Safety production virtual training method and system for mine underground scene

Abstract

The invention discloses a safety production virtual training method and system for a mine underground scene, and the method comprises the steps: creating a model library, and storing a preset part model through the model library; based on the mine underground mine hole excavation design drawing, the spatial orientation of each component model is obtained, and a virtual practical training scene of the mine underground is generated; based on a received practical training instruction, a virtual practical training scene is obtained from the scene library according to the practical training instruction, and the virtual practical training scene is displayed on the interaction module; adjusting and transforming the virtual practical training scene based on an operation feedback signal of a practical training miner, labeling the component model with a high danger coefficient in the virtual practical training scene, judging the safety condition of each component model under the mine, calculating the average labeling number in the virtual practical training scene, and comprehensively judging the safety coefficient of the mine. The system has the advantages that the mine underground environment is simulated through the virtual reality technology, miners are helped to carry out safety operation training under the risk-free condition, and the safety consciousness and the emergency response capacity of the miners are improved.

Description

Technical Field

[0001] The present invention relates to virtual reality technology, and particularly to a virtual training method and system for safe production in underground mine scenes. Background Art

[0002] A virtual training method and system for safe production in underground mine scenes creates a model library containing component models such as mine shafts, tunnels, and equipment, combines and rotates them according to the spatial positions of the mine design drawings to generate a complete virtual mine scene. The spatial information of these component models is converted into 3D coordinates to ensure the accuracy of the models and their operability in practical applications. On this basis, the system extracts the corresponding virtual training scenes from the scene library according to the received training instructions and displays them in the interaction module. During the training process of miners, according to the operation feedback, the system will adjust the scene in real time, especially marking the components with high danger coefficients. By counting the number of marks, the system can judge the safety status of each component of the mine and calculate the average number of marks in the virtual scene, so as to comprehensively evaluate the overall safety factor of the mine.

[0003] The current training methods for underground mine scenes on the market mainly use virtual reality (VR), augmented reality (AR), and digital twin technologies to create a realistically restored underground operation environment. Through VR technology, trainees can experience the underground environment of the mine immersively and learn how to respond to emergencies such as gas explosions and mine tremors, avoiding potential safety hazards in real operations. AR technology helps trainees perform tasks such as equipment maintenance and fault troubleshooting more intuitively by overlaying virtual information in real time. Digital twin technology creates a virtual model highly consistent with the real mine environment, making the training more precise, capable of monitoring the operations and performances of trainees in real time, and further enhancing the interactivity and accuracy of the training. The combination of these technologies not only provides a low-risk and high-efficiency training method for miners, but also reduces training costs and enables repeated practice in a simulated environment to accumulate experience. Summary of the Invention

[0004] In order to improve the existing virtual training method for underground mine scenes, a virtual training method and system for safe production in underground mine scenes are provided. The method creates a virtual scene containing mine shafts, tunnels, and equipment models, simulates the operations of miners, and provides real-time feedback on safety conditions. The system helps improve the safety awareness and emergency handling capabilities of miners by marking dangerous components and evaluating safety factors.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A virtual training method for safe production in underground mine scenes includes:

[0007] Create a model library to store preset component models through the model library. The component models include mine models, tunnel models, and equipment models;

[0008] Based on the mine shaft excavation design drawing underground in the mine, obtain the spatial orientation of each component model and generate a virtual training scenario underground in the mine;

[0009] Based on the received training instruction, obtain the virtual training scenario from the scenario library according to the training instruction and display it on the interaction module;

[0010] Based on the operation feedback signal of the training miners, adjust and transform the virtual training scenario, and label the component models with high danger coefficients in the virtual training scenario;

[0011] Based on the number of labels of each component module, judge the safety of each component model underground in the mine, and calculate the average number of labels in the virtual training scenario to comprehensively judge the mine safety coefficient.

[0012] Preferably, the creation of the model library to store preset component models through the model library. The component models include mine models, tunnel models, and equipment models specifically includes:

[0013] Create the database structure of the model library and record the metadata of each model;

[0014] Design the directory structure to manage and store model files;

[0015] Classify the complexity of the virtual teaching scenario based on the number of equipment models in the virtual training scenario. The complexity levels include low, medium, and high.

[0016] Preferably, the obtaining of the spatial orientation of each component model based on the mine shaft excavation design drawing underground in the mine and generating a virtual training scenario underground in the mine specifically includes:

[0017] Based on the mine shaft excavation design drawing underground in the mine, obtain the cross-sectional dimensions, excavation depth, inclination angle of the mine shaft, and curve and slope changes of the tunnel;

[0018] Perform modeling based on the three-dimensional Cartesian coordinate system to represent the spatial orientation of each component model in the mine shaft;

[0019] Standardize the data in the design drawing and unify all data into values in the standard coordinate system;

[0020] Convert the position of each component model on the design drawing into the 3D coordinate system. The formula is:

[0021] x = x 0 + L·cos(α)

[0022] y = y 0 + L·cos(α)

[0023] z = z 0 -D

[0024] Wherein, L is the length of the component in the plan view, α is the azimuth angle of the component in the design drawing, and D is the relative depth of the component;

[0025] Based on the partial model affected by spatial rotation during the conversion to 3D, perform the component rotation operation;

[0026] Based on all component models being placed in their corresponding positions, obtain the virtual training scenario for the entire underground mine.

[0027] Preferably, the performing the component rotation operation based on the partial model affected by spatial rotation during the conversion to 3D specifically includes:

[0028] Based on the x, y, and z axes of the three-dimensional Cartesian coordinate system, obtain the rotation matrices respectively;

[0029] Rotate around the x-axis, and the rotation matrix is:

[0030]

[0031] Rotate around the y-axis, and the rotation matrix is:

[0032]

[0033] Rotate around the z-axis, and the rotation matrix is:

[0034]

[0035] Wherein, θ is the rotation angle.

[0036] Preferably, the obtaining the virtual training scenario from the scenario library according to the training instruction and displaying it on the interaction module based on the received training instruction specifically includes:

[0037] Based on the operation steps underground in the mine, deliver different-stage training scenarios and component models to the training miners;

[0038] Based on the different-stage training scenarios and component models, the training miners make corresponding operations and send operation signals for subsequent processing.

[0039] Preferably, the adjusting and transforming the virtual training scenario based on the operation feedback signal of the training miners and marking the component models with high danger coefficients in the virtual training scenario specifically includes:

[0040] Based on the operation feedback signal of the training miners, obtain their position information, status information, and operation data;

[0041] Based on the obtained location information, substitute it into the modeling of the three-dimensional Cartesian coordinate system to determine whether the training scenario of the current trainee miner needs to be changed. If it is in the edge area of the component model, change its training scenario; otherwise, do not make any changes.

[0042] Based on the obtained status information, adjust the environmental conditions in the scenario according to the physical sign feedback of the miner.

[0043] Based on the obtained operation data, mark the components with relatively high danger coefficients in the training scenario, including highlighting, popping up a prompt box, and displaying a warning sign.

[0044] Based on the components marked with relatively high danger coefficients, update the markings in the scenario in real time to prompt the miner to pay attention.

[0045] Preferably, judging the safety conditions of each component model underground based on the marking quantity of each component module, and calculating the average marking quantity in the virtual training scenario, and comprehensively judging the mine safety coefficient specifically includes:

[0046] Based on the marking quantity of each component module, set a threshold to judge the safety conditions of the current component models.

[0047] Obtain the total sum of the marking quantities of all components and calculate the average marking quantity.

[0048] Based on the weighted average calculation of the marking quantities of each component, obtain the safety coefficient K of the current mine. The formula is:

[0049]

[0050] Among them, m is the number of components, ω i is the weight of the i-th component, and S i is the safety level of the i-th component, specifically the importance of the component or the functional importance in the mine.

[0051] Furthermore, a virtual training system for safe production in underground mine scenes is proposed, which is characterized by including:

[0052] A model library module, which is mainly used to store preset component models, and the component models include mine models, tunnel models, and equipment models.

[0053] A scene construction module, which is mainly used to convert the data in the underground mine tunnel excavation design drawing into a 3D coordinate system and generate a training scenario.

[0054] A model rotation module, which is mainly used to perform component rotation operations on the part of the model affected by spatial rotation and convert it to a normal angle.

[0055] An interaction module, which is mainly used for training miners to obtain the implemented training scenarios and send the current status and operation data;

[0056] A marking module, which is mainly used for marking the component models with high danger coefficients in the virtual training scenario and feeding back the relevant data of the dangerous module;

[0057] A safety factor module, which is mainly used for evaluating the safety of the mine based on the marked quantity of each component module in the mine and obtaining the safety factor;

[0058] A processor, which is mainly used for converting the design drawing data to the 3D coordinate system, rotating some models, and calculating the mine safety factor.

[0059] Compared with the prior art, the advantages of the present invention are as follows:

[0060] By using virtual reality technology, the complex environment underground in the mine is highly restored. Without actually entering the underground, it can simulate the operation process of miners and potential dangerous scenarios, reducing the risks in actual operations. Secondly, the virtual training scenario can be accurately constructed according to the mine design drawing to ensure the spatial accuracy of each component model, making the training closer to the actual working conditions. Miners can conduct various operation trainings and emergency drills in a real virtual environment. In addition, this method can receive the operation feedback of miners in real time, adjust the virtual scenario in a timely manner, and mark the potential dangerous areas, which helps miners quickly identify and avoid risks in actual work. By counting the marked quantity, the system can scientifically evaluate the safety factors of each component in the mine, providing data support for the safety management of the mine. At the same time, virtual training has the advantage of repeated training. Miners can practice multiple times, familiarize themselves with different operation processes, improve their emergency response capabilities and decision-making levels, and thus effectively improve the overall safety of mine operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the training method of the method and system proposed by the present invention;

[0062] Figure 2 It is a schematic diagram of model creation of the method and system proposed by the present invention;

[0063] Figure 3 It is a schematic diagram of the generation of the training scenario of the method and system proposed by the present invention;

[0064] Figure 4 It is a schematic diagram of the component rotation operation of the method and system proposed by the present invention;

[0065] Figure 5 It is a schematic diagram of the transformation of the training scenario of the method and system proposed by the present invention;

[0066] Figure 6 Schematic diagram for marking dangerous components of the method and system proposed by the present invention;

[0067] Figure 7 Schematic diagram for safety assessment of the method and system proposed by the present invention;

[0068] Figure 8 Architecture diagram of the electronic device in this solution;

[0069] Figure 9 Schematic diagram of the structure of the computer-readable storage medium in this solution. Detailed implementation manners

[0070] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0071] A virtual training system for safe production in underground mine scenarios, comprising:

[0072] A model library module, which is mainly used to store preset component models, and the component models include mine models, tunnel models and equipment models;

[0073] A scene construction module, which is mainly used to convert the data in the mine excavation design drawing underground into a 3D coordinate system and generate a training scene;

[0074] A model rotation module, which is mainly used to perform component rotation operations on some models affected by spatial rotation and convert them to a normal angle;

[0075] An interaction module, which is mainly used for trainee miners to obtain the implemented training scene and send current status and operation data;

[0076] A marking module, which is mainly used to mark component models with high danger coefficients in the virtual training scene and feedback relevant data of the dangerous modules;

[0077] A safety factor module, which is mainly used to evaluate the safety of the mine based on the marked quantity of each component module in the mine and obtain the safety factor;

[0078] A processor, which is mainly used for converting design drawing data into a 3D coordinate system, performing partial model rotation operations and calculating the mine safety factor.

[0079] A virtual training method for safe production in underground mine scenarios, comprising:

[0080] Step 1: Create a model library to store preset component models through the model library. The component models include mine models, tunnel models, and equipment models;

[0081] Step 2: Based on the mining shaft excavation design drawing underground in the mine, obtain the spatial orientation of each component model and generate a virtual training scene underground in the mine;

[0082] Step 3: Based on the received training instruction, obtain the virtual training scene from the scene library according to the training instruction and display it on the interaction module;

[0083] Step 4: Based on the operation feedback signal of the training miners, adjust and transform the virtual training scene, and mark the component models with a high risk coefficient in the virtual training scene;

[0084] Step 5: Based on the marked quantity of each component module, judge the safety condition of each component model underground in the mine, calculate the average marked quantity in the virtual training scene, and comprehensively judge the mine safety coefficient.

[0085] Refer to Figure 2 As shown, create a model library to store preset component models through the model library. The component models include mine models, tunnel models, and equipment models specifically include:

[0086] Create the database structure of the model library and record the metadata of each model;

[0087] Design the directory structure to manage and store model files;

[0088] Classify the complexity of the virtual teaching scene based on the number of equipment models in the virtual training scene. The complexity includes low, medium, and high.

[0089] Refer to Figure 3 As shown, based on the mining shaft excavation design drawing underground in the mine, obtain the spatial orientation of each component model and generate a virtual training scene underground in the mine specifically include:

[0090] Based on the mining shaft excavation design drawing underground in the mine, obtain the cross-sectional dimensions, excavation depth, inclination angle of the mine shaft, and curve and slope changes of the tunnel;

[0091] Build a model based on the three-dimensional Cartesian coordinate system to represent the spatial orientation of each component model in the mine shaft;

[0092] Standardize the data in the design drawing and unify all data into values in the standard coordinate system;

[0093] Convert the position of each component model on the design drawing into the 3D coordinate system. The formula is:

[0094] x = x 0 +L·cos(α)

[0095] y = y 0 + L·cos(α)

[0096] z = z 0 - D

[0097] Where L is the length of the component in the plan view, α is the orientation angle of the component in the design drawing, and D is the relative depth of the component;

[0098] Based on the partial model affected by spatial rotation during the conversion to 3D, perform component rotation operations;

[0099] Based on all component models being placed in their corresponding positions, obtain the virtual training scenario for the entire underground mine.

[0100] It can be understood that the virtual training scenario for the underground mine may have performance bottlenecks during rendering due to complex models, lighting, materials, etc., affecting the user experience. Therefore, it is necessary to optimize the models in the virtual scenario, reduce unnecessary details, use LOD technology to dynamically adjust the details of the models according to the viewing distance, use a real-time rendering engine for efficient rendering, and combine technologies such as lighting optimization and texture compression to improve the rendering efficiency, and use a hierarchical loading technology to gradually load the scene content according to the user's location and needs, avoiding loading too many resources at once.

[0101] Refer to Figure 4 As shown, based on the partial model affected by spatial rotation during the conversion to 3D, the specific steps of performing component rotation operations include:

[0102] Based on the x, y, and z axes of the three-dimensional Cartesian coordinate system, obtain the rotation matrices respectively;

[0103] When rotating around the x-axis, the rotation matrix is:

[0104]

[0105] When rotating around the y-axis, the rotation matrix is:

[0106]

[0107] When rotating around the z-axis, the rotation matrix is:

[0108]

[0109] Where θ is the rotation angle.

[0110] Refer to Figure 5 As shown, based on the received training instructions, obtaining the virtual training scenario from the scenario library according to the training instructions and displaying it on the interaction module specifically includes:

[0111] Based on the operation steps underground in the mine, different-stage training scenarios and component models are presented to the trainee miners.

[0112] Based on the different-stage training scenarios and component models, the trainee miners perform corresponding operations and send operation signals for subsequent processing.

[0113] Refer to Figure 6 As shown, based on the operation feedback signals of the trainee miners, the virtual training scenario is adjusted and transformed, and the component models with high risk coefficients in the virtual training scenario are marked. Specifically, it includes:

[0114] Based on the operation feedback signals of the trainee miners, their position information, status information, and operation data are obtained.

[0115] Based on the obtained position information, substituting it into the modeling of the three-dimensional Cartesian coordinate system to determine whether the training scenario of the current trainee miner needs to change. If it is in the edge area of the component model, then change its training scenario; otherwise, do not change.

[0116] Based on the obtained status information, according to the physical sign feedback of the miner, the environmental conditions in the scenario are adjusted.

[0117] Based on the obtained operation data, the components with relatively high risk coefficients in the training scenario are marked, including highlighting, popping up a prompt box, and displaying a warning sign.

[0118] Based on the components with relatively high marked risk coefficients, the markings in the scenario are updated in real time to prompt the miners to pay attention.

[0119] It can be understood that the adjustment of the virtual training scenario depends on the operation feedback signals of the miners. However, if these feedback signals do not match the actual operation ability or skills, the scenario adjustment may lose its practical training value. Different skill levels can be set for the miners, and according to their skill levels, the complexity of the virtual scenario and the feedback sensitivity are adjusted. Avoid setting training tasks with too high difficulty for beginners. At the same time, customize training tasks according to the personal progress of the miners, and dynamically adjust the task difficulty and safety prompts in the scenario to adapt to miners with different operation levels.

[0120] Refer to Figure 7 As shown, based on the marked quantity of each component module, the safety conditions of each component model underground in the mine are judged, and the average marked quantity in the virtual training scenario is calculated to comprehensively judge the mine safety coefficient. Specifically, it includes:

[0121] Based on the marked quantity of each component module, a threshold is set to judge the current safety conditions of each component model.

[0122] Obtain the total sum of the marked quantities of all components and calculate the average marked quantity.

[0123] Based on the weighted average calculation of the number of markings of each component, the safety factor K of the current mine is obtained, and the formula is:

[0124]

[0125] where m is the number of components, ω i is the weight of the i-th component, and S i is the safety level of the i-th component, specifically the importance of the component or its functional importance in the mine.

[0126] Furthermore, the method according to the embodiment of the present application can also be implemented by means of Figure 8 the architecture of the electronic device shown. As Figure 8 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method and system for virtual safety production training in a mine underground scenario provided by the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 8 the architecture shown is only exemplary. When implementing different devices, one or more components shown in the Figure 8 electronic device may be omitted according to actual needs.

[0127] Figure 9 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 9 shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, a method and system for virtual safety production training in a mine underground scenario according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0128] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0129] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A virtual training method for safe production in underground mine scenes, characterized in that: include: Create a model library to store preset component models, including mine models, tunnel models and equipment models; Based on the mine excavation design drawings, the spatial orientation of each component model is obtained, and a virtual training scene underground in the mine is generated; Based on the received training instructions, a virtual training scene is obtained from a scene library according to the training instructions, and displayed on the interactive module; Based on the operational feedback signals from the trainees, the virtual training scene is adjusted and transformed, and the component models with high risk factors in the virtual training scene are marked; Based on the number of annotations of each component module, the safety status of each component model in the mine is judged, and the average number of annotations in the virtual training scene is calculated to comprehensively judge the safety factor of the mine.

2. A virtual safety training method for underground mine scenes according to claim 1, characterized in that: The model library is created to store preset component models, wherein the component models include a mine model, a tunnel model and an equipment model. Specifically, the component models include: Create the database structure of the model library and record the metadata of each model; Design directory structure, manage and store model files; The complexity of virtual teaching scenes is classified into low, medium and high levels based on the number of equipment models in the virtual training scenes.

3. The method for virtual training of safe production in underground mine scenes according to claim 1 is characterized in that: The method of obtaining the spatial position of each component model based on the mine tunnel excavation design drawing and generating a virtual training scene in the mine specifically includes: Based on the mine excavation design drawing, obtain the cross-sectional dimensions, excavation depth and inclination angle of the mine, and the curve and slope changes of the tunnel; Modeling is done based on a three-dimensional Cartesian coordinate system to represent the spatial orientation of each component model in the mine; Standardize the data in the design drawing and unify all the data into values ​​in the standard coordinate system; Convert the position of each component model on the design drawing into the 3D coordinate system using the following formula: x=x0+L·cos(α) y=y0+L·cos(α) z=z0-D Where L is the length of the component in the plan view, α is the orientation angle of the component in the design drawing, and D is the relative depth of the component; Perform component rotation operations based on the parts of the model that are affected by spatial rotation during the 3D conversion process; Based on the placement of all component models in corresponding positions, a virtual training scene of the entire underground mine is obtained.

4. A virtual safety production training method for underground mine scenes according to claim 3, characterized in that: The component rotation operation based on the partial model affected by the spatial rotation during the 3D conversion specifically includes: Based on the x, y, and z axes of the three-dimensional Cartesian coordinate system, obtain the rotation matrix respectively; Rotate around the x-axis, the rotation matrix is: Rotate around the y-axis, the rotation matrix is: Rotate around the z-axis, the rotation matrix is: Here, θ is the rotation angle.

5. The method for virtual training of safe production in underground mine scenes according to claim 1 is characterized in that: The receiving training instruction, obtaining a virtual training scene from a scene library according to the training instruction, and displaying it on the interactive module specifically includes: Based on the underground operation steps, different training scenarios and component models are provided to the trainees; Based on the training scenarios and component models at different stages, the trained miners make corresponding operations and send operation signals for subsequent processing.

6. The method for virtual training of safe production in underground mine scenes according to claim 1 is characterized in that: The method of adjusting and transforming the virtual training scene based on the operation feedback signal of the trainee miner and marking the component models with high risk factors in the virtual training scene specifically includes: Based on the operational feedback signals of the trained miners, their position information, status information and operational data are obtained; Based on the acquired position information, it is substituted into the modeling of the three-dimensional Cartesian coordinate system to determine whether the current training scene of the miner needs to be changed. If it is in the edge area of ​​the component model, the training scene is changed, otherwise it is not changed; Based on the acquired status information and the miners’ physical feedback, the environmental conditions in the scene are adjusted; Based on the acquired operation data, mark the parts with higher risk factors in the training scenario, including highlighting, pop-up prompt boxes, and displaying warning signs; Based on the marked components with higher risk factors, the annotations in the scene are updated in real time to remind miners to pay attention.

7. The method for virtual training of safe production in underground mine scenes according to claim 1 is characterized in that: The safety status of each component model in the mine is judged based on the number of annotations of each component module, and the average number of annotations in the virtual training scene is calculated to comprehensively judge the safety factor of the mine, which specifically includes: Based on the number of annotations of each component module, set a threshold to determine the safety status of each component model; Get the sum of the number of annotations of all parts and calculate the average number of annotations; Based on the weighted average calculation of the number of marks on each component, the safety factor K of the current mine is obtained. The formula is: Where m is the number of components, ω i is the weight of the i-th component, S i is the safety level of the i-th component, specifically the importance of the component or its functional importance in the mine.

8. A virtual safety production training system for underground mine scenes, used to implement a virtual safety production training method for underground mine scenes as described in any one of claims 1 to 7, characterized in that: include: A model library module, wherein the model library module is mainly used to store preset component models, and the component models include mine models, tunnel models and equipment models; A scenario construction module, which is mainly used to convert the data in the underground mine excavation design drawing into a 3D coordinate system and generate a training scenario; A model rotation module, which is mainly used to perform component rotation operations on a part of the model affected by spatial rotation and convert it into a normal angle; An interactive module, which is mainly used for training miners to obtain the implemented training scenarios and send current status and operation data; A marking module, which is mainly used to mark component models with high risk factors in the virtual training scene and feed back relevant data of the risk module; A safety factor module, which is mainly used to evaluate the safety of the mine based on the number of annotations of each component module in the mine to obtain a safety factor; The processor is mainly used for converting design drawing data into a 3D coordinate system, performing partial model rotation operations and calculating a mine safety factor.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a virtual training method for safe production in underground mine scenes as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, a virtual training method for safe production in an underground mine scene according to any one of claims 1 to 7 is implemented.

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