Substation co-simulation training system construction method and system based on mixed reality

Abstract

The invention provides a mixed reality-based substation co-simulation training system construction method and system, and the method comprises the steps: determining a specific target of a training system, and inputting the specific target; analyzing the target, and making a training system construction plan based on an analysis result; creating a simulation training system virtual model of the transformer substation based on the training system construction plan, and importing the simulation training system virtual model into the mixed reality platform; checking the functions of the system, including the accuracy of the model, the fluency of interaction and the effectiveness of cooperative work, and predicting the use effect of the training system; comparing expected effects on the basis of the predicted value of the use effect of the training system, and deploying the system to an actual use environment when the expected effects are achieved; and the operation condition of the system is monitored regularly. According to the method, the training effect can be predicted in advance, so that optimization can be performed according to prediction of the training effect, and the training efficiency is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of substation training systems, and in particular to a method and system for constructing a substation collaborative simulation training system based on mixed reality, as well as a corresponding computer terminal and a computer-readable storage medium. Background Art

[0002] Substations are key facilities in the power system, used for the conversion and distribution of electric energy. They connect power plants, transmission lines and distribution networks to achieve voltage conversion, power regulation and power distribution. The design and function of substations involve many aspects, including equipment, technology and operation procedures. The main components of existing substations include: transformers, which are the most core equipment in substations and are used to change voltage levels; switchgear, which is used to control and protect the flow of current in the power system; protective devices, which monitor and protect the power system from overload, short circuit and other faults; distribution equipment, which is responsible for the distribution and regulation of electricity; control systems, which monitor and control various equipment in substations; and communication systems, which ensure information transmission between substations and other parts of the power system, including data collection, transmission and processing. Before using substations, training systems are needed to train relevant personnel. The current substation training system cannot predict the training effect in advance, and the training plan is not detailed enough to be optimized based on the prediction of the training effect. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for constructing a substation collaborative simulation training system based on mixed reality, and also provides a corresponding computer terminal and computer-readable storage medium.

[0004] According to one aspect of the present invention, a method for constructing a substation collaborative simulation training system based on mixed reality is provided, comprising:

[0005] Determine specific goals of the training system;

[0006] Analyze the specific goals and formulate a training system construction plan based on the analysis results;

[0007] According to the training system construction plan, a virtual model of the substation simulation training system is created and imported into the mixed reality platform to construct a training system;

[0008] Check the function of the training system and predict the effect of using the training system;

[0009] Based on the predicted value of the use effect, a comparison is performed with the expected effect; when the expected effect is achieved, the training system is deployed in an actual use environment;

[0010] Regularly monitor the operation of the training system and update and optimize the training system based on user feedback.

[0011] Preferably, the determining of specific objectives of the training system includes:

[0012] Obtain the strengths and weaknesses of the existing training system and identify areas for improvement;

[0013] Collect user feedback and clarify training objectives based on the collected data;

[0014] According to the training objectives, set the training system goals based on the SMART principle;

[0015] Record the determined training system goals in the system documentation, including functional goals, technical goals and time nodes, to obtain the specific goals of the training system.

[0016] Preferably, analyzing the specific goals and formulating a training system construction plan based on the analysis results includes:

[0017] Create a task list based on the specific goals;

[0018] Based on the task list, the following analysis is performed:

[0019] Obtain user skill levels and training needs based on user needs and backgrounds;

[0020] Define the functions that the training system needs to support, including virtual equipment operation, fault simulation, and safety warnings;

[0021] Collect technical specifications and functional descriptions of substation equipment;

[0022] Obtain the equipment's operating manual and troubleshooting procedures;

[0023] Based on the above analysis results, the functional requirements of the training system are obtained;

[0024] Prioritize the functional requirements of the training system;

[0025] Based on the priority calculation results, formulate a training system construction plan.

[0026] Preferably, the priority calculation of the training system function requirements includes:

[0027] The priority calculation formula is: In the formula, P is the priority of the demand, β is the importance of the demand, θ is the urgency of the demand, is the total weight.

[0028] Preferably, the formulating of a training system construction plan based on the priority calculation result includes:

[0029] Based on the size of the demand priority P value, the training system function requirements are arranged in order, and the largest P value is given priority;

[0030] Calculate the matching degree of the functional requirements of the training system;

[0031] Establish a planning model based on demand matching;

[0032] Based on the calculation results of the planning model, a training system construction plan is obtained.

[0033] Preferably, the demand matching degree is calculated using the formula: In the formula, D is the demand matching degree, F is the number of functions that meet the demand, and H is the total number of required functions.

[0034] Preferably, the planning model is established based on the demand matching degree, including:

[0035] The planning model formula is:

[0036]

[0037] In the formula, R is the actual utilization efficiency of resources, J is the actual resource usage, K is the total available resources, C is the budget control degree of the project, S is the actual expenditure, X is the budget, B is the maximization of overall benefits, D is the demand matching degree, and P is the demand priority.

[0038] Preferably, the step of creating a virtual model of the substation simulation training system and importing it into the mixed reality platform includes:

[0039] Use Blender 3D modeling tools to create basic component models, including transformers, circuit breakers, and switches;

[0040] Add model details, including screws, tags and pipes;

[0041] Apply textures and materials to reduce polygon count;

[0042] Choose a file format based on the requirements of your target mixed reality platform;

[0043] Mixed reality platform integration using Magic Leap mixed reality platform;

[0044] Create a new project using Unity, import the exported 3D model file into the development environment, and adjust the position, size, and rotation of the model;

[0045] Add interactive functions to the model, integrate simulation functions, and create a virtual model of the substation simulation training system.

[0046] Preferably, the checking of the function of the training system includes: checking the accuracy of the virtual model, the fluency of the interaction and the effectiveness of the collaborative work;

[0047] The use effect of the predicted training system includes: checking the accuracy of the virtual model, comparing the virtual model with the drawings and the real object of the actual substation, verifying the functional components of the training system, and ensuring that all functional components are correctly positioned in the virtual model.

[0048] Preferably, the flow of the interaction is checked by using a simulator to perform the following tests:

[0049] Click, drag and rotate operations;

[0050] Record response times and system latency for each interaction;

[0051] Collect feedback on the fluidity of user interactions.

[0052] Preferably, the effectiveness of the collaborative work is checked by using a simulator to perform the following operations:

[0053] Test multi-person collaboration functions in the same virtual environment, check whether multi-person operations can be synchronized and updated in real time, and monitor the completion status and efficiency of collaborative tasks.

[0054] Preferably, the use effect of the prediction training system includes:

[0055] The prediction formula for the effect of building a training system is:

[0056] Y=β0+β1X1+β2X2+…+β n X n +∈

[0057] Where Y is the predicted training effect, X1X2…X n is the independent variable that affects the training effect, β0 is the intercept, β1β2…β n is the regression coefficient and ∈ is the error term.

[0058] Preferably, the predicted value of the use effect of the training system is compared with the expected effect, and when the expected effect is achieved, the system is deployed in the actual use environment, including:

[0059] Set specific goals and expected results of the training system;

[0060] Select key performance indicators related to expected results, including test scores, operational efficiency and feedback scores;

[0061] Obtain forecast data, compare forecast results with expected effects, and calculate the deviation between forecast results and expected effects;

[0062] Determine whether the deviation meets the predetermined criteria;

[0063] If the predetermined standards are not met, the training system should be optimized;

[0064] If the predetermined standards are met, the training system will be deployed in the actual usage environment.

[0065] Preferably, the regularly monitoring the operation of the training system and updating and optimizing the training system according to user feedback includes:

[0066] Continuous monitoring and maintenance, setting up regular inspection and evaluation mechanisms, monitoring the long-term performance of the system, and continuously collecting and analyzing user feedback, system data and performance indicators;

[0067] Update the training system and optimize its functions based on the monitoring results.

[0068] According to another aspect of the present invention, a system for constructing a substation collaborative simulation training system based on mixed reality is provided, comprising:

[0069] A training system construction module, which is used to determine specific goals of the training system; analyze the specific goals and formulate a training system construction plan based on the analysis results; create a virtual model of the substation simulation training system according to the training system construction plan, and import it into the mixed reality platform to construct the training system;

[0070] A training system detection module, which is used to check the function of the training system and predict the use effect of the training system;

[0071] A training system deployment module, which compares the expected effect based on the predicted value of the use effect; when the expected effect is achieved, the training system is deployed in the actual use environment;

[0072] The training system update module is used to regularly monitor the operation of the training system and update and optimize the training system according to user feedback.

[0073] According to a third aspect of the present invention, there is provided a substation collaborative simulation training system based on mixed reality. The system is constructed using any one of the methods described above in the present invention, or the system described above in the present invention, and is used for substation collaborative simulation training.

[0074] According to a fourth aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, can be used to execute any one of the methods described above in the present invention, or to run the system described above in the present invention.

[0075] According to a fifth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the methods described above in the present invention, or to run the system described above in the present invention.

[0076] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0077] The method and system for constructing a substation collaborative simulation training system based on mixed reality provided by the present invention can predict the training effect in advance, so that optimization can be performed according to the prediction of the training effect to maximize the training efficiency;

[0078] The method and system for constructing a substation collaborative simulation training system based on mixed reality provided by the present invention calculate the priority of the training system functions based on demand analysis, and can perform efficient system function design, thereby making the construction of the training system more convenient, and can predict the training effect in advance, and then can optimize according to the prediction of the training effect, so as to maximize the training efficiency;

[0079] The substation collaborative simulation training system construction method and system based on mixed reality provided by the present invention clarify the training objectives through the SMART principle, ensure the directionality and effectiveness of system development, and enhance the pertinence of training;

[0080] The method and system for constructing a substation collaborative simulation training system based on mixed reality provided by the present invention can collect user feedback and skill evaluation, gain an in-depth understanding of the actual needs of users, ensure that the design of the training system can meet the specific requirements of different users, and thus improve the learning effect and satisfaction of users;

[0081] The method and system for constructing a substation collaborative simulation training system based on mixed reality provided by the present invention introduce demand priority calculation and demand matching degree analysis, so that the design of system functions can be data-driven, giving priority to the most important and most urgent needs, and improving resource utilization efficiency;

[0082] The method and system for constructing a substation collaborative simulation training system based on mixed reality provided by the present invention adopt a variety of testing methods to evaluate the virtual model of the training system, including the interaction fluency, collaborative work effectiveness and functional accuracy, to ensure the reliability and practicality of the system in practical applications;

[0083] The mixed reality-based substation collaborative simulation training system construction method and system provided by the present invention can establish a usage effect prediction model to evaluate and adjust the effect before the system is officially put into use, ensuring that the training system can achieve the expected effect and be continuously optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0085] Figure 1 This is a workflow diagram of a method for constructing a substation collaborative simulation training system based on mixed reality in one embodiment of the present invention.

[0086] Figure 2 It is a schematic diagram of the working contents of a method for constructing a substation collaborative simulation training system based on mixed reality in a preferred embodiment of the present invention.

[0087] Figure 3 This is a schematic diagram of the components of a system for building a substation collaborative simulation training system based on mixed reality in one embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0089] The existing substation training system cannot predict the training effect, and the training plan is not detailed enough and cannot be optimized according to the prediction of the training effect. To address this problem, an embodiment of the present invention provides a method for constructing a substation collaborative simulation training system based on mixed reality. The method calculates the priority of the training system function based on demand analysis, and can perform efficient system function design, thereby making the construction of the training system more convenient, and can predict the training effect in advance, so that it can be optimized according to the prediction of the training effect, so as to maximize the training efficiency.

[0090] Specifically, Figure 1 As shown, the method for constructing a substation collaborative simulation training system based on mixed reality provided in this embodiment may include the following operations:

[0091] S1, determine the specific goals of the training system;

[0092] S2, analyze specific goals and formulate a training system construction plan based on the analysis results;

[0093] S3, creating a virtual model of the substation simulation training system according to the training system construction plan, and importing it into the mixed reality platform to construct a training system;

[0094] S4, check the function of the training system and predict the effect of using the training system;

[0095] S5, based on the predicted value of the usage effect, compare the expected effect; when the expected effect is achieved, deploy the training system to the actual usage environment;

[0096] S6, regularly monitor the operation of the training system and update and optimize the training system based on user feedback.

[0097] The technical solutions provided by the above embodiments of the present invention are further described in detail below in conjunction with preferred embodiments.

[0098] like Figure 2 As shown, in this preferred embodiment, the method for constructing a substation collaborative simulation training system based on mixed reality provided by the above embodiment of the present invention includes the following steps:

[0099] S1, determine the specific goals of the training system and input the specific goals;

[0100] S2, analyze the goals and formulate a system construction plan based on the analysis results;

[0101] S3, according to the training system construction plan, use 3D modeling tools to create a virtual model of the substation simulation training system and import it into the mixed reality platform;

[0102] S4, check the functions of the training system, including the accuracy of the model, the fluency of interaction and the effectiveness of collaborative work, and predict the effectiveness of the training system;

[0103] S5, based on the predicted value of the use effect of the training system, compare the expected effect, and when the expected effect is achieved, deploy the system to the actual use environment;

[0104] S6, regularly monitor the system operation and optimize the system based on the feedback.

[0105] In some preferred implementations, the above S1, determining the specific goal of the training system, may further include the following operations:

[0106] S11, obtain the strengths and weaknesses of the existing training system and identify areas for improvement;

[0107] S12, collect user feedback and clarify training objectives based on the collected data;

[0108] S13, setting training system goals based on the SMART principle; further preferably, the SMART principle may also include: reducing the equipment operation error rate m (preferably 15%); after the training, the user's correct rate of system operation reaches n (preferably 90%); completing system deployment and conducting preliminary training within a set time (preferably within 6 months);

[0109] S14, record the determined goals in the system document, including functional goals, technical goals and time nodes, and obtain the specific goals of the training system.

[0110] In some preferred implementations, the above S2, analyzing the target and formulating a system construction plan based on the analysis results, may further include the following operations:

[0111] S21, establish a task list based on training objectives;

[0112] S22, based on the task list, conducts the following analysis:

[0113] S221, investigate user needs and backgrounds, and obtain user skill levels and training needs;

[0114] S222, define the functions that the system needs to support, including virtual device operation, fault simulation and safety warning;

[0115] S223, collect technical specifications and functional descriptions of substation equipment;

[0116] S224, obtaining the operation manual and troubleshooting process of the device;

[0117] S23, based on the above analysis results, obtain the functional requirements of the training system;

[0118] S24, performing priority calculation of the functional requirements of the training system; further preferably, the priority calculation formula is: In the formula, P is the priority of the demand, β is the importance of the demand, θ is the urgency of the demand, is the total weight;

[0119] S27, based on the priority calculation result, formulate a training system construction plan.

[0120] In some preferred implementations, the above S27, formulating a training system construction plan based on the priority calculation result, may further include the following operations:

[0121] S271, based on the magnitude of the demand priority P value, the training system function requirements are arranged in order, and the largest P value is processed first;

[0122] S272, calculating the demand matching degree for the functional requirements of the training system; further preferably, the demand matching degree calculation formula is: In the formula, D is the demand matching degree, F is the number of functions that meet the demand, and H is the total number of required functions;

[0123] S273, establishing a planning model based on the demand matching degree; further preferably, establishing a planning model based on the demand matching degree may also include: the planning model formula is:

[0124]

[0125] In the formula, R is the actual utilization efficiency of resources, J is the actual resource usage, K is the total available resources, C is the budget control degree of the project, S is the actual expenditure, X is the budget, B is the maximization of overall benefits, D is the demand matching degree, and P is the demand priority;

[0126] S274, obtaining a training system construction plan based on the calculation results of the planning model.

[0127] Through the above S2, the design of the virtual model of the substation and the interaction mode between the user and the training system can also be realized.

[0128] In some preferred implementations, the above S3, creating a virtual model of the substation simulation training system and importing it into the mixed reality platform, may further include the following operations:

[0129] S31, use Blender3D modeling tool to create basic component models, including transformers, circuit breakers and switches;

[0130] S32, add model details, including screws, labels and pipes;

[0131] S33, apply textures and materials, reduce polygon count;

[0132] S34, selecting a file format according to the requirements of the target mixed reality platform;

[0133] S35, mixed reality platform integration using Magic Leap mixed reality platform;

[0134] S36, create a new project using Unity, import the exported 3D model file into the development environment, and adjust the position, size, and rotation of the model;

[0135] S37, adds interactive functions to the model and integrates simulation functions.

[0136] In some preferred implementations, the above S4, checking the function of the training system and predicting the use effect of the training system, may further include the following operations:

[0137] S41, Checking the functionality of the training system, may include: checking the accuracy of the virtual model, the fluency of interaction, and the effectiveness of collaborative work;

[0138] S42, predicting the use effect of the training system, may include: checking the accuracy of the virtual model, comparing the virtual model with the drawings and the real object of the actual substation, verifying the functional components of the training system, and ensuring that all functional components are correctly positioned in the virtual model.

[0139] In some preferred implementations, the above S41 checks the flow of interaction and uses a simulator to perform the following tests:

[0140] Click, drag and rotate operations;

[0141] Record response times and system latency for each interaction;

[0142] Collect feedback on the fluidity of user interactions.

[0143] In some preferred implementations, the above S41, checking the effectiveness of the collaborative work, uses a simulator to perform the following operations:

[0144] Test multi-person collaboration functions in the same virtual environment, check whether multi-person operations can be synchronized and updated in real time, and monitor the completion status and efficiency of collaborative tasks.

[0145] In some preferred implementations, the above S42, predicting the use effect of the training system, may further include the following operations:

[0146] The prediction formula for the use effect of building a training system is:

[0147] Y=β0+β1X1+β2X2+…+β n X n +∈

[0148] Where Y is the predicted training effect, X1X2…X n is the independent variable that affects the training effect, β0 is the intercept, β1β2…β n is the regression coefficient and ∈ is the error term.

[0149] In some preferred implementations, the above S5, based on the predicted value of the use effect of the training system, performs a comparison of the expected effect, and when the expected effect is achieved, deploys the system to the actual use environment, and may further include the following operations:

[0150] Define the specific goals and expected results of the training system;

[0151] Select key performance indicators related to expected results, including test scores, operational efficiency and feedback scores;

[0152] Collect forecast data, compare the forecast value with the expected effect, and calculate the deviation between the forecast effect and the expected effect. The effect deviation is equal to the forecast effect minus the expected effect.

[0153] Compare the predicted results with the expected results to determine whether they meet the predetermined standards;

[0154] If the expected standards are not met, system optimization will be carried out;

[0155] Once the expected standards are met, the system will be deployed to the actual usage environment.

[0156] In some preferred implementations, the above S6, regularly monitoring the operation of the training system and updating and optimizing the training system according to user feedback, may further include the following operations:

[0157] Continuous monitoring and maintenance, setting up regular inspection and evaluation mechanisms, monitoring the long-term performance of the system, and continuously collecting and analyzing user feedback, system data and performance indicators;

[0158] Update the training system and optimize its functions based on the monitoring results.

[0159] Based on the same inventive concept, an embodiment of the present invention also provides a system for constructing a substation collaborative simulation training system based on mixed reality.

[0160] Specifically, Figure 3 As shown, the substation collaborative simulation training system construction system based on mixed reality provided by this embodiment may include the following modules:

[0161] A training system construction module, which is used to determine the specific goals of the training system; analyze the specific goals and formulate a training system construction plan based on the analysis results; create a virtual model of the substation simulation training system according to the training system construction plan, and import it into the mixed reality platform to construct the training system;

[0162] A training system detection module, which is used to check the functions of the training system and predict the use effect of the training system;

[0163] The training system deployment module compares the expected effect based on the predicted value of the use effect; when the expected effect is achieved, the training system is deployed to the actual use environment;

[0164] The training system update module is used to regularly monitor the operation of the training system and update and optimize the training system based on user feedback.

[0165] The following further describes the working contents of each functional module in the mixed reality-based substation collaborative simulation training system construction system provided by the above embodiment of the present invention.

[0166] Training system building blocks, whose work includes:

[0167] Determine the specific goals of the training system and input the specific goals, including: obtaining the advantages and disadvantages of existing training methods and identifying areas for improvement; collecting user feedback and clarifying training goals based on the collected data; setting system goals based on the SMART principle; the SMART principle specifically includes: reducing the equipment operation error rate by 15%; after the training, the user's accuracy rate of system operation reaches 90%; complete system deployment and conduct initial training within 6 months; record the determined goals in the system documentation, including functional goals, technical goals and time nodes.

[0168] Analyze the goals, formulate a system construction plan based on the analysis results, and design the interaction between the virtual model of the substation and the user and the system. Specifically, it includes: establishing a task list based on the training goals; investigating the needs and background of users, obtaining user skill levels and training needs; defining the functions that the system needs to support, including virtual equipment operation, fault simulation and safety warnings; collecting technical specifications and functional descriptions of substation equipment; obtaining the equipment's operating manual and fault handling process; and calculating demand priority. The priority calculation formula is: Among them, P is the priority of demand, β is the importance of demand, θ is the urgency of demand, is the total weight; based on the priority calculation results, a training system construction plan is formulated.

[0169] Based on the priority calculation results, the training system construction plan is formulated, which specifically includes: arranging the system function designs in order based on the size of the P value, giving priority to the one with the largest P value; calculating the demand matching degree of the system function; the demand matching degree calculation formula is: In the formula, D is the demand matching degree, F is the number of functions that meet the demand, and H is the total number of required functions. A planning model is established based on the demand matching degree.

[0170] The planning model established based on demand matching specifically includes: Among them, the planning model formula is:

[0171]

[0172] In the formula, R is the actual utilization efficiency of resources, J is the actual resource usage, K is the total available resources, C is the budget control degree of the project, S is the actual expenditure, X is the budget, B is the maximization of overall benefit, D is the demand matching degree, and P is the demand priority; the plan is formulated based on the calculation results of the planning model.

[0173] Use 3D modeling tools to create a virtual model of the substation simulation training system and import it into the mixed reality platform. Specifically, it includes: using Blender3D modeling tools to create basic component models, including transformers, circuit breakers and switches; adding details, including screws, labels and pipes; applying textures and materials to reduce the number of polygons; selecting file formats according to the requirements of the target mixed reality platform; using Magic Leap mixed reality platform for mixed reality platform integration; using Unity to create a new project, importing the exported 3D model file into the development environment, and adjusting the position, size and rotation of the model; adding interactive functions to the model and integrating simulation functions.

[0174] Training system detection module, its work content includes:

[0175] Check the functions of the system, including the accuracy of the model, the fluency of interaction and the effectiveness of collaborative work, and predict the effectiveness of the use of the training system, which specifically includes: checking the accuracy of the model, comparing the virtual model with the drawings and real objects of the actual substation, verifying the functional components, and ensuring that all functional components are correctly positioned in the model; using the simulator for further verification; testing the interactive functions, including click, drag and rotate operations; recording the response time and system delay of each interaction; collecting feedback on the fluency of user interaction; checking the effectiveness of collaborative work; the inspection steps are: testing the multi-person collaboration function in the same virtual environment, checking whether multi-person operations can be synchronized and updated in real time, monitoring the completion and efficiency of collaborative tasks; predicting the effectiveness of the training system.

[0176] The specific method for predicting the effect of using the training system includes: The prediction formula for the effect of using the training system is:

[0177] Y=β0+β1X1+β2X2+…+β n X n +∈

[0178] Where Y is the predicted training effect, X1X2…X n is the independent variable that affects the training effect, β0 is the intercept, β1β2…β n is the regression coefficient and ∈ is the error term.

[0179] Training system deployment module, its work content includes:

[0180] Based on the predicted value of the use effect of the training system, a comparison is made with the expected effect. When the expected effect is achieved, the system is deployed to the actual use environment. Specifically, it includes: defining the specific goals and expected effects of the training system; selecting key performance indicators related to the expected effects, including test scores, operating efficiency and feedback scores; collecting predicted data, comparing the predicted value with the expected effect, and calculating the deviation between the predicted effect and the expected effect, where the effect deviation is equal to the predicted effect minus the expected effect; comparing the predicted result with the expected effect to determine whether the predetermined standard is met; if the expected standard is not met, system optimization is performed; if the expected standard is met, the system is deployed to the actual use environment.

[0181] Training system update module, its work content includes:

[0182] Continuous monitoring and maintenance, set up regular inspection and evaluation mechanisms, monitor the long-term performance of the system, continuously collect and analyze user feedback, system data and performance indicators; update the system and improve functions based on the monitoring results.

[0183] It should be noted that the steps in the method provided by the present invention can be implemented by using the corresponding components in the system, and those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, and can also refer to the technical solution of the method to implement the composition of the system, that is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, which will not be elaborated here.

[0184] Based on the same inventive concept, an embodiment of the present invention also provides a substation collaborative simulation training system based on mixed reality. The system is constructed using any method of the above embodiments of the present invention, or any system of the above embodiments of the present invention, and is used for substation collaborative simulation training.

[0185] An embodiment of the present invention further provides a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any method of the above-mentioned embodiments of the present invention, or to run any system of the above-mentioned embodiments of the present invention.

[0186] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0187] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0188] The processor is used to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0189] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0190] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0191] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0192] The mixed reality-based substation collaborative simulation training system construction method and system provided in the above-mentioned embodiments of the present invention can predict the training effect in advance, so that optimization can be performed according to the prediction of the training effect to maximize the training efficiency; the priority of the training system function is calculated based on the demand analysis, and efficient system function design can be performed, thereby making the construction of the training system more convenient, and the training effect can be predicted in advance, so that optimization can be performed according to the prediction of the training effect to maximize the training efficiency.

[0193] All matters not covered in the above embodiments of the present invention are well known in the art.

[0194] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for constructing a substation collaborative simulation training system based on mixed reality, characterized in that: include: Determine specific goals of the training system; Analyze the specific goals and formulate a training system construction plan based on the analysis results; According to the training system construction plan, a virtual model of the substation simulation training system is created and imported into the mixed reality platform to construct a training system; Check the function of the training system and predict the effect of using the training system; Based on the predicted value of the use effect, a comparison is performed with the expected effect; when the expected effect is achieved, the training system is deployed in an actual use environment; Regularly monitor the operation of the training system and update and optimize the training system based on user feedback.

2. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 1, characterized in that: The specific objectives of the training system are as follows: Obtain the strengths and weaknesses of the existing training system and identify areas for improvement; Collect user feedback and clarify training objectives based on the collected data; According to the training objectives, set the training system goals based on the SMART principle; Record the determined training system goals in the system documentation, including functional goals, technical goals and time nodes, to obtain the specific goals of the training system.

3. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 1, characterized in that: The specific objectives are analyzed, and a training system construction plan is formulated based on the analysis results, including: Create a task list based on the specific goals; Based on the task list, the following analysis is performed: Obtain user skill levels and training needs based on user needs and backgrounds; Define the functions that the training system needs to support, including virtual equipment operation, fault simulation, and safety warnings; Collect technical specifications and functional descriptions of substation equipment; Obtain the equipment's operating manual and troubleshooting procedures; Based on the above analysis results, the functional requirements of the training system are obtained; Prioritize the functional requirements of the training system; Based on the priority calculation results, formulate a training system construction plan.

4. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 3 is characterized in that: Also includes any one or more of the following: - The prioritization of the training system functional requirements includes: The priority calculation formula is: In the formula, P is the priority of the demand, β is the importance of the demand, θ is the urgency of the demand, is the total weight; - Based on the priority calculation results, a training system construction plan is formulated, including: Based on the size of the demand priority P value, the training system function requirements are arranged in order, and the largest P value is given priority; Calculate the matching degree of the functional requirements of the training system; Establish a planning model based on demand matching; Based on the calculation results of the planning model, a training system construction plan is obtained; - The demand matching degree calculation formula is: In the formula, D is the demand matching degree, F is the number of functions that meet the demand, and H is the total number of required functions; - The planning model established based on demand matching includes: The planning model formula is: In the formula, R is the actual utilization efficiency of resources, J is the actual resource usage, K is the total available resources, C is the budget control degree of the project, S is the actual expenditure, X is the budget, B is the maximization of overall benefits, D is the demand matching degree, and P is the demand priority.

5. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 1, characterized in that: The step of creating a virtual model of the substation simulation training system and importing it into the mixed reality platform includes: Use Blender 3D modeling tools to create basic component models, including transformers, circuit breakers, and switches; Add model details, including screws, tags and pipes; Apply textures and materials to reduce polygon count; Choose a file format based on the requirements of your target mixed reality platform; Mixed reality platform integration using Magic Leap mixed reality platform; Create a new project using Unity, import the exported 3D model file into the development environment, and adjust the position, size, and rotation of the model; Add interactive functions to the model, integrate simulation functions, and create a virtual model of the substation simulation training system.

6. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 1, characterized in that: The checking of the function of the training system includes: checking the accuracy of the virtual model, the fluency of interaction and the effectiveness of collaborative work; The use effect of the predicted training system includes: checking the accuracy of the virtual model, comparing the virtual model with the drawings and the real object of the actual substation, verifying the functional components of the training system, and ensuring that all functional components are correctly positioned in the virtual model.

7. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 6, characterized in that: Also includes any one or more of the following: - Check the flow of the interaction and use the simulator to perform the following tests: Click, drag and rotate operations; Record response times and system latency for each interaction; Collect feedback on the smoothness of user interactions; - Check the effectiveness of the collaboration by using the simulator to: Test the multi-person collaboration function in the same virtual environment, check whether multi-person operations can be synchronized and updated in real time, and monitor the completion and efficiency of collaborative tasks; -The use effect of the prediction training system includes: The prediction formula for the effect of building a training system is: Y=β0+β1X1+β2X2+…+β n X n +∈ Where Y is the predicted training effect, X1X2…X n is the independent variable that affects the training effect, β0 is the intercept, β1β2…β n is the regression coefficient and ∈ is the error term.

8. The method for constructing a substation collaborative simulation training system based on mixed reality according to claim 1, characterized in that: Also includes any one or more of the following: - The predicted value of the effect of using the training system is compared with the expected effect. When the expected effect is achieved, the system is deployed in the actual use environment, including: Set specific goals and expected results of the training system; Select key performance indicators related to expected results, including test scores, operational efficiency and feedback scores; Obtain forecast data, compare forecast results with expected effects, and calculate the deviation between forecast results and expected effects; Determine whether the deviation meets the predetermined criteria; If the predetermined standards are not met, the training system should be optimized; If the predetermined standards are met, the training system will be deployed to the actual usage environment; -Regularly monitoring the operation of the training system and updating and optimizing the training system based on user feedback, including: Continuous monitoring and maintenance, setting up regular inspection and evaluation mechanisms, monitoring the long-term performance of the system, and continuously collecting and analyzing user feedback, system data and performance indicators; Update the training system and optimize its functions based on the monitoring results.

9. A system for constructing a substation collaborative simulation training system based on mixed reality, characterized in that: include: A training system building module, which is used to determine the specific goals of the training system; Analyze the specific goals and formulate a training system construction plan based on the analysis results; create a virtual model of the substation simulation training system according to the training system construction plan, and import it into the mixed reality platform to construct the training system; A training system detection module, which is used to check the function of the training system and predict the use effect of the training system; A training system deployment module, which compares the expected effect based on the predicted value of the use effect; when the expected effect is achieved, the training system is deployed in the actual use environment; The training system update module is used to regularly monitor the operation of the training system and update and optimize the training system according to user feedback.

10. A substation collaborative simulation training system based on mixed reality, characterized in that: The system is constructed using the method described in any one of claims 1 to 8, or the system described in claim 9, and is used for substation collaborative simulation training.

11. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it can be used to perform the method described in any one of claims 1 to 8, or to run the system described in claim 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method described in any one of claims 1 to 8, or to run the system described in claim 9.

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