system and method according to claim 5, characterized in that: The operation process of the teaching module includes: The teaching module is used to assign simulated task scenarios to students, including but not limited to power grid equipment control and troubleshooting. The system records student operations in real time. At the beginning of the task, the system shows the students the generalized power flow equation operation formula: ,in is the complex power, is the voltage, is the line current, and B is the susceptance part of the admittance matrix, which helps students understand the flow direction and power calculation. When a short circuit occurs in the simulation, the system automatically applies the short-circuit current formula: ,in is the node voltage, is the system equivalent impedance, I c It is the short-circuit current. It shows the change of short-circuit current. In the reactive power compensation exercise of the power grid, the students systematically show the reactive power formula: ,in is the reactive power, is the voltage, is the reactance. This formula helps students understand the generation of reactive power and its importance in stabilizing the grid. In the grid frequency control task, the system uses the frequency change formula: ,in is the frequency offset, is the system loss power, is the inertia constant. This formula shows the impact of load mutation on frequency, which helps students understand the principle of frequency regulation. After completing the task, the system helps students improve their skills through practical summary and provides repeated practice guidance on important links.

7. A virtual reality-based power grid training system according to claim 6, characterized in that: The operation process of the data integration module and the online access module includes: The data integration module is used to regularly extract the real-time operating data and parameters of each device from the power grid, update them to the database, and store them in the database in a matrix form to support real-time power grid simulation. Based on the real-time data, the system automatically calculates the power factor of the load: ,in is the power factor, is the active power, is the apparent power. The harmonic distortion rate formula is used to show students the harmonic components in the power grid system. P is the reactive power. The harmonic distortion rate formula is: ,in It is Subharmonic voltage, is the fundamental voltage, Thd is the harmonic distortion rate, and students can view the load distribution data through the load distribution coefficient formula. The load distribution coefficient formula is: ,in It is The apparent power of each node, is the allocation coefficient of the node, It is the sum of the apparent power of all nodes. This formula helps trainees analyze the load ratio of different nodes and ensures that trainees are exposed to the latest power grid status during training. Online access module: When students select the required learning content on the interface, the online access module retrieves resources such as video tutorials and operation documents for students to learn by themselves. At the same time, the database unit continuously records the students' learning progress and operation data, providing support for the deep learning unit for system analysis and personalized learning adjustments.

8. A virtual reality-based power grid training system according to claim 7, characterized in that: The operation process of the feedback module and the multi-person interaction module includes: Feedback module, which is used to display corresponding simulation results, data changes or prompts according to system settings when trainees complete operations, helping trainees to instantly understand the impact of their operations on the power grid; The multi-person interactive module builds a virtual reality environment that supports the simultaneous participation of multiple students, allowing multiple students to participate in operations and decision-making in the same power grid scenario at the same time, realizing interactive learning. When multiple people collaborate, the system calculates the synchronous power formula of the generator: ,in is the electromotive force of the generator, is the generator terminal voltage, is the synchronous reactance, is the load angle, is the synchronous power of the generator to demonstrate the power synchronization effect under the collaboration of multiple students. In the multi-student collaboration scenario, the system automatically displays the stability margin calculation formula: ,in is the stability margin, is the maximum transmittable power of the system, It is the current operating power, which helps students determine whether the system is within a safe and stable range. After an electrical fault occurs, the system applies the electromagnetic transient formula to simulate the post-fault recovery process. The electromagnetic transient formula is: ,in It's time The instantaneous current at is the initial current, is the attenuation coefficient, is the angular frequency, is the phase angle and t is the time, which helps students understand the dynamic behavior of power grid equipment after a fault.

9. A virtual reality-based power grid training system according to claim 8, characterized in that: The operation process of the learning management system includes: The learning management system uses machine learning technology to analyze the progress and test results of each student, and automatically generates personalized learning plans for students, including but not limited to course difficulty, content adjustment, and subsequent recommended learning content. By analyzing the real-time operations and formula results used by students, dynamic power formulas are recommended: ,in It's time The power when is the voltage, is the current, It is the phase angle, guiding students to master the calculation method of dynamic power balance.

10. A virtual reality-based power grid training method, based on a virtual reality-based power grid training system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Simplify the user interface for students and standardize the operation logic; S2. providing distributed guidance for trainees based on the user interface and setting a training mode; S3, used to integrate video tutorials and online document libraries, and call the video tutorials and online document libraries as teaching resources for the training mode; S4. After the trainees operate in the training mode, the result data, simulation effects and operation prompts are displayed, and a virtual reality environment supporting the simultaneous participation of multiple trainees is constructed, and a personalized learning method is formulated for each trainee; In step S1, the human-computer interaction system is used to create intuitive icons on the display interface, simplify the operation process and unify the operation logic, and reduce the cognitive burden of the trainees; An auxiliary module, used to configure the styles of interface elements and information prompts on the user interface, so that trainees can quickly locate and use required functions; In step S2, a step-by-step guidance module is used to provide function introduction and operation steps through a pop-up window when the trainee clicks an icon on the user interface; The teaching module is used to simulate the actual task scenario to set up the training mode according to the function introduction and operation steps, and guide the trainees to practice the system through distributed guidance, so as to improve their operation skills. In step S3, a data integration module is used to integrate video tutorials and online document libraries, including key steps and demonstration scenarios of system operation, and call them as teaching resources for the training mode; Students can call on teaching resources at any time to enhance learning effects and self-help support; In step S4, a feedback module is used to display result data, simulation effects and operation prompts after the trainee performs an operation in the training mode, so as to help the trainee quickly understand the impact of the operation; A multi-person interaction module, which constructs a virtual reality environment that supports the simultaneous participation of multiple trainees in the training mode, allowing multiple trainees to collaborate in the same power grid scenario; The learning management system uses machine learning technology to analyze the progress and test results of each student in the training model and adjust the course difficulty and content in a personalized manner.

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