Virtual reality training method and device for distribution network operation without power failure

Abstract

The invention discloses a virtual reality training method and device for distribution network non-power-cut operation. The method comprises the following steps: acquiring historical training information of a training object; querying a preset case library to obtain a target case, and modifying operation parameters of the target case to generate a training case; in response to a user action triggered by the training object on the target interface, generating a target training scene corresponding to the training case; creating a target three-dimensional virtual scene, and collecting real-time interaction operation of the training object; and generating a corresponding comprehensive evaluation result according to the user action and the real-time interaction operation. According to the invention, the case library is set to integrate various power-off-free operation scenes needing training, and the number of training cases can be expanded by modifying operation parameters, so that the training range and content are effectively enriched; and meanwhile, whether the user is familiar with the application range of different operation scenes or not and the operation proficiency of each application scene are measured by evaluating user actions and real-time interaction operation, so that the safety in the production operation process is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of non-stop operation of distribution network, and in particular to a virtual reality training method and device for non-stop operation of distribution network. Background Art

[0002] With the rapid expansion of power grids and increasingly stringent demands for power supply reliability, the power grid sector has vigorously promoted the application of distribution network non-stop operation technologies in recent years. Due to the spatial limitations of distribution network lines and equipment, the safety issues potentially arising from non-stop operations are receiving increasing attention from production departments. Complex distribution network non-stop operations are characterized by complex workflows, high safety risks, and high labor intensity. Consequently, power supply companies at all levels currently conduct relatively few such operations, and teams without experience in these operations have even fewer opportunities for on-site practical training. Deepening the application of complex live-line operation technologies requires not only a proficient grasp of relevant theoretical knowledge but also a high level of practical application and operational proficiency. Currently, due to limitations in technical equipment and training venues, most non-stop operation personnel struggle to gain intuitive and effective training experience in complex non-stop operations. This is particularly true given the difficulty in conducting effective pre-training and simulation drills before actually conducting these operations.

[0003] The existing technology also provides some simulation systems or assessment systems for distribution network non-stop power operations, such as patent applications CN201911214316X and CN2022109603988, but these simulation systems can only train limited items and are all pre-set. In fact, with the promotion and application of non-stop power operations, the types and scenarios of non-stop power operations are becoming more and more abundant, and the training effects of existing simulation systems cannot meet the actual application requirements. Summary of the Invention

[0004] The present invention provides a virtual reality training method and device for non-stop operation of distribution network, which solves the above-mentioned technical problems.

[0005] A first aspect of an embodiment of the present invention provides a virtual reality training method for non-stop operation of a distribution network, comprising the following steps: Step 1: Obtain the historical training information of the training object; Step 2: querying a preset case library, obtaining a target case based on the historical training information, and modifying the operation parameters of the target case to generate a training case; Step 3: generating target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; Step 4: creating a target three-dimensional virtual scene according to the target training scene information, and collecting the real-time interactive operations of the training subject in the target three-dimensional virtual scene; Step 5: Generate a corresponding comprehensive evaluation result based on the user action and the real-time interactive operation. The comprehensive evaluation result includes a scene selection score, an operation score, a problem analysis, and improvement suggestions.

[0006] A second aspect of an embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the virtual reality training method for non-stop power supply operation of a distribution network described above is implemented.

[0007] A third aspect of an embodiment of the present invention provides a virtual reality training device for distribution network non-stop power operation, including a computer-readable storage medium and a processor. When the processor executes the computer program on the computer-readable storage medium, it implements the steps of the above-mentioned virtual reality training method for distribution network non-stop power operation.

[0008] A fourth aspect of the embodiments of the present invention provides a virtual reality training device for non-stop operation of a distribution network, comprising an acquisition module, a training case generation module, a training scenario generation module, a virtual reality module, and an evaluation module. The acquisition module is used to acquire historical training information of the training object; The training case generation module is used to query a preset case library, obtain a target case based on the historical training information, and modify the operation parameters of the target case to generate a training case; The training scenario generation module is used to generate target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; The virtual reality module is used to create a target three-dimensional virtual scene according to the target training scene information, and collect the real-time interactive operations of the training subject in the target three-dimensional virtual scene; The evaluation module is used to generate corresponding comprehensive evaluation results according to the user actions and the real-time interactive operations. The comprehensive evaluation results include scene selection scores, operation scores, problem analysis and improvement suggestions.

[0009] The beneficial effects of the present invention are as follows: the present invention provides a virtual reality training method and device for non-stop power supply operation of distribution network, which integrates a variety of non-stop power supply operation scenarios that need to be trained by setting up a case library, and can expand the number of training cases by modifying the operation parameters, thereby effectively enriching the scope and content of training; at the same time, by evaluating user actions and real-time interactive operations, it measures whether the user is familiar with the application scope of different operation scenarios and the operation proficiency of each application scenario, thereby effectively improving the safety of the production operation process.

[0010] In order to make the above-mentioned objects, features and advantages of the invention more obvious and easy to understand, preferred embodiments of the present invention are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of the virtual reality training method for non-stop power distribution network operation provided in Example 1; Figure 2 This is a structural diagram of a virtual reality training device for distribution network non-stop operation provided in Example 2; Figure 3 This is a structural diagram of a virtual reality training device for non-stop operation of a distribution network provided in Example 3. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] It should be noted that, unless there is a conflict, the various features of the embodiments of the present invention may be combined with each other and are all within the scope of protection of the present invention. In addition, although the functional modules are divided in the device schematics and the logical order is shown in the flow charts, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flow charts. Furthermore, the terms "first," "second," "third," etc. used in the present invention do not limit the data or execution order, but only distinguish between identical or similar items with substantially the same functions and effects.

[0015] Figure 1 This is a flow chart of a virtual reality training method for non-stop operation of a distribution network provided in Example 1. Figure 1 As shown, the following steps are included: Step 1: Obtain the historical training information of the training object; Step 2: querying a preset case library, obtaining a target case based on the historical training information, and modifying the operation parameters of the target case to generate a training case; Step 3: generating target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; Step 4: creating a target three-dimensional virtual scene according to the target training scene information, and collecting the real-time interactive operations of the training subject in the target three-dimensional virtual scene; Step 5: Generate a corresponding comprehensive evaluation result based on the user action and the real-time interactive operation. The comprehensive evaluation result includes a scene selection score, an operation score, a problem analysis, and improvement suggestions.

[0016] The above embodiment provides a virtual reality training method for non-stop power supply operations in distribution networks. By setting up a case library, a variety of non-stop power supply operation scenarios that need to be trained are integrated, and the number of training cases can be expanded by modifying the operation parameters, thereby effectively enriching the scope and content of training. At the same time, by evaluating user actions and real-time interactive operations, it measures whether the user is familiar with the application scope of different operation scenarios and the operational proficiency of each application scenario, thereby effectively improving the safety of the production operation process.

[0017] Each step of the above method is described in detail below using specific embodiments.

[0018] It is understandable that, in a specific embodiment, the historical training information mainly refers to the information recorded in the historical training records of the trainee, including historical operation scenarios and the number of trainings for each historical operation scenario, independent operation scores and / or collaborative operation scores. The independent operation score here is mainly the scene selection score when the trainee operates alone and the operation proficiency score of the entire process. The collaborative operation score mainly refers to the cooperative operation score of the trainee and other operators. For example, when multiple trainees need to cooperate in an operation in a virtual reality scene, the communication time, task division, interactive operation process, interactive operation time and other scores of each trainee are scored.

[0019] Before executing the query step in step 2, a case library needs to be pre-built in a preferred embodiment, which specifically includes the following steps: Collect historical distribution network non-stop data; Analyzing and collating the historical distribution network non-stop power data, associating the operation parameters in the historical distribution network non-stop power data with the corresponding operation data to form at least one non-stop power operation case, and building the case library; The operation parameters include operation type, area range, season, weather and / or time period; The operation data includes real-life images of historical distribution network non-stop operations, operation scenes, the number of operators, and operation time.

[0020] It is understandable that in scenarios (operation types) such as overhead line maintenance, cable line maintenance, ring main unit maintenance, switch station modification, temporary power supply, and transformer replacement, non-stop distribution network operations are often required to ensure residents' electricity needs. In other words, this embodiment first collects non-stop operation examples for the above scenarios across different regions, seasons, weather conditions, or time periods. Operation examples with similar operation parameters are then merged and filtered, and associated with the corresponding operation data. For example, at least one selectable operation scenario is associated with each operation type under different operation parameters, thereby forming specific operation cases and building a case library.

[0021] Those skilled in the art will appreciate that the operational scenarios of this embodiment primarily include bypass operations, microgrid operations, bridging operations, short-bar insulated pole operations, mobile insulated platform operations, and medium- and low-voltage power supply vehicle access operations. Each scenario encompasses a variety of flexible operational methods. For example, bypass operations can employ various methods, including "overhead - bypass load switch (cabinet) - overhead," "standby cabinet - standby cabinet," "overhead - standby cabinet," "connecting cable - temporary bypass cabinet (K cabinet) - overhead," "standby cabinet - ring main cabinet vehicle - heightened," and "non-standby cabinet - temporary bypass cabinet (K+R cabinet) - heightened." Operators must master the workflows and applicable scenarios (work types) of these various methods and are integrated into the case library, enabling virtual reality training for operators. Furthermore, operators may encounter scenarios assisted by all-terrain lightweight live-line operation robots, which can also serve as an extension of the present invention's scenarios.

[0022] Then, step 2 is executed to query the preset case library, obtain the target case based on the historical training information, and modify the parameters of the target case to generate a training case. It is understood that in a preferred embodiment, step 2 specifically includes the following steps: Filtering, from the preset operation scenarios according to the historical training information, candidate operation scenarios whose number of training times is less than a first preset threshold or whose operation scores are less than a second preset threshold; Querying the case library to obtain at least one corresponding alternative case according to the alternative operation scenario; Acquiring current training information of the trainee, including personal information, work content, current training objectives, etc. of the trainee, thereby screening a target case from the at least one candidate case based on the current training information, wherein the target case has a corresponding target operation scenario; The operation parameters of the target case are modified to generate a training case.

[0023] The above preferred embodiment starts with a relatively small target, namely, an operational scenario, and selects operational scenarios that the trainee has not yet trained for or mastered (specifically, the operational methods within that scenario that the trainee has not yet trained for or mastered). This approach then matches the target operational scenario to a specific candidate case in the case library. Considering that the candidate case may have been used frequently in historical training, this embodiment can proactively modify the target case's operational parameters, such as time, regional scope, and seasonal weather, to generate a new training case and provide it to the trainee for training, further enriching the scope and content of the training.

[0024] In a further preferred embodiment, an early warning step is further included, specifically querying the case library to obtain a reference case most similar to the training case. If the operating scenario corresponding to the reference case is inconsistent with the target operating scenario, an early warning instruction is generated. In other words, when the operating parameters of this embodiment are changed to generate a training case, the operating scenario involved in the training case must still be consistent with the target operating scenario corresponding to the historical training information to ensure that the training objectives of the trainee are achieved.

[0025] Next, step 3 is performed. The trainee triggers a corresponding user action on the target interface, generating target training scenario information corresponding to the training case based on the user action. This step is the first assessment of the trainee, evaluating whether the trainee understands which work scenarios should be selected for the current training case, thereby assessing the trainee's understanding of the applicable scope (work type) of each work scenario. For example, in this training scenario, is the bypass work scenario more appropriate, or is the bridging work scenario more suitable? Or is the insulating short pole work scenario more suitable, or is the mobile insulating platform work scenario more suitable? It is understood that the target training scenario generated based on the user action can be consistent with the target work scenario corresponding to the training case, indicating that the trainee has a good grasp of the training scenario; or it can be inconsistent, indicating that the trainee has not mastered the applicable scope of each work scenario. The specific presentation can be based on the scenario selection score. In a preferred embodiment, if the target training scenario is inconsistent with the target work scenario, operation training can be conducted in the 3D virtual scene corresponding to the target training scenario, followed by another operation training in the 3D virtual scene corresponding to the target work scenario.

[0026] In a specific embodiment, in step 3, the target interface is a display interface, which is used to display at least one alternative training scenario corresponding to the training case; the corresponding user action is a selection action, and the target training scenario corresponding to the training case is generated according to the selection action.

[0027] In another specific embodiment, the target interface is an editing interface, the corresponding user action is a text information input action or a voice information input action, and the target training scenario corresponding to the training case is generated based on the text information or the voice information.

[0028] Then execute step 4 to create a target three-dimensional virtual scene according to the target training scene information, and collect the real-time interactive operations of the training subjects in the target three-dimensional virtual scene. The specific implementation scheme is involved in other existing documents and will not be described in detail here.

[0029] Finally, step 5 is executed to generate a corresponding comprehensive evaluation result based on the user action and the real-time interactive operation, which specifically includes the following steps: Generate a real-time operation flow for the training object and the actual operation time corresponding to each operation step according to the real-time interactive operation; comparing the target training scenario with the target work scenario to generate a scenario selection score; Comparing the real-time operation process with a preset standard operation process to generate a first operation score; Comparing the actual operation time with the standard operation time of each operation step to generate a second operation score; The first operation score and the second operation score are integrated to generate an independent operation score and / or a collaborative operation score of the training subject.

[0030] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0031] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the virtual reality training method for non-stop power supply operation of the distribution network described above is implemented.

[0032] Figure 2 This is a structural diagram of a virtual reality training device for distribution network uninterrupted operation provided by Example 2. Figure 2 As shown, it includes an acquisition module 100, a training case generation module 200, a training scene generation module 300, a virtual reality module 400 and an evaluation module 500. The acquisition module 100 is used to acquire historical training information of the training subject; The training case generation module 200 is used to query a preset case library, obtain a target case based on the historical training information, and modify the operation parameters of the target case to generate a training case; The training scenario generation module 300 is used to generate target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; The virtual reality module 400 is used to create a target three-dimensional virtual scene according to the target training scene information, and collect the real-time interactive operations of the training subject in the target three-dimensional virtual scene; The evaluation module 500 is used to generate corresponding comprehensive evaluation results according to the user actions and the real-time interactive operations. The comprehensive evaluation results include scene selection scores, operation scores, problem analysis and improvement suggestions.

[0033] The above embodiment provides a virtual reality training device for non-stop power distribution network operations. By setting up a case library, a variety of non-stop power distribution operation scenarios that need to be trained are integrated, and the number of training cases can be expanded by modifying the operation parameters, thereby effectively enriching the scope and content of training. At the same time, by evaluating user actions and real-time interactive operations, it measures whether the user is familiar with the application scope of different operation scenarios and the operational proficiency of each application scenario, thereby effectively improving the safety of the production operation process.

[0034] In a preferred embodiment, the virtual reality training device further includes a building module, and the building module specifically includes: Collection unit, used to collect historical distribution network non-stop data; The association unit is used to analyze and organize the historical distribution network non-stop power data, associate the operation parameters in the historical distribution network non-stop power data with the corresponding operation data, form at least one non-stop power operation case, and build the case library.

[0035] In a preferred embodiment, the training case generation module 200 specifically includes: a first selection unit, configured to select, from preset operation scenarios, candidate operation scenarios whose number of training times is less than a first preset threshold or whose operation scores are less than a second preset threshold based on the historical training information; A query unit, configured to query the case library and obtain at least one corresponding alternative case according to the alternative operation scenario; a second selection unit, configured to obtain current training information of the training subject, and select a target case from the at least one candidate case according to the current training information, wherein the target case has a corresponding target operation scenario; A parameter modification unit is used to modify the operation parameters of the target case to generate a training case.

[0036] In a preferred embodiment, the training case generation module 200 further includes an early warning unit, which is used to query the case library to obtain a reference case that is most similar to the training case, and generate an early warning instruction if the operation scenario corresponding to the reference case is inconsistent with the target operation scenario.

[0037] In a preferred embodiment, the evaluation module 500 specifically includes: A generating unit, configured to generate a real-time operation flow of a training subject and an actual operation time corresponding to each operation step according to the real-time interactive operation; a first comparison unit, configured to compare the target training scenario with the target work scenario to generate a scenario selection score; a second comparing unit, configured to compare the real-time operation process with a preset standard operation process to generate a first operation score; a third comparing unit, configured to compare the actual operation duration with a standard operation duration of each operation step to generate a second operation score; A scoring unit is configured to generate an independent operation score and / or a collaborative operation score for the training subject by integrating the first operation score and the second operation score.

[0038] An embodiment of the present invention also provides a virtual reality training device for distribution network non-stop power operation, including a computer-readable storage medium and a processor. When the processor executes the computer program on the computer-readable storage medium, it implements the steps of the above-mentioned virtual reality training method for distribution network non-stop power operation.

[0039] Figure 3 This is a structural diagram of a virtual reality training device for distribution network uninterrupted operation provided by Example 3 of the present invention. Figure 3 As shown, the virtual reality training device 8 for non-stop operation of the distribution network in this embodiment includes: a processor 80, a readable storage medium 81, and a computer program 82 stored in the readable storage medium 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned various method embodiments are implemented, for example Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of the modules in the above-mentioned device embodiments are realized, for example Figure 2 Functionality of the modules shown.

[0040] Exemplarily, the computer program 82 may be divided into one or more modules, which are stored in the readable storage medium 81 and executed by the processor 80 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 82 in the virtual reality training device 8 for non-stop power distribution network operation.

[0041] The virtual reality training device 8 for non-stop operation of the distribution network may include, but is not limited to, a processor 80 and a readable storage medium 81. It will be understood by those skilled in the art that Figure 3 This is merely an example of the virtual reality training device 8 for non-stop operation of the distribution network, and does not constitute a limitation on the virtual reality training device 8 for non-stop operation of the distribution network. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the virtual reality training device for non-stop operation of the distribution network may also include a power management module, an operation processing module, input and output devices, network access devices, a bus, etc.

[0042] The processor 80 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0043] The readable storage medium 81 can be an internal storage unit of the virtual reality training device 8 for non-stop power supply operation, such as the hard drive or memory of the virtual reality training device 8. The readable storage medium 81 can also be an external storage device of the virtual reality training device 8 for non-stop power supply operation, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the readable storage medium 81 can include both the internal storage unit and external storage device of the virtual reality training device 8 for non-stop power supply operation. The readable storage medium 81 is used to store the computer program and other programs and data required by the virtual reality training device 8 for non-stop power supply operation. The readable storage medium 81 can also be used to temporarily store data that has been output or is about to be output.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0045] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0046] Those skilled in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0047] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0048] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0050] The present invention is not limited to what is described in the specification and embodiments, and additional advantages and modifications will be readily apparent to those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein without departing from the spirit and scope of the general concept defined by the claims and their equivalents.

Claims

1. A virtual reality training method for non-stop power distribution network operation, characterized in that: The following steps are involved: Step 1: Obtain the historical training information of the training object; Step 2: querying a preset case library, obtaining a target case based on the historical training information, and modifying the operation parameters of the target case to generate a training case; Step 3: generating target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; Step 4: creating a target three-dimensional virtual scene according to the target training scene information, and collecting the real-time interactive operations of the training subject in the target three-dimensional virtual scene; Step 5: Generate a corresponding comprehensive evaluation result based on the user action and the real-time interactive operation. The comprehensive evaluation result includes a scene selection score, an operation score, a problem analysis, and improvement suggestions.

2. The virtual reality training method for non-stop distribution network operation according to claim 1 is characterized in that: It also includes the build steps, specifically: Collect historical distribution network non-stop data; Analyzing and collating the historical distribution network non-stop power data, associating the operation parameters in the historical distribution network non-stop power data with the corresponding operation data to form at least one non-stop power operation case, and building the case library; The operation parameters include operation type, area range, season, weather and / or time period; The operation data includes real-life images of historical distribution network non-stop operations, operation scenes, the number of operators, and operation time.

3. The virtual reality training method for distribution network non-stop operation according to claim 2 is characterized in that: The operation scenarios include bypass operation scenarios, microgrid operation scenarios, bridging operation scenarios, insulating short pole operation scenarios, mobile insulating platform operation scenarios, and medium and low voltage power supply vehicle access operation scenarios.

4. The virtual reality training method for non-stop distribution network operation according to any one of claims 1 to 3, characterized in that: The historical training information includes the historical operation scenarios of the training subject and the number of training times, independent operation scores and / or collaborative operation scores of each historical operation scenario.

5. The virtual reality training method for non-stop distribution network operation according to claim 4 is characterized in that: Querying a preset case library, obtaining a target case based on the historical training information, and modifying parameters of the target case to generate a training case specifically includes the following steps: Filtering, from the preset operation scenarios according to the historical training information, candidate operation scenarios whose number of training times is less than a first preset threshold or whose operation scores are less than a second preset threshold; Querying the case library to obtain at least one corresponding alternative case according to the alternative operation scenario; Acquiring current training information of a training subject, and screening a target case from the at least one candidate case based on the current training information, wherein the target case has a corresponding target operation scenario; The operation parameters of the target case are modified to generate a training case.

6. The virtual reality training method for non-stop distribution network operation according to claim 5 is characterized in that: The method also includes an early warning step, specifically: querying the case library to obtain a reference case that is most similar to the training case, and generating an early warning instruction if the operation scenario corresponding to the reference case is inconsistent with the target operation scenario.

7. The virtual reality training method for non-stop distribution network operation according to claim 5 is characterized in that: In step 3, the target interface is a display interface, which is used to display at least one alternative training scenario corresponding to the training case; the corresponding user action is a selection action, and the target training scenario corresponding to the training case is generated according to the selection action.

8. The virtual reality training method for non-stop distribution network operation according to claim 5 is characterized in that: In step 3, the target interface is an editing interface, the corresponding user action is a text information input action or a voice information input action, and a target training scenario corresponding to the training case is generated according to the text information or the voice information.

9. The virtual reality training method for non-stop distribution network operation according to claim 5 is characterized in that: Generating a corresponding comprehensive evaluation result according to the user action and the real-time interactive operation specifically includes the following steps: Generate a real-time operation flow for the training object and the actual operation time corresponding to each operation step according to the real-time interactive operation; comparing the target training scenario with the target work scenario to generate a scenario selection score; Comparing the real-time operation process with a preset standard operation process to generate a first operation score; Comparing the actual operation time with the standard operation time of each operation step to generate a second operation score; The first operation score and the second operation score are integrated to generate an independent operation score and / or a collaborative operation score of the training subject.

10. A virtual reality training device for distribution network non-stop operation, based on the virtual reality training method for distribution network non-stop operation according to any one of claims 1 to 9, characterized in that: It includes acquisition module, training case generation module, training scenario generation module, virtual reality module and evaluation module. The acquisition module is used to acquire historical training information of the training object; The training case generation module is used to query a preset case library, obtain a target case based on the historical training information, and modify the operation parameters of the target case to generate a training case; The training scenario generation module is used to generate target training scenario information corresponding to the training case in response to a user action triggered by the training subject on the target interface; The virtual reality module is used to create a target three-dimensional virtual scene according to the target training scene information, and collect the real-time interactive operations of the training subject in the target three-dimensional virtual scene; The evaluation module is used to generate corresponding comprehensive evaluation results according to the user actions and the real-time interactive operations. The comprehensive evaluation results include scene selection scores, operation scores, problem analysis and improvement suggestions.

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