Real-scene interaction safety teaching management method and device based on VR technology, and medium

Abstract

The invention discloses a live-action interaction safety teaching management method and device based on VR technology and a medium, and relates to the technical field of personnel training management, and the method comprises the steps: collecting three-dimensional point cloud data and a panoramic image of a target teaching environment, and constructing a three-dimensional space structure body with a semantic tag; generating a VR twinning environment with interaction detection capability, and configuring a standard operation track and a teaching content script; acquiring student operation data, constructing a behavior vector sequence and generating an actual behavior track; performing comprehensive scoring based on the behavior deviation function and the operation accuracy, and generating a personalized training scheduling plan; analyzing and optimizing a teaching script structure according to the bottleneck factor; and finally, according to the comprehensive score and the risk record, determining a post adaptation level, and filing a training evaluation report. According to the method, behavior tracking refinement, training feedback individuation and teaching content dynamic optimization are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel training management, and in particular to a method, device and medium for real-scene interactive safety teaching management based on VR technology. Background Art

[0002] With the rapid development of virtual reality (VR) technology, its application in the field of education and training has gradually deepened, showing significant advantages in high-risk scenarios such as safety education and operation drills. In recent years, VR teaching platforms that integrate technologies such as 3D modeling, motion capture, and virtual interaction have been gradually applied to special operations, industrial training and other scenarios. By constructing a virtual environment to simulate the real operation process, the authenticity and interactivity of teaching have been effectively improved. At the same time, with the advancement of technologies such as lidar, point cloud modeling and semantic segmentation, more refined support has been provided for the construction of VR teaching scenarios.

[0003] Although existing VR safety teaching systems have achieved scene virtualization and basic interactive functions, they still face multiple technical bottlenecks. First, the existing system is not adaptable enough to the structure of teaching content. It is unable to carry out structural optimization and dynamic version updates based on operational bottlenecks encountered by students during execution, making it difficult for teaching content to continuously evolve and match individual needs. Second, the student evaluation mechanism generally uses a single scoring model, which fails to incorporate multi-dimensional characteristics such as behavioral deviations and misoperation in high-risk areas, making it difficult to implement personalized training to optimize weak links. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a real-life interactive safety teaching management method based on VR technology, which solves the problems that the existing technology cannot optimize and update the teaching version during the training process and lacks personalized evaluation of student behavior.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a real-life interactive safety teaching management method based on VR technology, which comprises: collecting three-dimensional point cloud data of a target teaching environment, combining panoramic images and semantic annotations, and constructing a three-dimensional spatial structure with semantic labels; Construct the three-dimensional space structure into a VR twin environment with interactive detection capabilities, and configure standard operation tracks, action detection areas, and teaching content scripts in each semantic label area; Collect students' operation data in the VR environment, construct behavior vector sequences, and generate students' actual behavior trajectories; Based on the difference between the standard operation trajectory and the trainee's actual behavior trajectory, a deviation function is constructed to record the number of incorrect operations and the operation qualification rate in the key risk space area. A comprehensive scoring function is used to comprehensively evaluate the trainee's behavior and generate a personalized training scheduling plan. Obtain the operation data of each teaching script in the personalized training scheduling plan to perform bottleneck factor calculation, and optimize the teaching content structure and version update based on the bottleneck factor; Based on the trainees' final comprehensive scores of actual behaviors and risk records, job suitability levels are determined and training evaluation reports are filed.

[0007] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the specific steps of constructing a three-dimensional space structure with semantic labels are as follows: Deploy a laser radar in the target teaching environment, perform scanning operations, obtain 3D point cloud data, and perform spatial alignment and noise filtering on the 3D point cloud data to generate point cloud reconstruction data; Collecting texture image data of the target teaching environment, and performing spherical projection processing on the texture image data to obtain panoramic image data; Perform texture mapping on the point cloud reconstruction data and the panoramic image data in a unified coordinate system to generate a fused 3D model, and perform spatial structure boundary fitting on the fused 3D model; Perform spatial projection calculation on the structural boundaries in the fused 3D model, extract regional features, form a set of spatial candidate regions, extract geometric features from the set of spatial candidate regions and match them with usage rules to construct a set of semantic labels; The semantic label set is mapped to the spatial coordinate range in the fused 3D model to generate a 3D spatial structure with semantic labels.

[0008] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the three-dimensional space structure is constructed as a VR twin environment with interactive detection capabilities, and a standard operation trajectory, action detection area, and teaching content script are configured in each semantic label area. The specific steps are as follows: Read the three-dimensional space structure with semantic tags, and import the three-dimensional space structure with semantic tags into the VR development engine to initialize the three-dimensional scene; In the VR development engine, a physical collision body is added to each semantically labeled area of ​​a 3D spatial structure with semantic labels to build a VR twin environment with interactive detection capabilities. In a VR twin environment with interactive detection capabilities, based on the task requirements and operation specifications within the semantic label area, a set of standard operation trajectories corresponding to the semantic labels is generated. For each standard operation trajectory in the standard operation trajectory set, a matching motion detection area is set to form a binding relationship table between the standard operation trajectory and the motion detection area; A teaching content script is configured for each semantic label area in the three-dimensional space structure with semantic labels in the binding relationship table between the standard operation trajectory and the action detection area.

[0009] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the steps of constructing a behavior vector sequence to generate the actual behavior trajectory of the trainee are as follows: Collect the trainee's position coordinates, orientation angles, and input actions in the VR twin environment to generate a preliminary set of operation data sequences. Normalize and filter the preliminary set of operation data sequences to construct a continuous behavior vector sequence. The behavior vector sequence is spliced ​​according to the time dimension to generate the student's actual behavior trajectory.

[0010] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the method constructs a deviation function based on the difference between the standard operation trajectory and the trainee's actual behavior trajectory, and records the number of incorrect operations and the operation pass rate in the key risk space area. The specific steps are as follows: Align the standard operation trajectory with the trainee's actual behavior trajectory in time and space, calculate the behavioral deviation distance at each moment, and construct a behavioral deviation function; Calculating the time derivative of the behavior deviation function to obtain the first-order derivative of the behavior deviation function; Mark the critical risk space area in the standard operation trajectory, and count the number of deviations of the operator's actual behavior trajectory within the critical risk space area, and record it as the number of incorrect operations within the critical risk space area; Match the standard operation trajectory with the trainee's actual behavior trajectory in action sequence, calculate the action execution accuracy, and obtain the operation qualification rate.

[0011] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the method uses a comprehensive scoring function to comprehensively evaluate the student's behavior and generate a personalized training scheduling plan. The specific steps are as follows: The number of incorrect operations, the qualified operation rate, the behavior deviation function and the first-order derivative of the deviation function in the key risk space area are constructed into a comprehensive scoring function to calculate the actual comprehensive score of the trainees; The students' learning situation is evaluated based on their actual behavior comprehensive scores. If the students' actual behavior comprehensive scores are unqualified, the time period with the largest deviation value in the behavior deviation function is located and analyzed, the semantic label area is extracted, and a personalized training scheduling plan is generated.

[0012] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the following steps are performed: obtaining the operation data of each teaching script in the personalized training scheduling plan to perform bottleneck factor calculation, and optimizing the teaching content structure and updating the version based on the bottleneck factor. Read the teaching content script configured under the semantic tag in the personalized training schedule and extract the operation data recorded during the students' execution of the teaching content script; Structural processing of operation data is performed to extract the average completion time, number of operation errors, and number of repeated attempts of the operation elements in each teaching content script, and generate a set of operation performance indicators; Calculate the execution bottleneck factor of each operator based on the set of operation performance indicators, sort and filter out high-bottleneck operators according to the size of the bottleneck factor, and construct a bottleneck factor distribution map; Based on the teaching content script fragments of high-bottleneck operators in the bottleneck factor distribution map, the teaching content script is structurally optimized, and the structural optimization results are synchronously updated to the personalized training scheduling plan.

[0013] As a preferred solution of the VR-based real-life interactive safety teaching management method of the present invention, the specific steps of determining the job suitability level and archiving the training evaluation report are as follows: Read the trainee's actual behavior comprehensive score and the error operation record in the key risk space area to establish the score risk pair; Predefine a set of job suitability levels, set a judgment function, determine the trainee's job suitability level based on the scoring risk, and associate it with the job description information; The comprehensive scores of trainees’ actual behaviors, the number of erroneous operations in key risk space areas, job adaptation level scores, and related job description information are summarized to generate a structured training evaluation report data set.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the real-life interactive safety teaching management method based on VR technology as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the real-life interactive safety teaching management method based on VR technology as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: through a comprehensive scoring function and a personalized training scheduling mechanism, quantitative evaluation of teaching feedback and targeted training optimization are achieved; through bottleneck factor analysis and content structure optimization, dynamic updating and precise reconstruction of teaching scripts are achieved; through behavior vector sequence and deviation function calculation, high-precision tracking and deviation analysis of students' operating behaviors are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the real-life interactive safety teaching management method based on VR technology.

[0019] Figure 2 Flowchart for building a VR twin environment.

[0020] Figure 3 Flowchart for generating actual behavior trajectories.

[0021] Figure 4 Flowchart for comprehensive score generation and personalized training scheduling. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0025] Reference Figure 1 , is an embodiment of the present invention, which provides a real-life interactive safety teaching management method based on VR technology, comprising the following steps: S1: Collect 3D point cloud data of the target teaching environment, combine it with panoramic images and semantic annotations, and construct a 3D spatial structure with semantic labels.

[0026] Deploy lidar equipment in the target teaching environment, perform scanning operations, and obtain three-dimensional point cloud data of the target teaching environment.

[0027] Furthermore, the spatial structure of the target teaching environment is selected as the area to be modeled, and a lidar device is selected according to the geometric structure and distribution of obstructions of the teaching space. For example, a Velodyne HDL-32E lidar device with 32-line laser scanning capability is selected to perform three-dimensional point cloud data scanning to obtain three-dimensional point cloud data for each frame.

[0028] The three-dimensional point cloud data of the target teaching environment are spatially aligned and noise filtered to generate spatially continuous point cloud reconstruction data of the target teaching environment.

[0029] Furthermore, the point cloud coordinates of each frame of 3D point cloud data are uniformly converted to align the 3D point cloud data space. After the conversion is completed, the ICP algorithm is used to splice and fuse multiple lidar point clouds, and voxel filtering is used to perform sparse processing on the point cloud to remove noise and ground reflection interference, and finally generate target teaching environment point cloud reconstruction data with high-density spatial restoration accuracy.

[0030] The texture image data of the target teaching environment is collected, and the texture image data is subjected to spherical projection expansion processing to obtain panoramic image data of the target teaching environment.

[0031] Furthermore, an image acquisition device with multi-eye fisheye imaging capability or 360° panoramic shooting capability is selected, and the image acquisition device is arranged at the same layout point as the lidar device, and image acquisition operations are performed to obtain texture image data. A multi-image stitching method based on camera extrinsic parameters and perspective intersection reconstruction is used to perform texture image stitching. At the same time, when performing image stitching, spherical projection mapping is used to map the panoramic image data from the spherical coordinate system to a two-dimensional plane unfolding image to generate panoramic image data of a complete target teaching environment.

[0032] Perform texture mapping on the point cloud reconstruction data of the target teaching environment and the panoramic image data of the target teaching environment in a unified coordinate system to generate a fused three-dimensional model of the target teaching environment; Furthermore, the point cloud reconstruction data of the target teaching environment and the panoramic image data of the target teaching environment are texture mapped in a unified coordinate system. Specifically, the point cloud reconstruction data and the panoramic image data are calibrated to obtain the rigid body transformation matrix. The rigid body transformation matrix is ​​used to map each point cloud data point to the three-dimensional coordinates in the camera coordinate system; the three-dimensional coordinates in the camera coordinate system are projected onto the image plane through the camera intrinsic parameters, the pixel position of the point cloud reconstruction data in the panoramic image is obtained, and the RGB texture information at the pixel position is extracted for texture binding; after completing the texture binding, a color point cloud data set containing coordinate and color information is generated, and a surface reconstruction method, such as Poisson reconstruction, is used to construct the color point cloud data into a continuous patch structure to obtain a fused three-dimensional model.

[0033] It should be noted that the rigid body transformation matrix is: Assuming that the point cloud data is in the laser radar coordinate system In the camera coordinate system, the image is In the equation, a homogeneous transformation relationship is established, which is expressed as: ; in, 、 、 Respectively represent the point cloud in the camera coordinate system after homogeneous transformation in 、 、 Axis coordinates, 、 、 Respectively represent the point cloud data in the laser radar coordinate system Next 、 、 Axis coordinates, Represents the rigid body transformation matrix from the lidar coordinate system to the camera coordinate system, and "1" represents the expansion term of the homogeneous coordinate.

[0034] Among them, the rigid body transformation matrix belongs to the Euclidean group and is a 4×4 matrix, including two parts: rotation and translation.

[0035] Among them, "1" is used to express the three-dimensional coordinates as a four-dimensional homogeneous vector, which is convenient for affine transformation.

[0036] Perform spatial structure boundary fitting processing on the fused three-dimensional model of the target teaching environment; Furthermore, the local neighborhood of all points in the fused 3D model is constructed using the K-nearest neighbor method, the normal vector is estimated based on the local neighborhood information of each point in the fused 3D model, and the principal component analysis method is used for noise removal and smoothing; the continuous plane area in the fused 3D model is extracted using the region growing algorithm, and typical structural surfaces such as walls, floors, and tops are identified; the boundary point set is extracted by calculating the normal angle between adjacent planes, and the Hough transform is used to fit the boundary point set into a 3D broken line, further constructing a complete spatial structure boundary line network, forming a structural boundary set including the building entity outline, functional area boundaries, and structural transition surfaces.

[0037] It should be noted that the K value in the K nearest neighbor method is selected based on the average point spacing and spatial structure size of the three-dimensional point cloud, and a value that can cover local surface features but does not cross geometric mutations is selected as the K value.

[0038] Perform spatial projection calculation on the structural boundaries in the fused 3D model, extract regional features, and form a set of spatial candidate regions for the target teaching environment; Furthermore, each structural boundary line in the structural boundary set is selected in the fused 3D model, and based on the set custom coordinate system of the teaching space, a horizontal projection plane is selected as the projection reference plane.

[0039] The custom coordinate system of the teaching space is set as follows: the geometric center of the teaching area is set as the origin, the direction of the main channel or main working surface is selected as the positive direction of the X-axis, the positive direction of the Y-axis is perpendicular to the plane where the X-axis is located and points to the depth direction of the teaching space according to the right-hand rule, and the positive direction of the Z-axis is perpendicular to the XY plane and points upward.

[0040] A projection operation is performed on the three-dimensional coordinates of each vertex in the structural boundary line, wherein the projection operation specifically includes: mapping the three-dimensional coordinate system into a two-dimensional coordinate system to obtain the projection contour of the structural boundary on the two-dimensional plane.

[0041] All projected contours are tested for closure, and boundary sets with topological closure relationships are screened out to construct them into two-dimensional closed polygonal areas. The structural surfaces of the two-dimensional closed polygonal areas in three-dimensional space are recorded. The projected area, perimeter and shape factor of each two-dimensional closed area are calculated, and all the facets corresponding to the two-dimensional closed area in the three-dimensional model are retroactively fused to extract the elevation range of the two-dimensional closed area, which is used to identify the hierarchical information of the spatial height of the area.

[0042] Based on the boundary contact relationship, the spatial adjacency characteristics between different two-dimensional closed polygonal areas are analyzed to form a structured spatial area data set containing geometric attributes, spatial positions and topological relationships.

[0043] Among them, the structured spatial region data set includes the projected area, perimeter, elevation range, shape factor, as well as the mapping relationship with the structural surface of the fused three-dimensional model and spatial adjacency relationship information.

[0044] The geometric features of the candidate spatial regions of the target teaching environment are extracted and matched with usage rules to construct a semantic label set of the target teaching environment.

[0045] Furthermore, geometric attributes are extracted for each spatial candidate region, wherein the geometric attributes include projected area, boundary perimeter, shape factor, and elevation range of the structural surface covered by the region.

[0046] It should be noted that the shape factor is used to determine the regularity of an area, and the elevation range is used to distinguish different spatial levels such as the ground, table or top area.

[0047] Furthermore, a usage rule library is established, and each usage rule consists of a projection area, a shape factor interval, an elevation condition, and a semantic label. The semantic labels include the walkable area on the ground, the operating table area, and the risk hanging area above; the elevation condition includes the lowest point height, the highest point height, and the average height of the spatial candidate area. The elevation condition is used to distinguish different spatial levels in the usage rule matching process; the shape factor interval is defined by the area of ​​the spatial candidate area on the projection plane and the projection boundary perimeter. The shape factor interval is used to measure the regularity of the spatial candidate area to assist in usage judgment.

[0048] Usage rule matching is performed on each spatial candidate area. In the usage rule matching stage, the geometric attributes of each spatial candidate area are compared one by one with all usage rules. When the characteristics of the spatial candidate area meet the usage rules, the spatial candidate area is assigned a corresponding semantic label; if a spatial candidate area meets multiple rules, it is screened according to the rule priority to ensure that the semantic label is unique and reasonable, and finally a semantic label set is formed. Each label records the number of the spatial candidate area, spatial position, semantic label type and rule source number.

[0049] The semantic label set of the target teaching environment is mapped to the spatial coordinate range in the fused three-dimensional model of the target teaching environment to generate a three-dimensional spatial structure with semantic labels.

[0050] Furthermore, according to the correspondence between each semantic label in the semantic label set and the spatial candidate area, the corresponding structural surface in the fused three-dimensional model is extracted to generate a structural surface set, wherein the structural surface set is composed of triangular facets and three-dimensional vertex coordinates.

[0051] Each semantic label is bound to all the faces in the corresponding structural face set, and a semantic field is embedded in each structural face data structure to mark the functional type, such as the operating table area, walkable area, and risky edge area.

[0052] The structural faces bound with semantic labels are aggregated again to form semantic space units, where each semantic space unit includes a structural face index set, a vertex coordinate set, and a semantic attribute identifier, which together constitute a three-dimensional spatial structure with semantic labels.

[0053] S2: Construct the three-dimensional space structure into a VR twin environment with interactive detection capabilities, and configure standard operation trajectories, action detection areas, and teaching content scripts in each semantic label area.

[0054] See also Figure 2 , read the three-dimensional space structure with semantic tags, and import the three-dimensional space structure with semantic tags into the VR development engine to initialize the three-dimensional scene.

[0055] Furthermore, the three-dimensional space structure with semantic labels is converted into a format readable by the VR development engine and imported. The VR development engine identifies the semantic label field and maps the structure to the virtual scene space according to the semantic label classification. At the same time, each semantic label area is registered as a logical node in the standard operation trajectory scene to complete the three-dimensional scene initialization.

[0056] It should be noted that mature VR engines such as Unity3D and Unreal Engine are selected as VR development engines, and the basic environment configuration is completed based on the architecture of the VR development engine.

[0057] For example, install Unity on the computer and integrate the OpenXR plug-in and the target VR device matching software development kit SDK; import the constructed fused 3D model with semantic labels into the Unity project in FBX format, and load the naming rules and unique identifiers of each semantic label area in the teaching content script into the fused 3D model to initialize the 3D scene.

[0058] In the VR development engine, a physical collision body is added to each semantically labeled area of ​​the three-dimensional space structure with semantic labels to build a VR twin environment with interactive detection capabilities.

[0059] Furthermore, after the 3D scene is initialized, a matching physical collision body is generated for each semantically labeled area in the 3D space structure with semantic labels based on the boundary geometric dimensions and spatial range, and the collision body is bound to the nodes in the semantically labeled area. The collision body supports real-time collision event callbacks.

[0060] It should be noted that a matching physical collision body is generated based on the boundary geometric dimensions and spatial range. Specifically, the boundary geometric dimension parameters and three-dimensional spatial coordinate range of each semantic label area are obtained. The boundary geometric dimension parameters include length, width and height. Based on the boundary geometric dimension parameters, the built-in collision body constructor of the VR engine is called to generate a three-dimensional physical collision body that fits the spatial range. The generated physical collision body is bound to the logical node of the semantic label area, and the collision priority and trigger condition fields are written into the collision body according to the actual teaching content.

[0061] Each collision body carries attribute fields, including collision priority, trigger conditions, etc., which constitute an interactive perception set that supports semantic recognition, and then build a VR twin environment with complete interactive detection capabilities.

[0062] In a VR twin environment with interactive detection capabilities, a set of standard operation trajectories corresponding one-to-one to the semantic labels is generated based on the task requirements and operation specifications in the semantic label area.

[0063] Furthermore, the predefined teaching task database and standard operating procedure documents are read, and according to the functional attributes and task requirements of the semantic label area, for example, cable connection is completed in the operating table area, and obstacle avoidance drills are performed in the risk area. The standard operation starting point, target point and action path key frame point are configured in each semantic label area, and the trajectory of the configuration of the semantic label area is generated through the Bezier curve to generate the standard operation trajectory corresponding to the semantic label, and a set of standard operation trajectories corresponding to the semantic labels are obtained.

[0064] It should be noted that the teaching task database is compiled by the teaching unit based on the training outline, job operation specifications and previous training records. The teaching task database includes the objectives, steps and assessment standards of each task; the standard operating procedure document is compiled by the industry or enterprise based on relevant safety regulations, operating manuals and expert review opinions, recording the standard steps for completing the task, key parameters and qualification criteria.

[0065] For each standard operation trajectory in the standard operation trajectory set, a matching motion detection area is set to form a binding relationship table between the standard operation trajectory and the motion detection area.

[0066] Furthermore, for each trajectory in the standard operation trajectory set, an action detection area is generated according to the path range. For example, a spatial bounding volume with a buffer tolerance is generated around the trajectory, or a point cloud mask is used to define the detection boundary.

[0067] It should be noted that the path range refers to the three-dimensional envelope area constructed based on the lateral offset distance and longitudinal height tolerance with the spatial coordinates of each key frame point in the standard operation trajectory as the center, which is used to limit the allowable spatial offset range of the trainee's actual operation trajectory.

[0068] Furthermore, the action detection area forms a binding relationship with the standard operation trajectory through the space constraint parameter, thereby forming a binding relationship table between the standard operation trajectory and the action detection area.

[0069] A teaching content script is configured for each semantic label area in the three-dimensional space structure with semantic labels in the binding relationship table between the standard operation trajectory and the action detection area, thereby completing the teaching function configuration of each semantic label area.

[0070] Furthermore, each record in the binding relationship table is used as the configuration basis of the teaching unit, and a corresponding teaching content script is added to the semantic label area in the VR development engine. The teaching content script includes task description, standard operation trajectory number, audio and video guidance, real-time feedback logic, action sequence and detection rules, etc. Each teaching content script is bound to the node in the semantic label area, and the node in the semantic label area triggers the corresponding action detection area or trajectory key point.

[0071] It should be noted that the teaching content script can be implemented based on a state machine or event-driven approach, for example, using the logical structure of "start trigger" → "operation tracking" → "compliance judgment" → "feedback prompt" to achieve a complete teaching interaction closed loop.

[0072] S3: Collect the trainee’s operation data in the VR environment, construct a behavior vector sequence, and generate the trainee’s actual behavior trajectory.

[0073] See also Figure 3 , in a VR twin environment with interactive detection capabilities, the trainees’ position coordinates, orientation angles, and input actions are collected in real time in the VR twin environment.

[0074] Furthermore, the interactive handle, motion capture device, and spatial positioning device are connected to the VR development engine, and the students' behavioral data are collected in real time based on the frame-level event callback mechanism. The behavioral data includes position coordinates, orientation angle, input action, and timestamp.

[0075] It should be noted that input actions refer to the student's gestures, postures and other actions captured by the interactive handle and motion capture device.

[0076] The position coordinates, orientation angles, and input motion data collected for each frame are organized into five-tuples and continuously collected at the frame rate.

[0077] The trainees’ position coordinates, orientation angles, and input actions in the VR twin environment are structured according to timestamps to generate a preliminary set of operation data sequences.

[0078] Furthermore, the student's position coordinates, orientation angles, and input action data collected in real time are arranged in ascending order by timestamp to construct a preliminary set of operation data sequences.

[0079] The preliminary operation data sequence set is normalized and redundantly filtered to construct a continuous behavior vector sequence.

[0080] Furthermore, each piece of data in the preliminary operation data sequence set is normalized.

[0081] The normalization process is as follows: mapping the three-dimensional coordinates of the position coordinates to the standard space of the target teaching environment, converting the orientation angle values ​​into radians and standardizing them, and mapping the input actions into fixed-dimensional unique-hot encodings, for example, "grasping" is [0, 1, 0].

[0082] Furthermore, redundancy filtering is performed on each piece of data in the preliminary operation data sequence set.

[0083] The behavior vector sequence is spliced ​​according to the time dimension to generate the student's actual behavior trajectory.

[0084] Furthermore, the behavior vectors in the behavior vector sequence are normalized, and the normalized behavior vector sequence is spliced ​​into a continuous trajectory data structure along the time axis.

[0085] S4: Construct a deviation function based on the difference between the standard operation trajectory and the trainee's actual behavior trajectory, record the number of incorrect operations and the operation pass rate in the key risk space area, use the comprehensive scoring function to comprehensively evaluate the trainee's behavior, and generate a personalized training scheduling plan.

[0086] See also Figure 4 , align the standard operation trajectory with the trainee’s actual behavior trajectory in time and space, calculate the behavior deviation distance at each moment, and construct a behavior deviation function.

[0087] Furthermore, the target trajectory bound to the current teaching task is extracted from the standard operation trajectory set, where each standard operation trajectory point has a clear timestamp; at the same time, the student's behavior trajectory is extracted, and a dynamic time warping algorithm is used to time-align each standard operation trajectory point with the student's behavior trajectory point one by one to obtain a matching mapping relationship table between the standard operation trajectory point and the student's actual behavior trajectory point.

[0088] The Euclidean distance is calculated for each pair of matching points in the matching mapping relationship table in space, and a behavior deviation function is defined.

[0089] Among them, the behavior deviation function is expressed as: ; in, Indicates at a point in time The behavioral deviation value on Indicates at time The actual behavior trajectory vector of the trainees, Indicates at time Upper standard operation trajectory vector.

[0090] It should be noted that, in an optional implementation, multi-dimensional weights are considered, that is, spatial position is more important, and a weighted deviation function is further defined, which is expressed as: ; in, represents the weighted behavioral deviation value, Indicates the student’s current posture angle, represents the target attitude angle for standard operation, Indicates the student’s current gesture characteristics. Gesture features that represent standard operations, Represents the student’s current position coordinate vector, represents the position coordinate vector of the standard operation, represents the attitude angle deviation weight coefficient, represents the gesture action deviation weight coefficient, Represents the spatial position deviation weight coefficient.

[0091] It should be noted that the weight relationship is adjusted according to the actual task scenario. 、 、 The value range of is [0,1], and + + =1.

[0092] It should be noted that when collecting the student's gesture features, the original gesture results output by the motion capture device are mapped into a one-hot encoding vector of fixed dimension. For example, if the gesture category set is set to {grab, place, point, idle}, they are mapped to [1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1] respectively; for gestures containing continuous posture parameters, the spatial coordinate values ​​and angle information of positions such as fingertips, palm centers and joints in the three-dimensional coordinate system are extracted, and the spatial coordinate values ​​and angle values ​​are spliced ​​into a real vector, so that the student's current gesture features are uniformly represented as a numerical vector that can directly participate in the Euclidean distance.

[0093] The time derivative of the behavior deviation function is calculated to obtain the first-order derivative of the behavior deviation function.

[0094] Furthermore, a numerical first-order derivative approximation is performed on the behavior deviation function to obtain the first-order derivative of the behavior deviation function.

[0095] Mark the critical risk space area in the standard operation trajectory, and count the number of deviations of the operator's actual behavior trajectory within the critical risk space area, which is recorded as the number of erroneous operations within the critical risk space area.

[0096] Furthermore, a set of key risk spatial regions is predefined in the standard operation trajectory, and each key risk region has a spatial enclosing boundary and an associated time range.

[0097] All matching points in the trainee's actual behavior trajectory that are located within the spatial enclosing boundary and whose behavior deviation value is greater than the behavior deviation threshold are counted as one deviation and included in the error operation count of the current key risk area to obtain the set of error operation counts in the key risk space area.

[0098] It should be noted that the behavioral deviation threshold is obtained by collecting deviation sequences during the correct demonstration and reproduction by qualified students and eliminating obvious error segments, and performing a sliding mean on the deviation sequences to obtain the smoothed mean and standard deviation. The smoothed mean and standard deviation are linearly weighted to obtain the behavioral deviation threshold, which is used to determine the degree to which the student's actual behavioral trajectory exceeds the standard operation trajectory.

[0099] Match the standard operation trajectory with the trainee's actual behavior trajectory in action sequence, calculate the action execution accuracy, and obtain the operation qualification rate.

[0100] Furthermore, the action sequences of the standard operation trajectory and the trainee's actual behavior trajectory are extracted, the action sequences of the standard operation trajectory and the action sequences of the trainee's actual behavior trajectory are arranged in time series, and the action sequences of the standard operation trajectory and the trainee's actual behavior trajectory are compared using the edit distance method, and the minimum transformation cost is calculated. The minimum transformation cost is used as the action execution accuracy, and then the operation qualification rate is obtained.

[0101] The number of incorrect operations, operation qualification rate, behavior deviation function and the first-order derivative of the deviation function in the key risk space area are constructed as a comprehensive scoring function to calculate the trainee's actual behavior comprehensive score.

[0102] Furthermore, the comprehensive scoring function is expressed as: ; in, Indicates the comprehensive score of the students’ actual behavior. 、 、 represents the weight factor, Indicates the trainees’ pass rate of operation. Indicates the number of misoperations in the critical risk space area, represents the penalty coefficient, Indicates the total operation time, represents the reverse influence function of behavioral deviation, represents the first derivative of the behavior deviation function, Indicates time The importance weight of the comprehensive score of the students' actual behavior.

[0103] It should be noted that 、 、 The value range of is [0,1], and + + =1, used to balance the influence of the three scoring factors. The specific value is set according to the importance of the teaching task.

[0104] It should be noted that It is used to control the rate of score attenuation caused by the increase in the number of erroneous operations. It is set in the range of [0.05, 1.0] based on the actual impact of risky erroneous operations on the comprehensive score and the balance between score stability and risk sensitivity.

[0105] It should be noted that It is used to indicate the importance of each time period to the overall behavior evaluation, and the value range is , among which, if the moment If it is a critical task or high-risk operation, Close to 1; if the moment If it is in the buffer, transition or non-evaluation phase, Approximately equal to 0.

[0106] The students' learning situation is evaluated based on their actual behavior comprehensive scores. If the students' actual behavior comprehensive scores are unqualified, the time period with the largest deviation value in the behavior deviation function is located and analyzed, the semantic label area is extracted, and a personalized training scheduling plan is generated.

[0107] Furthermore, based on the statistical analysis method of historical assessment data, a normality test is performed on the actual behavioral comprehensive scores of past trainees and the field performance records after training to obtain the trainees' historical behavioral comprehensive scores, and the trainees' historical behavioral comprehensive scores are used as the trainees' comprehensive score threshold. If the trainees' actual behavioral comprehensive scores are not less than the trainees' comprehensive score threshold, the assessment is passed and marked as qualified; if the trainees' actual behavioral comprehensive scores are less than the trainees' comprehensive score threshold, the assessment fails and is marked as unqualified, and the behavioral deviation analysis process is entered.

[0108] The behavioral deviation analysis process is as follows: the length of the deviation positioning time window is set according to the importance of the trainee training. According to the length of the deviation positioning time window, the student's behavioral deviation function value is searched for the maximum deviation point in the time domain to obtain the maximum deviation time period.

[0109] Furthermore, the spatial position sequence within the maximum deviation time period is obtained from the behavior vector sequence, and the spatial position sequence is mapped to the fused three-dimensional model. The semantic label area where the maximum deviation time period is located is queried, and all semantic label areas where deviation peaks occur are constructed into a personalized training scheduling plan list, wherein each training scheduling item in the personalized training scheduling plan list includes the semantic label area, the standard operation trajectory number, the historical deviation degree, and the number of training times.

[0110] Output the personalized training schedule in a structured data format and link it to the student's profile.

[0111] S5: Obtain the operation data of each teaching script in the personalized training scheduling plan to perform bottleneck factor calculation, and optimize the teaching content structure and update the version based on the bottleneck factor.

[0112] Read the teaching content script configured under the semantic tag in the personalized training schedule, and extract the operation data recorded during the students' execution of the teaching content script.

[0113] Furthermore, the structured data of the personalized training scheduling plan associated with the students is parsed. Each training scheduling item corresponds to a training unit to be executed. The semantic tag number associated with each training scheduling item is extracted and used as the primary key to index the teaching content script. In the VR twin environment, the teaching content script is loaded into the spatial structure corresponding to the semantic tag area for interactive detection and initialization of the student's position coordinate sequence, orientation angle, input action and timestamp.

[0114] The student enters the semantic label area and starts to execute the teaching content script. Every time the student triggers an operation node, the student's behavior data is recorded and associated with the number of the action instruction of the current teaching content script. All behavior data is recorded as an operation data recording unit in the form of "timestamp-location-action-state", and continuous recording forms the student's operation data sequence.

[0115] The operation data in the operation data sequence are structured, and the average completion time, number of operation errors and number of repeated attempts of the operation elements in each teaching content script are extracted to generate a set of operation performance indicators.

[0116] Furthermore, for each teaching content script, the set of operation elements is divided according to the configuration of the standard operation trajectory and the action sequence of the standard operation trajectory, where each operation element corresponds to a semantic action, such as "turn the valve", "pull the rod", "confirm", etc.

[0117] The student operation data sequence formed by continuous recording is sliced ​​according to the trigger mark and timestamp information of the action, and each data segment is divided into operation metadata segments, where the operation metadata segments meet the following requirements: ; and ; in, Indicates the All operation data sequences in the teaching content script, Indicates the In the teaching content script Operands A subset of operational data collected during execution, Indicates the total number of operands contained in the teaching content script. Indicates the Operands, The symbol represents the set inclusion relation symbol, Represents a union operation.

[0118] Furthermore, the average completion time, number of operation errors, and number of repeated attempts are calculated for each operator, and all extracted indicators are unified to form a performance indicator set at the operator level.

[0119] The execution bottleneck factor of each operator is calculated based on the set of operation performance indicators.

[0120] Furthermore, the execution bottleneck factor of each operator is calculated by weight normalization.

[0121] Sort the execution bottleneck factors by their size to filter out high-bottleneck operators and construct a bottleneck factor distribution map.

[0122] Furthermore, all operands and corresponding execution bottleneck factors are stored as tuples to form a structured data list, and the structured data list is sorted in descending order according to the values ​​of the execution bottleneck factors to obtain a sorted structured data list.

[0123] Furthermore, all bottleneck factors are counted, and quantile analysis is performed based on the statistical distribution of all bottleneck factors. The quantile analysis results are used as the threshold for screening high-bottleneck operators.

[0124] For example, by using quartile analysis and taking Q3 as the threshold for screening high-bottleneck operators, only the worst-performing 25% of operators are screened to avoid misjudging accidental fluctuations or minor errors as bottlenecks.

[0125] The operators that meet the screening conditions are formed into a high bottleneck set and retained in the position of the sorted structured data list. The sorted operator index is used as the horizontal coordinate and the corresponding bottleneck factor value is used as the vertical coordinate. A line graph is drawn in the two-dimensional coordinate system to form a bottleneck factor distribution map, in which high bottleneck operators are represented by different colors.

[0126] Based on the teaching content script fragments of high-bottleneck operators in the bottleneck factor distribution map, the teaching content script is structurally optimized.

[0127] Furthermore, based on the bottleneck factor distribution map, the filtered high-bottleneck operation elements are analyzed according to the index call bound teaching content script fragments to obtain each teaching element corresponding to each teaching content script fragment, and each teaching element is optimized in combination with the actual behavior trajectory of the students.

[0128] Among them, the optimization operations include adding auxiliary guidance and adding interactive error correction prompts.

[0129] Among them, auxiliary guidance is added, for example, animation demonstrations or action trajectory lines are added based on the original voice prompts; interactive error correction prompts are added, for example, the correct demonstration is automatically displayed after an incorrect operation.

[0130] After the optimization operation is completed, the teaching elements are reconstructed and reorganized into structurally optimized teaching script fragments, the script metadata is updated, and it is marked as an optimized version.

[0131] Among them, the reconstruction process needs to ensure that the binding relationship between semantic labels, standard operation trajectories and action detection areas is not destroyed.

[0132] The structurally optimized teaching content script fragments are used to replace the original version and redeployed into the personalized training scheduling plan.

[0133] It should be noted that the verification of the training effect of personalized training scheduling on trainees is as follows: collecting the trainees' operation data under the optimization script, calculating the new bottleneck factor, and comparing it with the bottleneck factor before optimization. , then confirm that the optimization is effective, otherwise roll back the script to the original teaching content script.

[0134] in, represents the bottleneck factor before optimization, represents the optimized bottleneck factor, Threshold indicating a high bottleneck factor.

[0135] It should be noted that when the personalized training scheduling plan ends, the student's actual behavior comprehensive score will be recalculated. If the student's actual behavior comprehensive score is unqualified, the personalized training scheduling plan will be regenerated until the student's actual behavior comprehensive score is qualified.

[0136] S6: Based on the trainee’s final comprehensive score of actual behavior and risk record, perform job suitability level determination and archive the training evaluation report.

[0137] Read the trainee's actual behavior comprehensive score and the error operation record in the key risk space area to establish a score risk pair.

[0138] Furthermore, based on the completion of personalized training scheduling, the final comprehensive score of the actual behavior of the trainees is extracted, and the total number of wrong operations accumulated by the trainees in all key risk space areas is extracted to establish a scoring risk pair. .

[0139] Predefine a set of job suitability levels, set a judgment function, determine the trainee's job suitability level based on the scoring risk, and associate it with the job description information.

[0140] Furthermore, according to the safety level of each position, the predefined position adaptation level set is divided into level I adaptation position, level II adaptation position, level III adaptation position and unsuitable position, etc. Based on the requirements of each related position, a judgment function is set to judge the adaptation position corresponding to the trainee's actual behavior comprehensive score and the number of erroneous operations in the key risk space area.

[0141] For example, if ≥90 and If ≤1, it is considered as a Level I suitable position; If 70≤ <90 and ≤3, then the level II suitable position; If 60≤ <70 and ≤5, then the level III adaptation position; like <60 and >5, then the person is not suitable for the position.

[0142] Furthermore, based on the job adaptation level requirements of each job, the required comprehensive score of actual behavior, and the number of incorrect operations, a job information query table is constructed, wherein the job information query table includes the job title, operating authority range, security level requirements, etc.; the job information table is queried according to the trainee's job adaptation level to obtain related job description information that meets the needs.

[0143] The comprehensive scores of trainees’ actual behaviors, the number of erroneous operations in key risk space areas, job adaptation level scores, and related job description information are summarized to generate a structured training evaluation report data set.

[0144] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the real-life interactive safety teaching management method based on VR technology as proposed in the above embodiment.

[0145] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0146] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the real-life interactive safety teaching management method based on VR technology as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0147] In summary, the present invention realizes the quantitative evaluation of teaching feedback and targeted training optimization through a comprehensive scoring function and a personalized training scheduling mechanism; realizes the dynamic updating and precise reconstruction of teaching scripts through bottleneck factor analysis and content structure optimization; and realizes the high-precision tracking and deviation analysis of students' operating behaviors through behavior vector sequence and deviation function calculation.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A real-life interactive safety teaching management method based on VR technology, characterized by: include: Collect 3D point cloud data of the target teaching environment, combine it with panoramic images and semantic annotations, and construct a 3D spatial structure with semantic labels; Construct the three-dimensional space structure into a VR twin environment with interactive detection capabilities, and configure standard operation tracks, action detection areas, and teaching content scripts in each semantic label area; Collect students' operation data in the VR environment, construct behavior vector sequences, and generate students' actual behavior trajectories; Based on the difference between the standard operation trajectory and the trainee's actual behavior trajectory, a deviation function is constructed to record the number of incorrect operations and the operation qualification rate in the key risk space area. A comprehensive scoring function is used to comprehensively evaluate the trainee's behavior and generate a personalized training scheduling plan. Obtain the operation data of each teaching script in the personalized training scheduling plan to perform bottleneck factor calculation, and optimize the teaching content structure and version update based on the bottleneck factor; Based on the trainees' final comprehensive scores of actual behaviors and risk records, job suitability levels are determined and training evaluation reports are filed.

2. The VR-based real-life interactive safety teaching management method according to claim 1, characterized in that: The specific steps of constructing a three-dimensional space structure with semantic labels are as follows: Acquire 3D point cloud data, perform spatial alignment and noise filtering on the 3D point cloud data, and generate point cloud reconstruction data; Collecting texture image data of the target teaching environment, and performing spherical projection processing on the texture image data to obtain panoramic image data; Perform texture mapping on the point cloud reconstruction data and the panoramic image data in a unified coordinate system to generate a fused 3D model, and perform spatial structure boundary fitting on the fused 3D model; Perform spatial projection calculation on the structural boundaries in the fused 3D model, extract regional features, form a set of spatial candidate regions, extract geometric features from the set of spatial candidate regions and match them with usage rules to construct a set of semantic labels; The semantic label set is mapped to the spatial coordinate range in the fused 3D model to generate a 3D spatial structure with semantic labels.

3. The VR-based real-life interactive safety teaching management method according to claim 2, characterized in that: The three-dimensional space structure is constructed into a VR twin environment with interactive detection capabilities, and standard operation tracks, action detection areas, and teaching content scripts are configured in each semantic label area. The specific steps are as follows: Read the three-dimensional space structure with semantic tags, and import the three-dimensional space structure with semantic tags into the VR development engine to initialize the three-dimensional scene; In the VR development engine, a physical collision body is added to each semantically labeled area of ​​a 3D spatial structure with semantic labels to build a VR twin environment with interactive detection capabilities. In a VR twin environment with interactive detection capabilities, based on the task requirements and operation specifications within the semantic label area, a set of standard operation trajectories corresponding to the semantic labels is generated. For each standard operation trajectory in the standard operation trajectory set, a matching motion detection area is set to form a binding relationship table between the standard operation trajectory and the motion detection area; A teaching content script is configured for each semantic label area in the three-dimensional space structure with semantic labels in the binding relationship table between the standard operation trajectory and the action detection area.

4. The VR-based real-life interactive safety teaching management method according to claim 3, characterized in that: The specific steps of constructing a behavior vector sequence to generate the actual behavior trajectory of the trainee are as follows: Collect the trainee's position coordinates, orientation angles, and input actions in the VR twin environment to generate a preliminary set of operation data sequences. Normalize and filter the preliminary set of operation data sequences to construct a continuous behavior vector sequence. The behavior vector sequence is spliced ​​according to the time dimension to generate the student's actual behavior trajectory.

5. The VR-based real-life interactive safety teaching management method according to claim 4, characterized in that: The deviation function is constructed based on the difference between the standard operation trajectory and the actual behavior trajectory of the trainee, and the number of incorrect operations and the operation pass rate in the key risk space area are recorded. The specific steps are as follows: Align the standard operation trajectory with the trainee's actual behavior trajectory in time and space, calculate the behavioral deviation distance at each moment, and construct a behavioral deviation function; Calculating the time derivative of the behavior deviation function to obtain the first-order derivative of the behavior deviation function; Mark the critical risk space area in the standard operation trajectory, and count the number of deviations of the operator's actual behavior trajectory within the critical risk space area, and record it as the number of incorrect operations within the critical risk space area; Match the standard operation trajectory with the trainee's actual behavior trajectory in action sequence, calculate the action execution accuracy, and obtain the operation qualification rate.

6. The VR-based real-life interactive safety teaching management method according to claim 5, characterized in that: The comprehensive scoring function is used to comprehensively evaluate the student's behavior and generate a personalized training scheduling plan. The specific steps are as follows: The number of incorrect operations, the qualified operation rate, the behavior deviation function and the first-order derivative of the deviation function in the key risk space area are constructed into a comprehensive scoring function to calculate the actual comprehensive score of the trainees; The students' learning situation is evaluated based on their actual behavior comprehensive scores. If the students' actual behavior comprehensive scores are unqualified, the time period with the largest deviation value in the behavior deviation function is located and analyzed, the semantic label area is extracted, and a personalized training scheduling plan is generated.

7. The VR-based real-life interactive safety teaching management method according to claim 6, characterized in that: The operation data of each teaching script in the personalized training scheduling plan is obtained to perform bottleneck factor calculation, and the teaching content structure optimization and version update are performed based on the bottleneck factor. The specific steps are as follows: Read the teaching content script configured under the semantic tag in the personalized training schedule and extract the operation data recorded during the students' execution of the teaching content script; Structural processing of operation data is performed to extract the average completion time, number of operation errors, and number of repeated attempts of the operation elements in each teaching content script, and generate a set of operation performance indicators; Calculate the execution bottleneck factor of each operator based on the set of operation performance indicators, sort and filter out high-bottleneck operators according to the size of the bottleneck factor, and construct a bottleneck factor distribution map; Based on the teaching content script fragments of high-bottleneck operators in the bottleneck factor distribution map, the teaching content script is structurally optimized, and the structural optimization results are synchronously updated to the personalized training scheduling plan.

8. The VR-based real-life interactive safety teaching management method according to claim 7, characterized in that: The specific steps for determining the job suitability level and archiving the training evaluation report are as follows: Read the trainee's actual behavior comprehensive score and the error operation record in the key risk space area to establish the score risk pair; Predefine a set of job suitability levels, set a judgment function, determine the trainee's job suitability level based on the scoring risk, and associate it with the job description information; The comprehensive scores of trainees’ actual behaviors, the number of erroneous operations in key risk space areas, job adaptation level scores, and related job description information are summarized to generate a structured training evaluation report data set.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the real-life interactive safety teaching management method based on VR technology are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the real-life interactive safety teaching management method based on VR technology are implemented.

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