Mine teaching system and method based on multi-scene collaborative simulation of digital twinning

Abstract

The present application relates to the technical field of virtual reality, in particular to a kind of multi-scene collaborative simulation mine teaching system and method based on digital twinning, the multi-scene collaborative simulation mine teaching method based on digital twinning of the present application includes: according to the arrangement form of mining and preparation roadway, generate the virtual mine space containing large-dip-angle coal seam roadway model;Obtain the virtual model of working face equipment as the digital twin of working face equipment;Obtain the real-time model parameter of the physical model of working face equipment in different scenes;According to the real-time model parameter, the digital twin of working face equipment is updated online to obtain the updated digital twin;According to the updated digital twin, the corresponding virtual three-dimensional model of working face equipment is mapped in the virtual mine space, and the simulation walking platform is controlled to tilt;Machine learning algorithm evaluates working face equipment operation according to the real-time model parameter.The present application can improve the experience of students.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of virtual reality, and particularly relates to a multi-scene collaborative simulation mine teaching system and method based on digital twinning. BACKGROUND

[0002] Large-dip-angle coal seam mining is a typical operation scene in western China, and its working face arrangement and mining technology are significantly different from that of gently inclined coal seams. Mining engineering teachers generally use various mining and excavation systems models to conduct practical teaching for students and trainees in schools. However, the existing mining engineering teaching has the following problems:

[0003] Physical models are scattered: models such as coal mining machines and hydraulic supports are scattered in different laboratories, and it is difficult to build a complete underground operation scene;

[0004] Large-dip-angle display difficulty: traditional physical models are difficult to simulate the complex geological conditions of large-dip-angle coal seams;

[0005] Teaching space limitations: limited laboratory space, students cannot fully experience the real mine environment. SUMMARY

[0006] Therefore, the embodiments of the application provide a multi-scene collaborative simulation mine teaching system and method based on digital twinning to solve the technical problem that the laboratory space is limited and students cannot fully experience the real mine environment.

[0007] The technical scheme adopted by the application is:

[0008] In a first aspect, the application provides a multi-scene collaborative simulation mine teaching method based on digital twinning, which comprises:

[0009] S1: generating a virtual mine space containing a large-dip-angle coal seam roadway model according to the development and preparation roadway arrangement form of an actual production large-dip-angle coal seam mine;

[0010] S2: obtaining a virtual model of the working face equipment as a digital twin of the working face equipment;

[0011] S3: obtaining real-time model parameters of a plurality of working face equipment physical models in different scenes;

[0012] S4: obtaining an updated digital twin after online updating the digital twin of the working face equipment according to the real-time model parameters;

[0013] S5: mapping a corresponding virtual three-dimensional model of the working face equipment in the virtual mine space according to the updated digital twin;

[0014] S6: evaluating the working face equipment operation according to the real-time model parameters by using a machine learning algorithm.

[0015] Preferably, the method further comprises:

[0016] S57: obtaining a tilt parameter of a roadway model in the virtual mine space;

[0017] S58: determining a tilt angle of the simulation walking platform according to the virtual three-dimensional model position and the tilt parameter of the roadway model;

[0018] S59: controlling the simulation walking platform to tilt in real time according to the tilt angle of the simulation walking platform.

[0019] Preferably, the S4: obtaining an updated digital twin after online updating the digital twin of the working face equipment according to the real-time model parameters comprises:

[0020] S401: obtaining a safety protection range of each working face equipment;

[0021] S402: determining a safety protection range of the updated digital twin according to the safety protection range of each working face equipment and the corresponding updated digital twin model;

[0022] S403: judging whether there is mutual interference between the updated virtual three-dimensional models according to the safety protection range of each updated digital twin and the position of the corresponding virtual three-dimensional model in the virtual mine space;

[0023] S403: if there is no mutual interference, executing S5;

[0024] S404: if there is mutual interference, pausing the updating of the virtual three-dimensional model, and issuing an alarm information according to the interference situation.

[0025] Preferably, the S6: evaluating the working face equipment operation according to the real-time model parameters by using a machine learning algorithm comprises:

[0026] S61: obtaining real-time model parameters in the process of the trainee operating the physical model of the working face equipment in each scene;

[0027] S62: obtaining a support vector machine algorithm model trained by data;

[0028] S63: the support vector machine algorithm model evaluates the trainee's coal mining operation according to the input model parameters.

[0029] Preferably, the S62: obtaining a support vector machine algorithm model trained by data comprises:

[0030] S621: Obtain data of a plurality of sensors for detecting model parameters when operating the working face equipment, and divide the sensor data into an independent operation data group and a multi-scenario cooperative operation data group;

[0031] S622: Obtain a support vector machine algorithm model corresponding to an independent operation evaluation item and a support vector machine algorithm model corresponding to a multi-scenario cooperative operation evaluation item as a first support vector machine initial algorithm model and a second support vector machine initial algorithm model, respectively;

[0032] S623: Obtain an evaluation result of an evaluation item corresponding to the independent operation data group;

[0033] S624: Obtain an evaluation result of an evaluation item corresponding to the multi-scenario cooperative operation data group;

[0034] S625: Label each group of sensor data according to each evaluation result;

[0035] S626: Train the first support vector machine initial algorithm model through the labeled independent operation data group to obtain an independent operation evaluation algorithm model;

[0036] S627: Train the second support vector machine initial algorithm model through the labeled multi-scenario cooperative operation data group to obtain a multi-scenario cooperative operation evaluation algorithm model.

[0037] Preferably, the S63: The support vector machine algorithm model evaluates the student's coal mining operation according to the input model parameters, comprising:

[0038] S630: Obtain an independent operation data group in a data group generated by detecting real-time model parameters of the working face equipment;

[0039] S631: Obtain an independent operation evaluation algorithm model;

[0040] S632: Obtain a data class label corresponding to each data in the independent operation data group;

[0041] S633: Input the data in the independent operation data group and the corresponding data class label into the independent operation evaluation algorithm model;

[0042] S634: The independent operation evaluation algorithm model outputs a corresponding independent operation evaluation result according to the input data in the independent operation data group;

[0043] S635: Obtain a multi-scenario cooperative operation data group in a data group generated by detecting real-time model parameters of the working face equipment;

[0044] S636: Obtain a multi-scenario cooperative operation evaluation algorithm model;

[0045] S637: Obtain the data category label corresponding to each data in the multi-scenario cooperative operation data set;

[0046] S638: Input the data in the multi-scenario cooperative operation set and the corresponding data category label into the multi-scenario cooperative operation evaluation algorithm model;

[0047] S639: The multi-scenario cooperative operation algorithm model outputs the evaluation result of the corresponding multi-scenario cooperative operation according to the input data in the multi-scenario cooperative operation data set.

[0048] Preferably, the evaluation result of the independent operation includes whether the hydraulic support is in place, whether the hydraulic support guard plate is opened, whether the hydraulic support support resistance reaches the set value, whether the drum height of the coal mining machine is correct, whether the oblique cutting operation sequence of the coal mining machine is correct, whether the coal piling and material scattering of the scraper conveyor is correct, whether the flatness of the scraper conveyor meets the preset requirement, whether the end operation of the scraper conveyor is correct, whether the installation and removal operation of the end support is correct, whether the advance support matches the mining, and whether the advance support length meets the preset requirement.

[0049] The evaluation result of the multi-scenario cooperative operation includes whether the traction speed and position of the coal mining machine match the hydraulic support action, whether the timing of the coal cutting, support moving and pushing operation is correct, and whether the parallelism of adjacent hydraulic supports meets the preset requirement.

[0050] In a second aspect, the present application also provides a multi-scenario cooperative simulation mine teaching system based on digital twinning, which applies the teaching method of the first aspect, and the method comprises a working face equipment physical model, a cloud server and a digital twinning updating module.

[0051] Preferably, it also comprises a VR glasses, which is in communication connection with the cloud server, and the VR glasses receives the image data for displaying the virtual mine space sent by the cloud server.

[0052] Preferably, it also comprises a simulation walking platform, which is in communication connection with the cloud server, and the simulation walking platform is used for receiving the data for controlling the inclination angle of the simulation walking platform sent by the cloud server.

[0053] Beneficial effects: the multi-scene collaborative simulation mine teaching system and method based on digital twinning in the application generate a virtual mine space through the development and preparation roadway data of the actual mine, accurately reproduce the roadway slope of the large dip angle coal seam and the top and bottom plate form, so that students can intuitively understand the equipment arrangement logic under complex geological conditions. The virtual model is updated in real time through the sensor data of the physical model, ensuring that the virtual scene and the action of the physical equipment are synchronized. The real-time parameters of the coal mining machine, hydraulic support and other equipment scattered in different laboratories are integrated into a unified virtual scene, a complete coal mine operation process chain is constructed, the physical space fragmentation limit is broken through, and the multi-device collaborative process is completely presented. By using the machine learning algorithm to analyze the device operation data, the evaluation results are generated in real time, guiding the students to correct errors in time and improving the teaching accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. For those skilled in the art, other drawings can also be obtained without creative labor on the premise of these drawings, and these are within the protection scope of the application.

[0055] Figure 1 The flowchart of the multi-scene collaborative simulation mine teaching method based on digital twinning of the application;

[0056] Figure 2 The schematic diagram of part of the virtual space under the VR perspective of the application;

[0057] Figure 3 The schematic diagram of the coal mining operation in the virtual space under the VR perspective of the application;

[0058] Figure 4 The flowchart of the method for controlling the inclination of the simulation platform in the application;

[0059] Figure 5 The flowchart of the method for updating the digital twinning in the application;

[0060] Figure 6 The flowchart of the method for evaluating the working face device operation in the application;

[0061] Figure 7 The flowchart of the method for training the support vector machine algorithm model in the application;

[0062] Figure 8 The flowchart of the method for evaluating the coal mining operation by using the support vector machine algorithm in the application;

[0063] Figure 9 The structural block diagram of the multi-scene collaborative simulation mine teaching method based on digital twinning of the application. DETAILED DESCRIPTION

[0064] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be noted that, in this document, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or sequence between these entities or operations. In the description of the present application, it should be understood that the orientations or positional relationships indicated by the terms “center”, “upper”, “lower”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer” and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. Moreover, the terms “include”, “contain” or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement “include” do not exclude the presence of additional identical elements in the process, method, article or device that includes the elements. If there is no conflict, the embodiments of the present application and the various features in the embodiments can be combined with each other, and are all within the protection scope of the present application.

[0065] Embodiment 1

[0066] As shown in Figure 1 The present application provides a multi-scenario collaborative simulation mine teaching method based on digital twinning, which comprises:

[0067] S1: According to the development and preparation roadway layout form of the actual large-dip-angle coal seam mine, a virtual mine space containing a large-dip-angle coal seam roadway model is generated;

[0068] For example, the geological exploration data (coal seam dip angle, thickness, roof and floor lithology) of the actual large-dip-angle mine, the roadway layout map (development roadway, preparation roadway coordinates) can be obtained; the underground roadway point cloud data can also be obtained by three-dimensional laser scanning, and then the virtual mine space is constructed using a 3D engine according to these data, and the roadway three-dimensional grid can also be generated based on the fusion of BIM model and point cloud data. The large-dip-angle coal seam refers to a coal seam with a dip angle greater than 35 degrees.

[0069] This step generates a virtual mine space through the development and preparation of the actual mine roadway data, accurately reproduces the roadway slope of the large-dip-angle coal seam and the top and bottom plate form, and enables students to intuitively understand the equipment layout logic under complex geological conditions.

[0070] S2: Obtain a virtual model of the working face equipment as a digital twin of the working face equipment;

[0071] The working face equipment refers to various mechanical equipment and devices that directly participate in coal mining, support, transportation, and safety guarantee operations in the coal mining working face. These devices work together to complete the whole process production task from coal seam cutting to coal transportation, and are the core component of the mine production system. The working face equipment includes working face equipment such as a coal mining machine, which is responsible for cutting the coal seam and stripping the coal from the coal wall. Specifically, it includes drum-type coal mining machines, coal plows, continuous coal mining machines, and other types. Different types of coal mining machines are suitable for different coal seam thicknesses and inclination angles, such as large-dip-angle coal seams that require anti-skid braking devices. The working face equipment also includes support equipment such as hydraulic supports, which are used to support the roof and prevent the roof from collapsing; hydraulic supports include support-type supports suitable for stabilizing the roof, shield-type supports suitable for breaking the roof and providing lateral protection, and large-dip-angle special supports equipped with anti-skid devices. The working face equipment also includes advanced support equipment that supports the roadway in front of the coal mining face to prevent advanced pressure from damaging the roadway, including single hydraulic props, step-type advanced supports, unit supports, and other types. The working face equipment also includes transportation equipment such as a scraper conveyor, which is used to transport the coal cut by the coal mining machine to the roadway transfer point. The present embodiment can model various working face equipment in three dimensions as needed to obtain the corresponding virtual model of the working face equipment. The virtual model can be used as a digital twin of the working face equipment.

[0072] S3: Obtain real-time model parameters of a plurality of working face equipment physical models in different scenarios;

[0073] The physical model of the working face equipment in this embodiment is a real teaching training equipment that is made by imitating the structure and function of the working face equipment in the mine, and has basically the same structure and function as the actual working face equipment in the mine. Due to the complexity of the coal mining process, more working face equipment is needed, and due to the limitation of the site, different equipment is often distributed in different teaching scenes, so the model parameters of the working face equipment distributed in different scenes need to be collected in real time. When the trainees operate the physical model of the working face equipment distributed in different scenes, the state of the physical model of the working face equipment, such as the posture of the model, the relative position relationship between the components of the working face equipment model, and the relative posture between the components, will change. The aforementioned parameters reflecting the state of the physical model of the working face equipment, the relative position relationship between the components of the physical model of the working face equipment, and the relative posture between the components are the aforementioned parameters of the physical model. This step needs to collect the aforementioned model parameters in real time while the trainees are operating, so as to obtain the model parameters of the physical model of the working face equipment. Since these models are distributed in different scenes, the data collection in each scene needs to be synchronized in time.

[0074] S4: obtaining an updated digital twin after online updating the digital twin of the working face equipment according to the real-time model parameters;

[0075] This step updates the digital twin using the model parameters of the physical model of the working face equipment collected in real time, so that the model parameters of the digital twin are consistent with those of the physical model, and thus the state of the digital twin is consistent with that of the physical model of the working face equipment. Therefore, the state of the digital twin can accurately and timely reflect the operation of the trainees on the physical model of the working face equipment in each teaching scene.

[0076] S5: mapping a corresponding virtual three-dimensional model of the working face equipment in the virtual mine space according to the updated digital twin, and controlling the simulation walking platform to tilt according to the position of the virtual three-dimensional model in the virtual mine space;

[0077] In this embodiment, the initial virtual three-dimensional model of the working face equipment can be imported into the virtual mine space generated in the previous step in advance. Then, the virtual three-dimensional model of the working face equipment in the virtual mine space is updated in time using the updated digital twin after the digital twin is updated, so that the state of the virtual three-dimensional model of the working face equipment in the virtual mine space is synchronized with the state of the physical model of the working face equipment operated by the trainees. Therefore, the trainees can experience the effect of operating the working face equipment in the virtual mine, and the trainees in each teaching scene can have the experience of coal mining in the actual mine space, as shown in Figure 2 andFigure 3 As a large-inclination coal seam tunnel is generated in the virtual space, the inclination state of the virtual three-dimensional model of the working face equipment when it is in the large-inclination coal seam tunnel is different, and the embodiment uses the simulation walking platform to simulate the inclination scene of the large-inclination coal seam tunnel.

[0078] This step updates the virtual model in real time through the sensor data of the physical model (such as the inclination angle of the hydraulic support and the cutting depth of the coal mining machine), and ensures that the virtual scene is synchronized with the action of the physical equipment. The real-time parameters of the coal mining machine, hydraulic support and other equipment scattered in different laboratories are integrated into a unified virtual scene, a complete coal mine operation process chain is constructed, the physical space fragmentation limit is broken, and the multi-device collaborative process is completely presented.

[0079] S6: using a machine learning algorithm to evaluate the coal mining operation according to the real-time model parameters.

[0080] This embodiment can update the digital twin in real time using real-time model parameters, and update the virtual three-dimensional model of the working face equipment in the virtual mine using the digital twin in real time, while using the collected model parameters of the physical model of the working face equipment to timely evaluate the coal mining operation of the personnel trained in the scene, so that the trainees can obtain feedback on the training effect in a timely manner while training in the virtual mine space, thereby more conducive to the trainees to find their own shortcomings and make targeted improvements, and the training effect can be significantly improved.

[0081] This step uses a support vector machine algorithm to analyze the equipment operation data, and generates an evaluation result in real time to guide the trainees to correct errors in a timely manner and improve the accuracy of teaching.

[0082] As shown in Figure 4 The multi-scene collaborative simulation mine teaching method based on digital twin in the embodiment comprises the following steps:

[0083] S57: obtaining an inclination parameter of a tunnel model in the virtual mine space;

[0084] The inclination parameter of the tunnel model includes geometric feature parameters such as the slope angle, the strike azimuth and the radius of curvature of the tunnel in the virtual mine space. The slope angle is the angle between the tunnel floor and the horizontal plane, ranging from 0° to 90°, and a large-inclination coal seam is usually defined as 35° to 45°.

[0085] The strike azimuth is the angle between the extension direction of the tunnel and the north direction, which is used to determine the spatial orientation of the virtual scene.

[0086] S58: determining the inclination angle of the simulation walking platform according to the position of the virtual three-dimensional model and the inclination parameter of the tunnel model;

[0087] The simulation walking platform is a somatosensory simulation device with a dynamically adjustable inclination angle, usually a six-degree-of-freedom (6-DOF) hydraulic drive or electric cylinder drive platform, which can simulate the slope and vibration somatosensory feedback during walking in the underground.

[0088] S59: controlling the inclination of the simulation walking platform according to the inclination angle of the simulation walking platform.

[0089] According to the virtual roadway slope parameters, the pitch and roll angles of the simulation walking platform are adjusted in real time through the PID algorithm, so that the somatosensory experience of the trainee is consistent with the visual experience of the virtual scene.

[0090] The embodiment synchronizes the visual-somatosensory experience of the simulation platform inclination with the virtual scene, so that the trainee can experience the difficulty of walking in a large-inclination roadway, and solves the problem of "visual-somatosensory disconnection" in traditional VR teaching. When the trainee operates on the inclined platform, the body posture and equipment control strategy need to be adjusted synchronously, which improves the spatial coordination ability in complex environments. The embodiment realizes the deep coupling of virtual scene and somatosensory feedback through the way of roadway inclination parameter extraction-kinematics mapping-high-precision closed-loop control. Compared with traditional fixed platforms or pure VR teaching, not only can it stimulate the teaching immersion in multiple dimensions, but also can make the training safe, simulate extreme working conditions in a controlled environment, and realize the stereoscopic evaluation, such as supplementing the operation evaluation dimension through body data (such as center of gravity offset).

[0091] As shown in Figure 5 To solve the problem of interference of working face equipment in different scenes after multiple scenes are fused into the virtual mine space, the S4: online updating of the digital twin of the working face equipment according to the real-time model parameters includes:

[0092] S401: obtaining the safety protection range of each working face equipment;

[0093] The safety protection range of the working face equipment is based on the inherent safety boundary defined by the mechanical structure of the equipment, such as the minimum distance between the top beam of the hydraulic support and the drum of the coal mining machine, which should be greater than the safety distance.

[0094] S402: determining the safety protection range of the updated digital twin according to the safety protection range of each working face equipment and the corresponding updated digital twin model;

[0095] The safety protection range of the digital twin is consistent with the safety protection range of the corresponding working face equipment.

[0096] S403: Determine whether there is mutual interference between the updated virtual three-dimensional models according to the safety protection range of each updated digital twin and the position of the virtual three-dimensional model corresponding to the updated digital twin in the virtual mine space; the aforementioned interference refers to the overlap or intrusion of the safety protection range of two or more devices in the virtual mine space, which may cause the following risks:

[0097] Geometric interference: device model collision body directly contacts, such as intrusion of the drum of the coal mining machine into the area without moving the support;

[0098] Process interference: violation of the coordinated operation rules, such as starting the coal mining machine cutting when the pre-support is not completed.

[0099] S403: If there is no mutual interference, execute S5;

[0100] If there is no mutual interference, the three-dimensional model in the virtual mine space is updated normally, including:

[0101] According to the real-time model parameters, obtain the real-time elastic adjustment amount of the safety protection range of the virtual three-dimensional model;

[0102] According to the safety protection range of the digital twin and the dynamic adjustment amount, determine the elastic boundary of the safety protection of the virtual three-dimensional model;

[0103] Since the working face equipment in the coal mining operation is often in a state of continuous motion, if a fixed boundary is used as the safety range, it is difficult to adapt to the dynamic changes of the working face equipment in the coal mining operation, resulting in a too conservative safety range and affecting the precise operation, or a too small safety range of the working face equipment in high-speed motion, which makes it unable to handle unexpected situations. To this end, the embodiment adjusts the static safety protection range according to the real-time model parameters reflecting the real-time state of the working face equipment (such as the cutting speed of the coal mining machine, the support force of the support), so that it can adapt to the running state of the working face equipment. In this way, precise operation can be completed, and the problem of insufficient emergency handling time is avoided. The working face real-time parameters used to determine the dynamic adjustment amount include the cutting speed of the coal mining machine, the drum height, the motor current / temperature; hydraulic support: support pressure, push travel, inclination angle; conveyor: chain speed, load torque, vibration frequency.

[0104] The specific real-time dynamic adjustment amount can be obtained by multiplying a correction factor on the basis of using a fixed boundary as a safety range, wherein the correction factor includes a speed influence factor, a load influence factor, and a geological attenuation influence factor. In this way, the same working face equipment can have different dynamic protection ranges under different dynamic conditions, so that the safety protection range can adapt to the actual running state of the working face equipment.

[0105] According to the real-time model parameter, a dynamic range of the digital twin in a future preset time range is obtained;

[0106] The dynamic range in the future preset time range refers to a possible operation range of the digital twin in a future time range.

[0107] According to the dynamic range and the elastic boundary, a time dynamic elastic boundary of the virtual three-dimensional model is determined;

[0108] For example, the elastic boundary of the future 5s is pre-calculated at an interval of 100ms, and a continuous space-time body is generated as the time dynamic elastic boundary. When the working face equipment runs at a high speed, the dynamic elastic boundary of time will also be expanded, thereby reasonably improving the safety range. In the case that the model parameters at different times in the future time range are dynamically changed, the model parameters at a certain time in the future can be predicted according to the current real-time model parameters, and the time dynamic elastic boundary corresponding to the time is generated according to the predicted model parameters.

[0109] According to the time dynamic elastic boundary of the virtual three-dimensional model and the position of the virtual three-dimensional model in the virtual mine space, it is judged whether there is mutual interference between the updated virtual three-dimensional models;

[0110] The mutual interference detection can include hard collision detection. When the two device safety voxels overlap in the same space-time domain, it is judged that a hard collision occurs.

[0111] Soft conflict detection: the dynamic elastic boundaries of the virtual three-dimensional models of the two working face equipments continuously overlap for more than a set time, which is judged as a soft conflict;

[0112] Environment coupling conflict: the intersection of the device safety voxel and the roadway deformation voxel is greater than a set threshold.

[0113] S404: if there is no mutual interference, S5 is performed;

[0114] S405: if there is mutual interference, the update of the virtual three-dimensional model is suspended, the digital twin is rolled back to a safe state according to the interference condition, and an alarm information is issued according to the interference condition.

[0115] The alarm information issuing mode includes:

[0116] Visual warning: the interference area is highlighted (red flashing) in the virtual scene;

[0117] Force feedback: simulate the collision vibration through a touch device;

[0118] Voice prompt: broadcast the violation type (such as "E203: coal mining machine enters an unsupported area")

[0119] S406: Reverse update the working face equipment physical model according to the digital twin in the safe state and start the degraded operation mode.

[0120] The embodiment makes the digital twin rollback to the safe state after detecting the interference, and reversely updates the working face equipment using the digital twin in the safe state, so that the working face equipment physical model does not continue to run in the interference state, thereby avoiding the trainee training in the error condition. In the degraded operation mode, the running speed of the physical model is limited within a certain range. The embodiment maps the working face equipment in different scenes to the same virtual mine space, and avoids the interference caused by unreasonable operation of the actual physical model in different scenes through interference detection, so that the training environment is closer to the real environment.

[0121] As shown in Figure 6 S6: using a machine learning algorithm to evaluate the coal mining operation according to the real-time model parameters, including:

[0122] S61: obtaining the real-time model parameters of the trainee operating the working face equipment physical model in each scene;

[0123] In the embodiment, the real-time model parameters of the coal equipment physical model include but are not limited to the height of the hydraulic support column, the angle of the hydraulic support guard plate, the position of the hydraulic support, the angle of the coal mining machine rocker arm, the position of the coal mining machine, the traction speed of the coal mining machine, and the speed of the belt conveyor.

[0124] The embodiment can install sensors on the set positions of each working face equipment physical model, and use these sensors to collect data reflecting the model parameters to accurately obtain the real-time model parameters of the working face equipment physical model when the trainee performs the coal mining operation. For example, for the hydraulic support, displacement sensors and pressure sensors can be installed on the column, angle sensors can be installed on the guard plate hinge, displacement sensors can be installed on the front cantilever, and displacement sensors can be installed on the jack; for the coal mining machine, angle sensors can be installed on the rocker arm, and speed sensors can be installed on the drum. Position sensors can also be installed on the scraper conveyor.

[0125] S62: obtaining a support vector machine algorithm model trained by data;

[0126] Before using the support vector machine algorithm model for evaluation and analysis, the support vector machine algorithm model is trained by historical data collected by the sensor in this step. Through data training, the parameters in the support vector machine algorithm model are continuously optimized, so that it can accurately evaluate the coal mining operation of the trainee according to the data collected by the sensor.

[0127] S63: The support vector machine algorithm model outputs an evaluation of the learner's coal mining operation according to the input model parameters. After obtaining the data reflecting the model parameters detected by the sensor, the data is input into the trained machine learning algorithm model, and the machine learning algorithm outputs an evaluation result of the learner's coal mining operation according to the input data. The model parameters can indirectly reflect the learner's operation of the working face equipment, but the evaluation of whether the learner's operation meets the standard operation requirements will be affected by multiple model parameters, and different model parameters will also affect each other, so there is a relatively complex nonlinear relationship between the model parameters and the evaluation result. In this embodiment, the support vector machine algorithm in the machine learning algorithm is used to process the data collected by the sensor to obtain the evaluation result of the learner's coal mining operation.

[0128] The S63: The support vector machine algorithm model outputs an evaluation of the learner's coal mining operation according to the input model parameters further comprises:

[0129] S631: Obtain historical data of the state vector of the simulation walking platform;

[0130] The state vector of the simulation walking platform includes the running speed of the conveyor belt of the simulation walking platform, the rotation angle of the turntable, the roll angle of the platform, the pitch angle of the platform, and other state parameters. In specific implementation, data reflecting the foregoing state parameters can be collected.

[0131] S632: Obtain the optimal classification boundary of the support vector machine algorithm model corresponding to the historical data;

[0132] For any set of collected historical data, find the optimal classification boundary of the support vector machine algorithm model corresponding to the data. The optimal classification boundary can be set as needed, for example, the scale parameter of the kernel function is set to a small value to keep the classification boundary smooth when the platform is stable, and the scale parameter of the kernel function is set to a large value to increase the flexibility of the classification boundary when the platform is unstable, to adapt to complex data distribution. The stability of the platform can be measured by the rate of change of the platform state parameters. When the inclination of the platform is large, the bias term of the kernel function is set to offset the classification boundary in the direction of inclination, to adapt to the change of data distribution.

[0133] S633: Obtain the mapping relationship between the ideal scale parameter and the bias term in the support vector machine and the state vector of the simulation walking platform according to the historical data and the optimal classification boundary;

[0134] In specific implementation, the historical data of the platform state vector and the scale parameter and the bias term under the corresponding optimal classification boundary can be used for regression analysis to obtain the mapping relationship between the ideal scale parameter and the bias term and the state vector of the simulation walking platform.

[0135] S634: Obtain a state vector of the simulation walking platform when a target state change rate of the simulation walking platform reaches a set threshold value;

[0136] The target state of the simulation walking platform can be selected as required, for example, one or more of the running speed of the platform conveyor belt, the rotation angle of the rotating disc, the roll angle of the platform, and the pitch angle of the platform can be selected as the target state. The change amount of the selected target state in a unit time is the target state change rate. When one or more of the target state change rates exceeds the set threshold value, the dynamic adjustment of the classification boundary can be started, and the current state vector of the simulation walking platform at the current time is obtained.

[0137] S635: Determine a current ideal scale parameter and a current bias term according to the current state vector and the mapping relationship;

[0138] This step finds the optimal scale parameter and bias term corresponding to the current state according to the current state vector through the mapping relationship as the current ideal scale parameter and the current bias term.

[0139] S636: Adjust the classification boundary of the support vector machine algorithm model according to the current ideal scale parameter and the bias term.

[0140] After updating the support vector machine algorithm model with the current ideal scale parameter and the bias term, the classification boundary is adjusted.

[0141] In this embodiment, the classification boundary of the support vector machine algorithm model is adjusted according to the platform running state to avoid false classification caused by the platform state. Since the platform running state reflects the environmental state of the user in the virtual mine space, such evaluation method can be adaptively adjusted according to the environment of the user, thereby making the evaluation method more scientific.

[0142] In this embodiment, the S6: evaluating the coal mining operation according to the real-time model parameter by using the machine learning algorithm comprises:

[0143] As shown in FIG. Figure 7 In this embodiment, the S62: obtaining the support vector machine algorithm model for evaluation after training the initial support vector machine algorithm model by using the training data with labels comprises:

[0144] S621: Obtain data of a plurality of sensors for detecting model parameters when operating the working face equipment, and divide the sensor data into independent operation data groups and multi-scenario cooperative operation data groups;

[0145] The embodiment detects and collects model parameters by sensors installed at the setting positions of each working face equipment. The embodiment can evaluate the training effect of the training personnel from two aspects of the operation specification of a single working face equipment and the coordinated operation specification of the working face equipment in multiple different teaching scenarios. To this end, the collected sensor data is divided into an independent operation data group and a multi-scenario coordinated operation data group, which are respectively used for the evaluation of the operation specification of a single working face equipment and the coordinated operation specification of the working face equipment in multiple different teaching scenarios.

[0146] S622: Obtain a support vector machine algorithm model corresponding to the independent operation evaluation item and a support vector machine algorithm model corresponding to the multi-scenario coordinated operation evaluation item as a first support vector machine initial algorithm model and a second support vector machine initial algorithm model respectively.

[0147] The independent operation evaluation item is used for evaluating the operation specification of a single working face equipment, and the multi-scenario coordinated operation evaluation item is used for evaluating the coordinated operation specification of the working face equipment in multiple different teaching scenarios.

[0148] S623: Obtain the evaluation result of the evaluation item corresponding to the independent operation data group.

[0149] In the specific implementation of the step, an operator can operate the working face equipment, then obtain the data of the working face equipment related parameters detected by the sensor during the operation, and determine whether the operation meets the corresponding requirements according to the operation specification of a single working face equipment, and the determination result is used as the evaluation result of the corresponding sensor data.

[0150] S624: Obtain the evaluation result of the evaluation item corresponding to the multi-scenario coordinated operation data group.

[0151] In the specific implementation of the step, multiple operators in different scenarios can operate the working face equipment, then obtain the data of the working face equipment related parameters detected by the sensor during the operation, and determine whether the operation meets the corresponding requirements according to the coordinated operation specification of the working face equipment in multiple different teaching scenarios, and the determination result is used as the evaluation result of the corresponding sensor data.

[0152] S625: Label each group of sensor data according to each evaluation result.

[0153] S626: Train the first support vector machine initial algorithm model by the labeled independent operation data group to obtain an independent operation evaluation algorithm model.

[0154] S627: Train the second support vector machine initial algorithm model by the labeled multi-scenario coordinated operation data group to obtain a multi-scenario coordinated operation evaluation algorithm model.

[0155] The step trains the support vector machine algorithm through the labeled sensor data, optimizes the parameters of the model until the evaluation effect of the machine learning algorithm model meets the requirements. Different support vector machines use different data sets for training.

[0156] As shown in Figure 8 In the embodiment, the S63: inputting the real-time model parameters into the support vector machine algorithm model to obtain the evaluation result of the coal mining operation includes:

[0157] S630: obtaining an independent operation data set in a generated data set of the detected working face equipment real-time model parameters;

[0158] S631: obtaining an independent operation evaluation algorithm model;

[0159] S632: obtaining a data category label corresponding to each data in the independent operation data set;

[0160] The data category label includes a positive class (represented by a numerical value +1) and a negative class (represented by a numerical value -1).

[0161] The method of obtaining the data category label corresponding to the sensor data can use a threshold method, that is, a threshold is set according to the safety operation specification of the equipment, and the category is directly judged through the sensor data. For example, if the data of the pressure sensor on the hydraulic support column exceeds the rated range, it is marked as a negative class, and if it does not exceed the rated range, it is marked as a positive class. In addition, the sensor data can be input into a deep learning algorithm model to automatically output the data category label corresponding to the sensor data by the deep learning algorithm model.

[0162] S633: inputting the data in the independent operation data set and the corresponding data category label into the independent operation evaluation algorithm model;

[0163] S634: the independent operation evaluation algorithm model outputs the evaluation result of the corresponding independent operation according to the input data in the independent operation data set;

[0164] Specifically includes the following steps:

[0165] Obtaining a support vector corresponding to the independent operation data;

[0166] Calculating the kernel function value of the support vector;

[0167] Multiplying each kernel function value with the corresponding Lagrange multiplier and class label (if the class label is a positive class, multiply by +1, if the class label is a negative class, multiply by -1), and then adding all the multiplied results;

[0168] Adding a bias term to the added result to obtain the calculation result.

[0169] S635: Obtain multi-scene collaborative operation data sets in the generated data set of detecting real-time model parameters of the working face equipment;

[0170] S636: Obtain a multi-scene collaborative operation evaluation algorithm model;

[0171] S637: Obtain data category labels corresponding to each data in the multi-scene collaborative operation data set;

[0172] S638: Input the data in the multi-scene collaborative operation set and the corresponding data category labels into the multi-scene collaborative operation evaluation algorithm model;

[0173] S639: The multi-scene collaborative operation algorithm model outputs the corresponding multi-scene collaborative operation evaluation results according to the input data in the multi-scene collaborative operation data set, specifically including the following steps:

[0174] Obtain support vectors corresponding to the multi-scene collaborative operation data;

[0175] Calculate the kernel function values of the support vectors;

[0176] Multiply each kernel function value with the corresponding Lagrange multiplier and category label (multiply by +1 if the category label is positive, and multiply by -1 if the category label is negative), and then add all the multiplied results;

[0177] Add a bias term to the added result to obtain the calculation result.

[0178] In this embodiment, the evaluation results of the multi-scene collaborative operation include whether the hydraulic support moving frame is in place, whether the hydraulic support guard plate is opened, whether the hydraulic support support resistance reaches the set value, whether the drum height of the coal mining machine is correct, whether the oblique cutting operation sequence of the coal mining machine is correct; whether the handling after the coal piling and material scattering of the scraper conveyor is correct, whether the flatness of the scraper conveyor meets the preset requirement, whether the end operation of the scraper conveyor is correct, whether the installation and removal operation of the end support is correct, whether the advance support matches the mining, and whether the advance support length meets the preset requirement.

[0179] In this embodiment, the evaluation results of the multi-scene collaborative operation include whether the traction speed and position of the coal mining machine match the hydraulic support action, whether the timing of the coal cutting, frame moving, and pushing operation is correct, and whether the parallelism of adjacent hydraulic supports meets the preset requirement.

[0180] In this embodiment, the S5: mapping the corresponding virtual three-dimensional model of the working face equipment in the virtual mine space according to the updated digital twin further includes:

[0181] S51: Obtain a dynamic incremental change space of the coal mining machine according to the pre-update data and the post-update data of the digital twin of the coal mining machine;

[0182] This step compares the pre-update data and the post-update data of the digital twin of the coal mining machine, and predicts the state of the coal mining machine after the next action. Since the coal mining machine only changes locally when performing the next action, some parts do not change. In this embodiment, only the locally changed parts are updated. These changed parts are referred to as dynamic increments in this document. Since there are multiple possibilities of the predicted state of the coal mining machine, each possibility corresponds to a dynamic increment, and these dynamic increments are combined to form a dynamic incremental change space. For example, through comparison of the pre-update data and the post-update data, it is concluded that the left swing arm of the coal mining machine is rotated at least downward, the left swing arm and the right swing arm rotate at a constant speed, and the remaining parts remain unchanged. Since the change amount of the swing arm rotation speed of the coal mining machine is limited in a short time, the speed range of the swing arm rotation of the coal mining machine within a certain time range can be predicted. The state of the swing arm and the roller under various rotation speeds of the swing arm in the speed range forms the dynamic incremental change space of the coal mining machine.

[0183] S52: The digital twin update module determines the public update data of the coal mining machine according to the dynamic incremental change space;

[0184] Each dynamic increment in the dynamic incremental change space can have the same data that can be used to update the virtual model of the coal mining machine. These data are the intersection of each dynamic increment data, which are also referred to as public update data in this document. For example, the data near the center of the swing arm rotation is the same for different rotation angles, and therefore can be used as public update data. This step can obtain the public update data before the update time through edge computing at the digital twin update module end.

[0185] S53: The server updates the corresponding part of the virtual model of the coal mining machine according to the received public update data;

[0186] However, a part of the public update data can be sent to the server first. The server can update the virtual three-dimensional model of the coal mining machine with the public update data according to the pre-set update time arrangement.

[0187] S54: Determine the data length of the candidate state data of the coal mining machine according to the dynamic incremental change space and the public update data already received by the server;

[0188] According to the public update data that the server has received, it can be determined which parts of the coal mining machine have their update data determined, and the update data of the remaining parts has multiple possibilities, each dynamic increment in the dynamic incremental change space corresponds to a possibility. This step will obtain all possible update data of the remaining parts as candidate data, and obtain the length of the candidate data.

[0189] S55: Determine whether the candidate data processing time meets the requirements according to the candidate data length and the update time point.

[0190] The candidate data processing time is the time required to send the candidate data to the server. If the candidate data processing time can meet the requirement that the candidate data is sent before the update time point, it meets the requirement, otherwise it does not meet the requirement.

[0191] S56: If it meets the requirement, the server updates the virtual three-dimensional model according to the end state of the coal mining machine and the candidate data.

[0192] If the candidate data processing time is short enough, the server can receive the candidate data before the update time, and then select the data corresponding to the end state from the candidate data according to the end state of the coal mining machine at the update time to update the three-dimensional virtual model.

[0193] S57: If not, repeat steps S51 to S56.

[0194] If the length of the candidate data is long, continue to obtain the public update data to reduce the data length of the candidate data. Until the candidate data processing time meets the requirement.

[0195] This embodiment obtains the public update data by predicting the motion state of the coal mining machine before the update time arrives, and sends the public update data to the server in advance, so that the server can complete the preparation of the data required for updating before the update, thereby greatly shortening the time required for updating and significantly increasing the real-time performance of the virtual three-dimensional model update.

[0196] Embodiment 2

[0197] As shown in Figure 9 The embodiment provides a multi-scene collaborative simulation mine teaching system based on digital twinning, which applies the teaching method described in embodiment 1. The system includes a working face equipment physical model, a cloud server, and a digital twinning update module. The working face equipment physical model is installed with a sensor, and the digital twinning update module is used to update the digital twinning according to the data collected by the sensor. The digital twinning update module is in communication connection with the cloud server.

[0198] The cloud server generates a virtual mine space containing a large-inclination coal seam roadway model according to the development and preparation roadway layout form of the actual production large-inclination coal seam mine, each sensor installed on the physical model of the working face equipment collects data reflecting the state of the physical model of the working face equipment in real time, and the digital twin updating module updates the digital twin online according to the sensor data. The updated digital twin drives the virtual three-dimensional model of the working face equipment mapped in the virtual mine space to be updated in real time online.

[0199] The teaching system also includes a VR glasses and a simulation walking platform, the VR glasses are in communication connection with the cloud server, the simulation walking platform is in communication connection with the cloud server, the VR glasses receive image data for displaying the virtual mine space sent by the cloud server, and the simulation walking platform is used for receiving data for controlling the inclination angle of the simulation walking platform sent by the cloud server. During the learning and training process of the students by using the system, the students watch the virtual space produced by the cloud server through the VR glasses. The simulation walking platform is used for simulating the inclination state of the large-inclination coal seam roadway, so that the body feeling of the students is consistent with the visual of the virtual scene.

[0200] The above is a detailed introduction to the multi-scene collaborative simulation mine teaching system and method based on digital twinning provided by the embodiment of the application.

[0201] It should be noted that the present application is not limited to the specific arrangements and processes described above and illustrated in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the present application.

[0202] The functional blocks shown in the structure block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), optical discs, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via a computer network such as the Internet, an intranet, etc.

[0203] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps are performed simultaneously.

[0204] The above merely illustrates the specific implementation of the present application. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, module and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein. It should be understood that the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A mine teaching method based on digital twinning multi-scene collaborative simulation, characterized in that, The method comprises: S1: generating a virtual mine space containing a large-inclination coal seam roadway model according to the layout form of the mining and preparation roadway in the actual production of a large-inclination coal seam mine; S2: obtaining a virtual model of the working face equipment as a digital twin of the working face equipment; S3: obtaining real-time model parameters of a plurality of working face equipment physical models in different scenes; S4: obtaining an updated digital twin after online updating the digital twin of the working face equipment according to the real-time model parameters; S5: mapping a corresponding virtual three-dimensional model of the working face equipment in the virtual mine space according to the updated digital twin, and controlling the inclination of the simulation walking platform according to the position of the virtual three-dimensional model in the virtual mine space; S6: evaluating the operation of the working face equipment according to the real-time model parameters by using a machine learning algorithm; The S4 comprises: S401: obtaining the safety protection range of each working face equipment; S402: determining the safety protection range of the updated digital twin according to the safety protection range of each working face equipment and the corresponding updated digital twin model; S403: determining whether there is mutual interference between the updated virtual three-dimensional models according to the safety protection range of each updated digital twin and the position of the corresponding virtual three-dimensional model of the updated digital twin in the virtual mine space, comprising: obtaining a dynamic adjustment amount of the safety protection range of the virtual three-dimensional model according to the real-time model parameters; determining the elastic boundary of the safety protection of the virtual three-dimensional model according to the safety protection range of the digital twin and the dynamic adjustment amount; obtaining the dynamic range of the digital twin within a preset time in the future according to the real-time model parameters; determining the time dynamic elastic boundary of the virtual three-dimensional model according to the dynamic range and the elastic boundary; determining whether there is mutual interference between the updated virtual three-dimensional models according to the time dynamic elastic boundary of the virtual three-dimensional model and the position of the virtual three-dimensional model in the virtual mine space; S404: if there is no mutual interference, executing S5; S405: if there is mutual interference, pausing the update of the virtual three-dimensional model, and rolling back the digital twin to a safe state according to the interference; S406: performing reverse updating of the physical model of the working face equipment according to the digital twin in the safe state and starting a degraded operation mode of the physical model.

2. The mine teaching method based on digital twinning multi-scene collaborative simulation according to claim 1, characterized in that, The S5 further comprises: S57: obtaining the inclination parameter of the roadway model in the virtual mine space; S58: determining the inclination angle of the simulation walking platform according to the position of the virtual three-dimensional model and the inclination parameter of the roadway model; S59: controlling the real-time inclination of the simulation walking platform according to the inclination angle of the simulation walking platform.

3. The mine teaching method based on digital twinning multi-scenario collaborative simulation according to any one of claims 1-2, characterized in that, The S6 comprises: S61: obtaining the real-time model parameters in the process of operating the physical model of the working face equipment by the trainee in each scene; S62: obtaining a support vector machine algorithm model trained by data; S63: the support vector machine algorithm model evaluates the coal mining operation of the trainee according to the input model parameters.

4. The mine teaching method based on digital twinning multi-scene collaborative simulation according to claim 3, characterized in that, The S63 further comprises: S631: obtaining historical data of the state vector of the simulation walking platform; S632: Obtain an optimal classification boundary of a support vector machine algorithm model corresponding to the historical data; S633: Obtain a mapping relationship between an ideal scale parameter and a bias term in the support vector machine and a state vector of the simulation walking platform according to the historical data and the optimal classification boundary; S634: Obtain a state vector of the current simulation walking platform when a target state change rate of the simulation walking platform reaches a set threshold; S635: Determine a current ideal scale parameter and a current bias term according to the current state vector and the mapping relationship; S636: Adjust the classification boundary of the support vector machine algorithm model according to the current ideal scale parameter and the bias term.

5. The mine teaching method based on digital twinning multi-scene collaborative simulation according to claim 3, characterized in that, The S62 comprises: S621: Obtain data of a plurality of sensors for detecting model parameters when operating the working face equipment, and divide the sensor data into independent operation data groups and multi-scenario collaborative operation data groups; S622: Obtain a support vector machine algorithm model corresponding to an independent operation evaluation item and a support vector machine algorithm model corresponding to a multi-scenario collaborative operation evaluation item as a first support vector machine initial algorithm model and a second support vector machine initial algorithm model respectively; S623: Obtain evaluation results of the evaluation items corresponding to the independent operation data groups; S624: Obtain evaluation results of the evaluation items corresponding to the multi-scenario collaborative operation data groups; S625: Label the data in the corresponding independent operation data groups and multi-scenario collaborative operation data groups according to the evaluation results; S626: Train the first support vector machine initial algorithm model through the labeled independent operation data groups to obtain an independent operation evaluation algorithm model; S627: Train the second support vector machine initial algorithm model through the labeled multi-scenario collaborative operation data groups to obtain a multi-scenario collaborative operation evaluation algorithm model.

6. The mine teaching method based on digital twinning multi-scene collaborative simulation according to claim 5, characterized in that, The S63 comprises: S630: Obtain an independent operation data group in a data group generated by detecting real-time model parameters of the working face equipment; S631: Obtain an independent operation evaluation algorithm model; S632: Obtain data category labels corresponding to each data in the independent operation data group; S633: Input the data in the independent operation data group and the corresponding data category labels to the independent operation evaluation algorithm model; S634: The independent operation evaluation algorithm model outputs corresponding independent operation evaluation results according to the input data in the independent operation data group; S635: Obtain a multi-scenario collaborative operation data group in a data group generated by detecting real-time model parameters of the working face equipment; S636: Obtain a multi-scenario collaborative operation evaluation algorithm model; S637: Obtain data category labels corresponding to each data in the multi-scenario collaborative operation data group; S638: Input the data in the multi-scenario collaborative operation data group and the corresponding data category labels to the multi-scenario collaborative operation evaluation algorithm model; S639: The multi-scenario collaborative operation algorithm model outputs corresponding multi-scenario collaborative operation evaluation results according to the input data in the multi-scenario collaborative operation data group.

7. A multi-scenario collaborative simulation mine teaching system based on digital twinning, applying the teaching method of any one of claims 1 to 6, characterized in that, The working face equipment physical model is provided with a sensor, and a digital twin updating module is used to update the digital twin according to data collected by the sensor.

8. The mine teaching system based on digital twinning multi-scene collaborative simulation according to claim 7, characterized in that, The VR glasses are in communication connection with the cloud server, and the VR glasses receive image data sent by the cloud server for displaying a virtual mine space.

9. The mine teaching system based on digital twinning multi-scene collaborative simulation according to claim 7, characterized in that, The simulation walking platform is in communication connection with the cloud server, and the simulation walking platform is used to receive data sent by the cloud server for controlling the inclination angle of the simulation walking platform.

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