Primary library station VR practical training management system and method, and storage medium

Abstract

The invention belongs to the technical field of grassroots library station management, and particularly relates to a grassroots library station VR practical training management system and method and a storage medium, and the system comprises a three-dimensional modeling unit, a virtual scene simulation construction unit, a VR equipment access unit, a practical training login selection unit and an output display unit. According to the invention, employees can rapidly learn equipment operation and accident processing in a short time through the virtual reality technology, zero-risk emergency processing training is realized, the training safety is greatly enhanced, the dependence of enterprises on field training is reduced, the costs of training sites, equipment maintenance and the like are reduced, and the training efficiency is improved. The operation skill and the emergency processing capability of employees are improved, the operation safety of a base library station is ensured, all devices involved in the VR device access unit are subjected to device cooperation evaluation analysis before the VR device is accessed, corresponding devices are replaced and adjusted when an immersive high-influence signal is generated, and the operation safety of the VR device access unit is improved. And the immersive effect and the experience stability of the practical training experience of the basic library station are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of grass-roots depot management, and specifically to a grass-roots depot VR training management system, method and storage medium. Background Art

[0002] Grass-roots depots refer to warehouses and stations established to meet the needs of grass-roots units or production frontlines for material supply, storage, management, and emergency response. They are usually located at or near the production site to facilitate the timely and efficient provision of required materials and services. In the general oil and gas gathering and transportation factory, grass-roots stations are the responsible entities for oil and gas storage and sales, and are responsible for the value accounting of crude oil transportation volume and light hydrocarbon production; With the rapid development of the petrochemical industry, the operational complexity and safety risks of grass-roots depots are increasing continuously. The traditional training methods for grass-roots depots mainly use on-site training and classroom teaching, which have problems such as high costs, high risks, and limited training effects, and cannot meet the requirements of modern oil depot processes and safe production, and are not conducive to ensuring the operational safety of grass-roots depots; In view of the above technical deficiencies, a solution is now proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide a grass-roots depot VR training management system, method and storage medium, which solves the problems that the existing technology mainly uses on-site training and classroom teaching methods to train grass-roots depot personnel, with high costs, high risks, and limited training effects, and cannot meet the requirements of modern oil depot processes and safe production, and is not conducive to ensuring the operational safety of grass-roots depots.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: A grass-roots depot VR training management system includes a 3D modeling unit, a virtual scene simulation construction unit, a VR device access unit, a training login selection unit, and an output display unit; the 3D modeling unit uses 3ds Max to perform high-precision modeling on gas stations and oil depots to ensure the real restoration of the operation scene, and exports FBX format files for use in Unity3D after the modeling is completed; the virtual scene simulation construction unit imports the models in Unity3D, creates environmental elements to enhance the scene realism, and builds two virtual scenes of a comprehensive energy supply gas station and a coastal oil depot; The VR device access unit is used to access VR devices. Users can roam through the first-person perspective, operate the devices, and handle accidents, enhancing the immersive learning experience; the training login selection unit is used for user login and user authentication. After the authentication is completed, the user enters the selection process, selects to enter the teaching version module or the assessment version module, and starts the training after the selection is completed; the output display unit displays the user's training information and VR device access information.

[0005] Furthermore, the integrated energy supply gas station simulated by the virtual scenario simulation construction unit simulates the emergency disposal of oil dispenser overflow, the emergency disposal of oil spill and fire during oil unloading at the energy supply station, the six-step method of refueling service, the eight-step method of underground tank handover during oil unloading at the energy supply station, and the inspection content during the shift change at the energy supply station; The coastal oil depot scenario constructed by the virtual scenario simulation construction unit focuses on simulating the comprehensive emergency drill for fire caused by leakage of loading arm in the oil loading area, the comprehensive emergency drill for fire caused by leakage of tank root valve, and the inspection of the coastal oil depot.

[0006] Furthermore, the teaching version module is used for basic training, suitable for new employees and operators, providing step-by-step operation guidelines to learn the operation process, equipment operation methods, and safety regulations; The assessment version module is used to assess the operation ability and emergency handling level of employees, automatically record the operation process and generate an assessment report, provide an operation score through data analysis, and give improvement suggestions to help the enterprise conduct a comprehensive evaluation of the training effect of employees.

[0007] Furthermore, the output display unit is communicatively connected to the immersive impact analysis unit. The immersive impact analysis unit conducts an evaluation and analysis of the equipment cooperation of all the equipment involved in the VR device access unit, generates an immersive high-impact signal or an immersive low-impact signal through the analysis, and sends the immersive high-impact signal or the immersive low-impact signal to the output display unit for display.

[0008] Furthermore, the specific analysis process of the equipment cooperation assessment and analysis is as follows: All the equipment involved in the VR device access unit is obtained, and the corresponding equipment is marked as i, where i is a natural number greater than 1; the production date of equipment i is collected, the time interval between the current date and the production date is marked as the production time collection value, and the total usage duration of equipment i in the historical stage is marked as the usage time collection value. The production time collection value and the usage time collection value are respectively compared numerically with the corresponding preset production time collection threshold and preset usage time collection threshold. If the production time collection value or the usage time collection value exceeds the corresponding preset threshold, then equipment i is marked as a reverse device; If both the production time collection value and the usage time collection value do not exceed the corresponding preset thresholds, then with the current moment as the ending moment, a tracking period with a set duration of L1 is traced back. The ratio of the number of times equipment i fails during operation within the tracking period to the operation duration of equipment i within the tracking period is calculated to obtain the tracking anomaly value. The tracking anomaly value, the production time collection value, and the usage time collection value are weighted and summed to calculate the equipment collection value. The equipment collection value is compared numerically with the corresponding preset equipment collection threshold. If the equipment collection value exceeds the preset equipment collection threshold, then equipment i is marked as a reverse device; if there is a reverse device among the equipment involved in the VR device access unit, an immersive high-impact signal is generated.

[0009] Further, if there is no reverse device among the devices involved in the VR device access unit, calculate the ratio of the device acquisition value of device i to the corresponding preset device acquisition threshold, thereby obtaining the device occupation ratio value. Calculate the average value of the device occupation ratio values of all devices involved in the VR device access unit to obtain the immersive matching coefficient, and compare the immersive matching coefficient with the preset immersive matching coefficient threshold. If the immersive matching coefficient exceeds the preset immersive matching coefficient threshold, generate an immersive high-impact signal; if the immersive matching coefficient does not exceed the preset immersive matching coefficient threshold, generate an immersive low-impact signal.

[0010] Further, the output display unit is communicatively connected to the basic library station training supervision unit. The basic library station training supervision unit is used to set the detection period, analyze the personnel training status of the basic library station during the detection period, generate a training supervision qualified signal or a training supervision unqualified signal through the analysis, and send the training supervision qualified signal or the training supervision unqualified signal to the output display unit for display. Among them, the specific analysis process of the basic library station training supervision unit is as follows: Obtain all the employees of the basic library station, collect all the assessment reports of the corresponding employees in the assessment version module during the detection period, calculate the ratio of the number of times the corresponding employees failed the assessment in the historical stage to the total number of assessments to obtain the assessment anomaly value, and compare the assessment anomaly value with the preset assessment anomaly threshold. If the assessment anomaly value exceeds the preset assessment anomaly threshold, mark the corresponding employees as risk employees; If the assessment anomaly value does not exceed the preset assessment anomaly threshold, mark the total historical working duration of the corresponding employees in the basic library station as the total working value, and collect the single-time duration of each learning of the corresponding employees in the teaching version module during the detection period. Mark the learning process with a single-time duration exceeding the preset single-time duration threshold as a compliance process, mark the number of compliance processes corresponding to the corresponding employees during the detection period as the compliance frequency, and calculate the sum of all single-time durations of the corresponding employees learning in the teaching version module during the detection period to obtain the school hour detection value; Obtain the employee execution value by calculating the weighted sum of the compliance frequency and the school hour detection value. Preset several groups of preset total working value ranges in advance, and each group of preset total working value ranges corresponds to a group of preset employee execution thresholds respectively. Compare the total working value of the corresponding employees with all the preset total working value ranges one by one, and mark the preset employee execution threshold corresponding to the preset total working value range containing the corresponding total working value as the target threshold; Numerically compare the employee execution value with the corresponding target threshold. If the employee execution value does not exceed the corresponding target threshold, mark the corresponding employee as a risky employee; obtain the number of risky employees in the grass-roots depot during the detection period and mark the ratio of the number of risky employees to the total number of employees as the training supervision outlier value. Numerically compare the training supervision outlier value with the preset training supervision outlier threshold. If the training supervision outlier value exceeds the preset training supervision outlier threshold, generate a training supervision unqualified signal; if the training supervision outlier value does not exceed the preset training supervision outlier threshold, generate a training supervision qualified signal.

[0011] Furthermore, the grass-roots depot training supervision unit is communicatively connected to the grass-roots depot operation risk analysis unit. The grass-roots depot training supervision unit sends the training supervision qualified signal to the grass-roots depot operation risk analysis unit. When the grass-roots depot operation risk analysis unit receives the training supervision qualified signal, it evaluates and analyzes the operation risk level of the grass-roots depot during the detection period, generates an operation risk alarm signal or an operation low-risk signal through the analysis, and sends the operation risk alarm signal or the operation low-risk signal to the output display unit for display; among them, the specific analysis process of the grass-roots depot operation risk analysis unit is as follows: Collect the total number of accidents that occurred in the grass-roots depot during the detection period and mark it as the operation risk frequency. Numerically compare the operation risk frequency with the preset operation risk frequency threshold. If the operation risk frequency exceeds the preset operation risk frequency threshold, generate an operation risk alarm signal; If the operation risk frequency does not exceed the preset operation risk frequency threshold, monitor the grass-roots depot. Mark the ratio of the number of risky employees on duty in the grass-roots depot as the on-duty risk situation value. Numerically compare the on-duty risk situation value with the preset on-duty risk situation threshold. If the on-duty risk situation value exceeds the preset on-duty risk situation threshold, it is determined that the grass-roots depot is in an abnormal on-duty state; Obtain the single duration of each time the grass-roots depot is in an abnormal on-duty state during the detection period and mark it as the on-duty abnormal duration value. Sum up all the on-duty abnormal duration values during the detection period to obtain the on-duty abnormal time value, and mark the number of on-duty abnormal duration values that exceed the preset on-duty abnormal duration threshold during the detection period as the on-duty abnormal frequency value; Numerically compare the on-duty abnormal time value and the on-duty abnormal frequency value with the preset on-duty abnormal time threshold and the preset on-duty abnormal frequency threshold respectively. If the on-duty abnormal time value or the on-duty abnormal frequency value exceeds the corresponding preset threshold, generate an operation risk alarm signal; if both the on-duty abnormal time value and the on-duty abnormal frequency value do not exceed the corresponding preset threshold, generate an operation low-risk signal.

[0012] Furthermore, a grass-roots depot VR training management method proposed by the present invention includes the following steps: Step 1: Use 3ds Max to perform high-precision modeling on gas stations and oil depots. After the modeling is completed, export FBX format files; Step 2: Import the model in Unity3D, create environmental elements, and build two virtual scenarios: the integrated energy supply gas station and the coastal oil depot; Step 3: Connect the VR device to create an immersive learning foundation for users; Step 4: Authenticate the user. After the authentication is completed, the user enters the selection process and undergoes practical training when the selection is finished.

[0013] Furthermore, the present invention provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned VR practical training management method for grass-roots depots and stations.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In the present invention, through virtual reality technology, employees can quickly learn equipment operation and accident handling in a short time, realizing zero-risk emergency handling training, greatly enhancing the safety of training, reducing the enterprise's dependence on on-site training, improving employees' operation skills and emergency handling capabilities, being beneficial to ensuring the operation safety of grass-roots depots and stations, and conducting equipment cooperation evaluation and analysis on all equipment involved in the VR device access unit before connecting the VR device, and replacing and adjusting the corresponding equipment when generating immersive high-impact signals to ensure the immersive effect and experience stability of the grass-roots depot and station practical training experience; 2. In the present invention, through the grass-roots depot and station practical training supervision unit, the personnel practical training status of grass-roots depots and stations during the detection period is analyzed. When a signal indicating unqualified practical training supervision is generated, the supervision of employees in grass-roots depots and stations is strengthened to improve and maintain the working ability of each employee, and when a signal indicating qualified practical training supervision is generated, the operation risk level of grass-roots depots and stations during the detection period is evaluated and analyzed. When a signal indicating operation risk alarm is generated, the subsequent management of grass-roots depots and stations is strengthened and the personnel arrangement of grass-roots depots and stations is reasonably carried out, significantly reducing the operation risk of grass-roots depots and stations and having a high level of intelligence. Description of the Drawings

[0015] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings; Figure 1 It is the system block diagram of Embodiment 1 in the present invention; Figure 2 It is the system block diagram of Embodiment 2 and Embodiment 3 in the present invention; Figure 3 It is the method flowchart of Embodiment 4 in the present invention. Detailed Embodiments

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1 As Figure 1 shown, a grass-roots library and station VR training management system proposed by the present invention includes a 3D modeling unit, a virtual scene simulation construction unit, a VR device access unit, a training login selection unit, an immersive impact analysis unit, and an output display unit; Among them, the 3D modeling unit uses 3ds Max to perform high-precision modeling on gas stations and oil depots to ensure the real restoration of the operation scene, and exports FBX format files for Unity3D after the modeling is completed; the virtual scene simulation construction unit imports the models in Unity3D, creates environmental elements to enhance the scene realism, builds two virtual scenes of a comprehensive energy supply gas station and a coastal oil depot, and stores the created scenes; The VR device access unit is used to access VR devices (including VR headsets, motion capture devices, tactile feedback devices, etc.). Users can roam through the first-person perspective, operate the devices and handle accidents, enhancing the immersive learning experience; the training login selection unit is used for user login and user authentication. After the authentication is completed, the user enters the selection process, selects to enter the teaching version module or the assessment version module, and starts the training after the selection is completed; the output display unit displays the user's training information and VR device access information.

[0018] It should be noted that the comprehensive energy supply gas station constructed by the virtual scene simulation construction unit mainly simulates the emergency disposal of oil spill from fuel dispensers, the emergency disposal of oil spill and fire during oil unloading at the energy supply station, the six-step method of refueling service, the eight-step method of underground tank handover during oil unloading at the energy supply station, and the inspection content during shift handover at the energy supply station; the coastal oil depot scene constructed by the virtual scene simulation construction unit focuses on simulating the comprehensive emergency drill for leakage and fire of the loading arm in the oil loading area, the comprehensive emergency drill for leakage and fire of the tank root valve, and the inspection of the coastal oil depot.

[0019] Furthermore, the virtual scene simulation construction unit covers the teaching version module and the assessment version module. Among them, the teaching version module is mainly used for basic training, suitable for new employees and operators, providing step-by-step operation guides, learning operation processes, equipment operation methods, and safety regulations; the assessment version module is mainly used to assess the operation ability and emergency handling level of employees, automatically records the operation process and generates an assessment report, provides operation scores through data analysis, and gives improvement suggestions to help enterprises comprehensively evaluate the training effect of employees.

[0020] Through the virtual reality technology in the technical solution of the present invention, employees can quickly learn equipment operation and accident handling in a short time. Compared with traditional on-site training, the time is significantly shortened. Moreover, there are relatively large safety risks in traditional on-site training, while the VR system realizes zero-risk emergency handling training through virtual simulation scenarios, greatly enhancing the safety of training, reducing the enterprise's dependence on on-site training, and lowering the costs in aspects such as training venues and equipment maintenance. In addition, employees repeatedly practice in the virtual environment, improving their operation skills and emergency handling abilities, enhancing their safety awareness, and being conducive to reducing the operation risks of grass-roots depots and stations.

[0021] Before the VR device access unit accesses the VR device, the immersive impact analysis unit conducts equipment cooperation evaluation and analysis on all the equipment involved in the VR device access unit, generates an immersive high-impact signal or an immersive low-impact signal through the analysis, and sends the immersive high-impact signal or the immersive low-impact signal to the output display unit for display. Moreover, when the output display unit receives the immersive high-impact signal, it issues a warning to remind the management personnel to conduct replacement and adjustment of the corresponding equipment, ensuring the immersive effect and experience stability of the grass-roots depot and station training experience and reducing the training management difficulty. The specific analysis process of the equipment cooperation evaluation and analysis is as follows: All the equipment involved in the VR device access unit is obtained, and the corresponding equipment is marked as i, where i is a natural number greater than 1; the production date of the equipment i is collected, and the interval duration between the current date and the production date is marked as the production-time collection value. Moreover, the total usage duration of the equipment i in the historical stage is marked as the usage-time collection value. The production-time collection value and the usage-time collection value are respectively compared numerically with the corresponding preset production-time collection threshold and preset usage-time collection threshold. If the production-time collection value or the usage-time collection value exceeds the corresponding preset threshold, it indicates that the equipment condition of the equipment i is poor, and then the equipment i is marked as a reverse device; If both the production-time collection value and the usage-time collection value do not exceed the corresponding preset thresholds, then with the current moment as the ending moment, a tracking period with a set duration of L1 is traced back. Preferably, L1 = 40 days; the number of times the equipment i fails during operation within the tracking period and the operation duration of the equipment i within the tracking period are obtained, and the ratio of the number of times the equipment i fails during operation within the tracking period to the operation duration of the equipment i within the tracking period is calculated to obtain the tracking anomaly value; The equipment collection value is calculated by weighted summation of the tracking anomaly value, the production-time collection value, and the usage-time collection value, that is, preset weight coefficients greater than zero are assigned to the tracking anomaly value, the production-time collection value, and the usage-time collection value in advance. The tracking anomaly value, the production-time collection value, and the usage-time collection value are respectively multiplied by the corresponding preset weight coefficients, and the three product results are summed up to obtain the equipment collection value accordingly; moreover, the larger the value of the equipment collection value, the worse the overall equipment condition of the equipment i. The device acquisition value is numerically compared with the corresponding preset device acquisition threshold. If the device acquisition value exceeds the preset device acquisition threshold, it indicates that the overall device condition of device i is poor, and then device i is marked as a reverse device. If there is a reverse device among the devices involved in the VR device access unit, it indicates that the current training conditions provided are not good, which is not conducive to ensuring the immersive experience effect and experience stability, and then an immersive high-impact signal is generated.

[0022] Furthermore, if there is no reverse device among the devices involved in the VR device access unit, the ratio of the device acquisition value of device i to the corresponding preset device acquisition threshold is calculated to obtain the device acquisition occupancy value. The average value of the device acquisition occupancy values of all devices involved in the VR device access unit is calculated to obtain the immersive allocation difference coefficient. Among them, the larger the value of the immersive allocation difference coefficient, the better the overall training conditions provided currently. The immersive allocation difference coefficient is numerically compared with the preset immersive allocation difference coefficient threshold; If the immersive allocation difference coefficient exceeds the preset immersive allocation difference coefficient threshold, it indicates that the current training conditions provided are not good, which is not conducive to ensuring the immersive experience effect and experience stability, and then an immersive high-impact signal is generated. If the immersive allocation difference coefficient does not exceed the preset immersive allocation difference coefficient threshold, it indicates that the current training conditions provided are good, which can ensure the immersive experience effect and experience stability, and then an immersive low-impact signal is generated.

[0023] Embodiment 2 As Figure 2 shown, the difference between this embodiment and Embodiment 1 is that the output display unit is communicatively connected to the basic library station training supervision unit. The basic library station training supervision unit is used to set the detection period. Preferably, the detection period is 30 days. The personnel training status of the basic library station within the detection period is analyzed to generate a training supervision qualified signal or a training supervision unqualified signal through the analysis; And the training supervision qualified signal or the training supervision unqualified signal is sent to the output display unit for display. When the output display unit receives the training supervision unqualified signal, it issues a corresponding warning to remind the management personnel to strengthen the supervision of the employees of the basic library station in the future, so that the working abilities of each employee can be improved and maintained, which is conducive to ensuring the operation safety of the basic library station. Among them, the specific analysis process of the basic library station training supervision unit is as follows: All employees at the grass-roots depots and stations are obtained, and all assessment reports of the corresponding employees in the assessment version module during the detection period are collected. The ratio of the number of times the corresponding employees' assessments are unqualified in the historical stage to the total number of assessments is calculated to obtain the assessment anomaly value. The assessment anomaly value is numerically compared with the preset assessment anomaly threshold. If the assessment anomaly value exceeds the preset assessment anomaly threshold, it indicates that the assessment performance of the corresponding employees during the detection period is poor and the work risk at the grass-roots depots and stations is relatively high. Then, the corresponding employees are marked as risk employees; If the assessment anomaly value does not exceed the preset assessment anomaly threshold, the total historical working duration of the corresponding employees in the grass-roots depots and stations is marked as the total working value, and the single duration of each learning of the corresponding employees in the teaching version module during the detection period is collected. The learning process with a single duration exceeding the preset single-duration threshold is marked as a compliance process. The number of compliance processes corresponding to the corresponding employees during the detection period is marked as the compliance frequency, and the sum of all single durations of the corresponding employees' learning in the teaching version module during the detection period is calculated to obtain the learning-hour detection value; The employee execution value is obtained by weighted summation calculation of the compliance frequency and the learning-hour detection value, that is, preset weight coefficients greater than zero are assigned to the compliance frequency and the learning-hour detection value in advance. The compliance frequency and the learning-hour detection value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the two product results is marked as the employee execution value; moreover, the larger the value of the employee execution value, the better the comprehensive practical training and learning performance of the corresponding employees during the detection period; Several groups of preset total working value ranges are set in advance, and each group of preset total working value ranges corresponds to a group of preset employee execution thresholds; moreover, the larger the value of the preset total working value range, the smaller the value of the corresponding preset employee execution threshold; the total working value of the corresponding employees is compared one by one with all preset total working value ranges, and the preset employee execution threshold corresponding to the preset total working value range containing the corresponding total working value is marked as the target threshold; The employee execution value is numerically compared with the corresponding target threshold. If the employee execution value does not exceed the corresponding target threshold, it indicates that the practical training and learning performance of the corresponding employees during the detection period is poor and the work risk at the grass-roots depots and stations is relatively high. Then, the corresponding employees are marked as risk employees; The number of risk employees at the grass-roots depots and stations during the detection period is obtained and the ratio of it to the total number of employees is marked as the practical training supervision anomaly value. The practical training supervision anomaly value is numerically compared with the preset practical training supervision anomaly threshold. If the practical training supervision anomaly value exceeds the preset practical training supervision anomaly threshold, it indicates that the personnel assessment and learning performance at the grass-roots depots and stations during the detection period is poor. Then, a practical training supervision unqualified signal is generated; if the practical training supervision anomaly value does not exceed the preset practical training supervision anomaly threshold, it indicates that the personnel assessment and learning performance at the grass-roots depots and stations during the detection period is generally good. Then, a practical training supervision qualified signal is generated.

[0024] Example 3 As Figure 2 shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the grass-roots depot training supervision unit is communicatively connected to the grass-roots depot operation risk analysis unit. The grass-roots depot training supervision unit sends a qualified training supervision signal to the grass-roots depot operation risk analysis unit. When the grass-roots depot operation risk analysis unit receives the qualified training supervision signal, it evaluates and analyzes the operation risk level of the grass-roots depot during the detection period, and generates an operation risk alarm signal or an operation low-risk signal through the analysis; and sends the operation risk alarm signal or the operation low-risk signal to the output display unit for display. When the output display unit receives the operation risk alarm signal, it issues a corresponding early warning to remind the management personnel to strengthen the subsequent management of the grass-roots depot and reasonably arrange the personnel of the grass-roots depot, further reducing the operation risk of the grass-roots depot. Among them, the specific analysis process of the grass-roots depot operation risk analysis unit is as follows: Collect the total number of accidents that occurred in the grass-roots depot during the detection period and mark it as the operation risk frequency. Compare the operation risk frequency with the preset operation risk frequency threshold. If the operation risk frequency exceeds the preset operation risk frequency threshold, it indicates that the operation risk of the grass-roots depot is relatively large during the detection period, and an operation risk alarm signal is generated; If the operation risk frequency does not exceed the preset operation risk frequency threshold, the grass-roots depot is monitored. Mark the ratio of the number of risk employees on duty in the grass-roots depot as the on-duty risk condition value. Compare the on-duty risk condition value with the preset on-duty risk condition threshold. If the on-duty risk condition value exceeds the preset on-duty risk condition threshold, it indicates that the current personnel arrangement of the grass-roots depot is unreasonable and there is a relatively large risk, then it is judged that the grass-roots depot is in an abnormal on-duty state; Obtain the single duration of each abnormal on-duty state of the grass-roots depot during the detection period and mark it as the on-duty abnormal duration value. Sum up all the on-duty abnormal duration values during the detection period to obtain the on-duty abnormal value, and compare the on-duty abnormal duration value with the preset on-duty abnormal duration threshold, and mark the number of on-duty abnormal duration values that exceed the preset on-duty abnormal duration threshold during the detection period as the on-duty abnormal frequency value; Compare the on-duty abnormal value and the on-duty abnormal frequency value with the preset on-duty abnormal threshold and the preset on-duty abnormal frequency threshold respectively. If the on-duty abnormal value or the on-duty abnormal frequency value exceeds the corresponding preset threshold, it indicates that the personnel arrangement situation of the grass-roots depot during the detection period is poor and the potential safety hazards are relatively high, and an operation risk alarm signal is generated; if both the on-duty abnormal value and the on-duty abnormal frequency value do not exceed the corresponding preset threshold, it indicates that the personnel arrangement situation of the grass-roots depot during the detection period is poor and the potential safety hazards are relatively low, and an operation low-risk signal is generated.

[0025] Example 4 As Figure 3As shown in the figure, the difference between this embodiment and Embodiment 1, Embodiment 2, and Embodiment 3 is that a VR training management method for grass-roots depots and stations proposed by the present invention includes the following steps: Step 1: Use 3ds Max to perform high-precision modeling on gas stations and oil depots, and export the file in FBX format after the modeling is completed; Step 2: Import the model in Unity3D, create environmental elements, and build two virtual scenarios of an integrated energy supply gas station and a coastal oil depot; Step 3: Connect VR devices to create a basis for immersive learning for users; Step 4: Authenticate the user. After the authentication is completed, the user enters the selection process and conducts training when the selection is completed.

[0026] Moreover, the present invention also proposes a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned VR training management method for grass-roots depots and stations is implemented. Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0027] The working principle of the present invention: When in use, perform high-precision modeling on gas stations and oil depots using 3ds Max, import the model in Unity3D, create environmental elements to enhance the realism of the scene, build two virtual scenarios of an integrated energy supply gas station and a coastal oil depot, connect the VR device access unit to the VR device, enable employees to quickly learn equipment operation and accident handling in a short time through virtual reality technology, greatly shorten the training time compared with traditional on-site training, and realize zero-risk emergency handling training through virtual simulation scenarios, greatly enhancing the safety of training, reducing the enterprise's dependence on on-site training, reducing costs in aspects such as training venues and equipment maintenance, improving employees' operation skills and emergency handling capabilities, enhancing safety awareness, being beneficial to reducing the operation risk of grass-roots depots and stations, and conducting equipment cooperation assessment and analysis on all equipment involved in the VR device access unit before connecting the VR device, and performing replacement and adjustment of corresponding equipment when generating an immersive high-impact signal to ensure the immersive effect and experience stability of the grass-roots depot and station training experience, with a high level of intelligence.

[0028] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments only. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A VR training management system for grass-roots depots and stations, characterized in that It includes a 3D modeling unit, a virtual scene simulation construction unit, a VR device access unit, a training login selection unit, and an output display unit; the 3D modeling unit uses 3ds Max to perform high-precision modeling on gas stations and oil depots, and exports FBX format files after the modeling is completed; The virtual scene simulation construction unit imports the FBX scene model in Unity3D, creates environmental elements, and builds two virtual scenes: an integrated energy supply gas station and a coastal oil depot; The VR device access unit is used to access VR devices. Users can roam through the first-person perspective, operate the devices, and handle accidents; The training login selection unit is used for user login and user authentication. After the authentication is completed, the user enters the selection process and selects to enter the teaching version module or the assessment version module; the output display unit displays the user's training information and VR device access information.

2. The grass-roots library and station VR training management system according to claim 1, wherein The integrated energy supply gas station constructed by the virtual scene simulation construction unit simulates the emergency disposal of fuel dispenser overflow, the emergency disposal of oil spill and fire during oil unloading at the energy supply station, the six-step method of refueling service, the eight-step method of underground tank handover during oil unloading at the energy supply station, and the inspection content during the shift handover at the energy supply station; The coastal oil depot scene constructed by the virtual scene simulation construction unit focuses on simulating the comprehensive emergency drill for fire caused by the leakage of the loading arm in the oil loading area, the comprehensive emergency drill for fire caused by the leakage of the tank root valve, and the inspection of the coastal oil depot.

3. The grass-roots library VR training management system according to claim 2, characterized in that The teaching version module is used for basic training, providing step-by-step operation guides, learning operation processes, equipment operation methods, and safety regulations; the assessment version module is used to assess the operation ability and emergency handling level of employees, automatically records the operation process and generates an assessment report, provides operation scores through data analysis, and gives improvement suggestions.

4. A grass-roots library VR training management system according to claim 1, characterized in that, The output display unit is communicatively connected to the immersive impact analysis unit. The immersive impact analysis unit conducts equipment cooperation evaluation and analysis on all the devices involved in the VR device access unit, and sends immersive high-impact signals or immersive low-impact signals to the output display unit for display.

5. The VR training management system for grass-roots depots and stations according to claim 4, characterized in that The specific analysis process of the equipment cooperation evaluation and analysis is as follows: Obtain all the devices involved in the VR device access unit, mark the corresponding device as i, and i is a natural number greater than 1; collect the production date of device i, mark the time interval between the current date and the production date as the production time collection value, and mark the total usage time of device i in the historical stage as the usage time collection value. Compare the production time collection value and the usage time collection value with the corresponding preset production time collection threshold and preset usage time collection threshold respectively. If the production time collection value or the usage time collection value exceeds the corresponding preset threshold, mark device i as a reverse device; If both the production-time acquisition value and the usage-time acquisition value do not exceed their corresponding preset thresholds, then the current moment is taken as the ending moment and traced back for a tracking period with a set duration of L1. Calculate the ratio of the number of times device i malfunctioned during operation within the tracking period to the running duration of device i within the tracking period to obtain a tracking anomaly value. Perform a weighted summation calculation on the tracking anomaly value, the production-time acquisition value, and the usage-time acquisition value to obtain a device acquisition value. Compare the device acquisition value with the corresponding preset device acquisition threshold. If the device acquisition value exceeds the preset device acquisition threshold, then mark device i as a reverse device; if there is a reverse device among the devices involved in the VR device access unit, then generate an immersive high-impact signal.

6. The VR training management system for basic libraries and stations according to claim 5, wherein If there is no reverse device among the devices involved in the VR device access unit, then calculate the average value of the device acquisition occupancy values of all the devices involved in the VR device access unit to obtain an immersive allocation difference coefficient. If the immersive allocation difference coefficient exceeds the preset immersive allocation difference coefficient threshold, then generate an immersive high-impact signal; otherwise, generate an immersive low-impact signal.

7. The grass-roots library VR training management system according to claim 6, characterized in that The output display unit is communicatively connected to the grass-roots depot training supervision unit. The grass-roots depot training supervision unit is used to set a detection period, analyze the personnel training status of the grass-roots depot during the detection period, and send a training supervision qualified signal or a training supervision unqualified signal to the output display unit for display; among them, the specific analysis process of the grass-roots depot training supervision unit is as follows: Obtain all the employees of the grass-roots depot, collect all the assessment reports of the corresponding employees in the assessment version module during the detection period, calculate the ratio of the number of times the corresponding employees failed the assessment in the historical stage to the total number of assessments to obtain an assessment anomaly value, compare the assessment anomaly value with the preset assessment anomaly threshold. If the assessment anomaly value exceeds the preset assessment anomaly threshold, then mark the corresponding employees as risk employees; If the assessment anomaly value does not exceed the preset assessment anomaly threshold, then mark the total historical duration of the corresponding employees working at the grass-roots depot as the total working time value, and collect the single-time duration of each learning of the corresponding employees in the teaching version module during the detection period. Mark the learning process with a single-time duration exceeding the preset single-time duration threshold as a compliance process, mark the number of compliance processes corresponding to the corresponding employees during the detection period as the compliance frequency, and calculate the sum of all the single-time durations of the corresponding employees learning in the teaching version module during the detection period to obtain a school-hour detection value; Obtain an employee execution value by performing a weighted summation calculation on the compliance frequency and the school-hour detection value. Preset several groups of preset total working time value ranges in advance, and each group of preset total working time value ranges corresponds to a group of preset employee execution thresholds; compare the total working time value of the corresponding employees with all the preset total working time value ranges one by one, and mark the preset employee execution threshold corresponding to the preset total working time value range containing the corresponding total working time value as the target threshold; Numerically compare the employee execution value with the corresponding target threshold. If the employee execution value does not exceed the corresponding target threshold, mark the corresponding employee as a risky employee; obtain the number of risky employees in the grass-roots depot-station during the detection period and mark the ratio of the number of risky employees to the total number of employees as the training supervision outlier value. Numerically compare the training supervision outlier value with the preset training supervision outlier threshold. If the training supervision outlier value exceeds the preset training supervision outlier threshold, generate a training supervision unqualified signal; if the training supervision outlier value does not exceed the preset training supervision outlier threshold, generate a training supervision qualified signal.

8. A basic library and station VR training management system according to claim 7, characterized in that, The training supervision unit of the grass-roots depot-station is communicatively connected to the operation risk analysis unit of the grass-roots depot-station. When the operation risk analysis unit of the grass-roots depot-station receives the training supervision qualified signal, it evaluates and analyzes the operation risk degree of the grass-roots depot-station during the detection period, and sends the operation risk alarm signal or the operation low-risk signal to the output display unit for display; among them, the specific analysis process of the operation risk analysis unit of the grass-roots depot-station is as follows: If the operation risk frequency exceeds the preset operation risk frequency threshold, generate an operation risk alarm signal; if the operation risk frequency does not exceed the preset operation risk frequency threshold, numerically compare the on-duty outlier value and the on-duty outlier frequency value with the preset on-duty outlier threshold and the preset on-duty outlier frequency threshold respectively. If the on-duty outlier value or the on-duty outlier frequency value exceeds the corresponding preset threshold, generate an operation risk alarm signal; otherwise, generate an operation low-risk signal.

9. A VR training management method for grass-roots depots and stations, characterized in that, Include the following steps: Step 1, high-precision modeling; Step 2, build a virtual scene; Step 3, connect VR devices; Step 4, user identity authentication. After the identity authentication is completed, the user enters the selection process and conducts training when the selection is completed.

10. A computer storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, it implements the grass-roots depot-station VR training management method as described in claim 9.

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