An engineering digital acceptance inspection simulation method and device based on VR technology and a medium

Abstract

The application discloses a kind of engineering digital acceptance simulation methods, equipment and medium based on VR technology, it is related to intelligent construction technical field, including the construction BIM model is imported into localization VR platform, asynchronous loading and format conversion are carried out by resource system, and physical parameter of material is bound, initialization physical calculus engine load working condition, and parameterized BIM model is generated;Real-time physical calculus is carried out through parameterized BIM model, and risk early warning is triggered in combination with structure response value, and defect thermodynamic diagram is generated by PBR rendering, and the accurate coordinate of high-risk area is obtained;High-risk area accurate coordinate is matched with the construction BIM model, and the initial pose of AR glasses is collected, space parameter calibration and virtual-real space matrix calculation are carried out after establishing AR space coordinate system, and virtual-real space conversion matrix is generated.The application enhances the intelligentization, precision and operability of engineering construction acceptance.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction technology, and in particular to a method, equipment and medium for digital acceptance simulation of engineering projects based on VR technology. Background Technology

[0002] With the development of information technology, Building Information Modeling (BIM) technology is increasingly widely used in engineering design, construction management, and facility operation and maintenance. BIM models created using 3D modeling software not only provide rich geometric information but also include a large number of non-geometric attributes, such as material properties and cost estimates, thereby enabling efficient management of the entire lifecycle of building projects. In recent years, Virtual Reality (VR) and Augmented Reality (AR) technologies have also begun to be gradually applied to the construction industry, showing great potential, especially in visualization, simulation training, and interactive experiences. How to effectively combine BIM models with VR / AR technologies and conduct in-depth data analysis and physical calculations on this basis has become a hot research area.

[0003] While existing technologies have attempted to combine BIM with VR / AR, several shortcomings remain. First, in terms of data processing, traditional integration methods typically employ a synchronous loading strategy, resulting in low efficiency when importing large-scale BIM models, especially under resource constraints, thus impacting user experience. Second, most current systems fail to fully utilize the rich physical parameters contained within BIM models for accurate real-time physical simulation and structural response analysis. These limitations restrict a comprehensive assessment of the intrinsic quality of engineering projects and hinder real-time risk prediction and defect detection. Summary of the Invention

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

[0005] Therefore, this invention provides a method, equipment, and medium for digital acceptance simulation of engineering based on VR technology, which solves the problems of low model loading efficiency and insufficient physical simulation accuracy in existing BIM and VR / AR integration.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] Firstly, this invention provides a VR-based digital acceptance simulation method for engineering projects, comprising: importing a construction BIM model into a domestic VR platform, asynchronously loading and converting the format through a resource system, binding material physical parameters, initializing the load conditions of the physical calculation engine, and generating a parametric BIM model; performing real-time physical calculations through the parametric BIM model, triggering risk warnings by combining structural response values, generating a defect heatmap through PBR rendering, and obtaining the precise coordinates of high-risk areas; matching the precise coordinates of high-risk areas with the construction BIM model, collecting the initial pose of AR glasses, establishing an AR spatial coordinate system, calibrating spatial parameters and calculating the virtual-real space matrix, and generating a virtual-real space transformation matrix; marking high-risk areas in AR visualization based on the virtual-real space transformation matrix, and obtaining an acceptance report through deviation analysis.

[0008] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the specific steps of importing the construction BIM model into the domestic VR platform and asynchronously loading and converting the format through the resource system are as follows:

[0009] Convert the construction BIM model to a new format to generate a construction BIM model that matches the new format.

[0010] Based on the construction BIM model with matching format, obtain the assigned loading tasks and generate a memory-optimized construction BIM model through asynchronous loading.

[0011] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the specific steps for binding material physical parameters, initializing the physical calculation engine load conditions, and generating a parametric BIM model are as follows:

[0012] Select memory-optimized construction BIM model components one by one, bind the corresponding material physical parameters, and generate a construction BIM model with physical parameter binding;

[0013] Add load conditions to the construction BIM model with physical parameter binding to generate a parametric BIM model.

[0014] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the steps of performing real-time physical calculations using a parametric BIM model, triggering risk warnings by combining structural response values, generating a defect heatmap through PBR rendering, and obtaining precise coordinates of high-risk areas are as follows:

[0015] The parametric BIM model is parsed by the physics calculation engine to generate a memory-resident parametric BIM model.

[0016] Real-time physical calculations are initiated on the memory-resident parametric BIM model, and combined with the structural response values, a parametric BIM model with structural response values ​​is generated.

[0017] Scan the abnormal node markers in the parametric BIM model with structural response values, and generate a parametric BIM model with risk warning markers based on the number of consecutive abnormal nodes.

[0018] The parametric BIM model with risk warning markers is rendered using PBR to generate a defect heatmap;

[0019] Based on the RGB channel values ​​in the defect heatmap, the spatial coordinates of high-risk areas are filtered, and the spatial coordinates are clustered to generate accurate coordinates of high-risk areas.

[0020] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the specific steps for matching the precise coordinates of the high-risk area with the construction BIM model and collecting the initial pose of the AR glasses are as follows:

[0021] Spatially match the precise coordinates of high-risk areas with the construction BIM model to generate high-risk area coordinates with BIM component IDs.

[0022] AR glasses are used to establish a spatial coordinate system at the physical engineering site, and the initial pose of the AR glasses is collected through sensor fusion algorithms.

[0023] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the steps of establishing the AR spatial coordinate system, calibrating spatial parameters, calculating the virtual-real space matrix, and generating the virtual-real space transformation matrix are as follows:

[0024] The initial pose of the AR glasses is transformed by quaternion to generate a rotation matrix. Combined with the calculation of the origin offset of the construction BIM model, the spatial parameters of the calibrated AR device are generated.

[0025] Based on the spatial parameters of the calibrated AR device, calculate the virtual-real space transformation matrix.

[0026] As a preferred embodiment of the VR-based engineering digital acceptance simulation method of the present invention, the steps of marking high-risk areas using AR visualization based on the virtual-real space transformation matrix and obtaining an acceptance report through deviation analysis are as follows:

[0027] Input the coordinates of high-risk areas with BIM component IDs into the virtual-real space transformation matrix to obtain the entity space coordinates, and mark the high-risk areas for AR visualization through PBR rendering;

[0028] Based on the high-risk area markers visualized by AR, deviation analysis is performed to generate an acceptance report for spatial deviation analysis.

[0029] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the engineering digital acceptance simulation method based on VR technology as described in the first aspect of the present invention.

[0030] Thirdly, 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, it implements any step of the engineering digital acceptance simulation method based on VR technology as described in the first aspect of the present invention.

[0031] The beneficial effects of this invention are as follows: by constructing a parametric BIM model and achieving precise mapping between virtual and real spaces, the physical simulation results are deeply integrated with AR on-site visualization, which not only improves the processing efficiency of large models and the ability to predict structural risks, but also realizes a closed loop from virtual analysis to real-world guidance, enhancing the intelligence, precision and operability of engineering construction and acceptance. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of a VR-based digital acceptance simulation method for engineering projects.

[0034] Figure 2 A flowchart for binding asynchronous loading with physical parameters.

[0035] Figure 3 A flowchart for obtaining a defect heatmap.

[0036] Figure 4 A flowchart for generating the virtual-real space transformation matrix. Detailed Implementation

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0038] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used 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 different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0040] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a method for simulating intelligent engineering acceptance based on VR technology, including the following steps:

[0041] S1: Import the construction BIM model into the domestic VR platform, asynchronously load and convert the format through the resource system, bind material physical parameters, initialize the load cases of the physical calculation engine, and generate a parametric BIM model. For details, please refer to [link to relevant documentation]. Figure 2 .

[0042] S1.1: Convert the construction BIM model to a new format to generate a construction BIM model that matches the new format;

[0043] Furthermore, the construction BIM model processes formats such as FBX and OBJ through automatic resource format recognition, performs compression to reduce storage space usage, bakes and optimizes lighting and shadow information, and converts it into a standardized format compatible with domestic VR platforms. LOD technology is applied to hierarchically simplify the geometric structure of the construction BIM model, ensuring resource quality balance at different viewing distances, and outputting a construction BIM model with a matching format.

[0044] S1.2: Based on the construction BIM model with matching format, obtain the assigned loading tasks and generate a memory-optimized construction BIM model through asynchronous loading;

[0045] Furthermore, based on format matching, the construction BIM model is divided into unit loading tasks, and loading is performed hierarchically according to spatial location priority. The first layer loads components in the core visible area, the next layer loads adjacent areas, and the outer areas are loaded with a delay. In the asynchronous mechanism, high-frequency components are preloaded into the memory cache, and low-frequency components are dynamically unloaded into the disk cache. The least recently used algorithm is used to manage the resource pool, maintain stable memory usage, and generate a memory-optimized construction BIM model.

[0046] It should be noted that the resource system employs a resource pool management mechanism to classify, store, and manage resources, ensuring resource orderliness and efficient retrieval. The resource pool allows for quick resource location and retrieval, while a caching mechanism prevents unnecessary duplicate loading.

[0047] S1.3: Select memory-optimized construction BIM model components one by one, bind the corresponding material physical parameters, and generate a construction BIM model with physical parameter binding.

[0048] Furthermore, memory-optimized construction BIM model components are selected one by one, and physical parameters from the material property library are associated with them. Metal components are bound to metallicity and gloss parameters, concrete components to roughness and normal map parameters, and glass components to refractive index and transparency parameters. By mapping the visual characteristics of materials through physically based rendering technology, it is ensured that the reflection of metal, the texture of wood, and the transmission effect of glass match the real physical behavior, generating a construction BIM model with bound physical parameters.

[0049] It should be noted that the material property library is built using physically based rendering technology. It employs physically accurate lighting models to simulate the propagation, reflection, and refraction of real light. It defines and stores metallicity parameters to control metallic gloss, roughness parameters to simulate surface texture details, and normal map parameters to enhance geometric bumpiness. These parameters directly map material visual effects such as metallic reflection, wood grain, and glass transparency. It also combines high dynamic range imaging and environment mapping technology to collect complex environmental lighting data, such as sky reflection and indirect lighting information, and integrates them into the material property library to ensure the physical consistency of different materials in the virtual scene. The physical parameters are sourced from standardized measurements and a database of real material properties.

[0050] S1.4: Add load conditions to the construction BIM model with physical parameter binding to generate a parametric BIM model.

[0051] Furthermore, load conditions are injected into the construction BIM model with physical parameter binding: static loads are bound to structural self-weight parameters, dynamic loads are bound to equipment vibration parameters, and environmental loads are bound to wind pressure and snow load parameters. During the initialization of the physics engine, calculation paths are assigned based on load type: gravity loads are directly connected to the rigid body dynamics calculation chain, and fluid loads trigger the particle system simulation chain. When multiple loads are superimposed, parallel computing pipelines are enabled to generate a parametric BIM model.

[0052] It should be noted that load conditions specifically refer to the set of physical force parameters acting on the construction BIM model, including three categories: static loads, dynamic loads, and environmental loads. Static loads correspond to the continuous pressure parameters generated by the structure's self-weight; dynamic loads correspond to the periodic force parameters generated by equipment operation vibrations; and environmental loads correspond to natural external force parameters such as wind pressure and snow loads. Load conditions are transformed into visual behaviors through a physics calculation engine. Static loads drive the rigid body dynamics calculation chain to simulate structural settlement; dynamic loads trigger the particle system to generate equipment vibration ripples; and environmental loads activate fluid dynamics algorithms to render wind and snow erosion effects. When multiple loads are superimposed, parallel computing pipelines simultaneously process the data streams of the gravity chain, fluid chain, and particle chain, ensuring real-time visualization of load interactions.

[0053] S2: Real-time physical calculations are performed using a parametric BIM model, and risk warnings are triggered by combining structural response values. Defect heatmaps are generated through PBR rendering (physically based rendering) to obtain the precise coordinates of high-risk areas.

[0054] S2.1: The parametric BIM model is parsed through the physics calculation engine to generate a memory-resident parametric BIM model.

[0055] Furthermore, the parametric BIM model is loaded into a memory pool via a physics engine, and the model data is categorized and stored using a resource pool management mechanism. The engine parses geometric topological relationships and load parameters, mapping structural self-weight, equipment vibration, and wind and snow loads into rigid body dynamics calculation chains. Vertex shading and physical attribute data are integrated through a unified rendering pipeline to generate a memory-resident parametric BIM model.

[0056] It should be noted that the unified rendering pipeline is the core processing architecture of physically based rendering technology, which includes three levels: the vertex processing stage converts the geometric coordinate data of the parametric BIM model, the geometry processing stage generates surface details and normal information, and the pixel processing stage performs material optical calculations. The pipeline integrates physical property data and optical rendering characteristics. In the vertex shading stage, load parameters are mapped to vertex displacement. In the pixel shading stage, metallicity parameters are converted into reflectivity, roughness parameters control specular scattering, and normal map parameters drive bump lighting response. Through a parallel computing architecture, the physical calculation data stream and the optical rendering data stream are processed simultaneously, realizing the hardware acceleration fusion of rigid body dynamics chain and PBR material chain on domestic GPUs.

[0057] S2.2: Initiate real-time physical calculations on the memory-resident parametric BIM model, and generate a parametric BIM model with structural response values ​​by combining the structural response values.

[0058] Furthermore, real-time physics calculations are initiated on the memory-resident parametric BIM model. Gravity loads trigger rigid body displacement calculation chains, dynamic loads activate particle systems to simulate vibration ripples, and environmental loads drive hydrodynamic erosion algorithms. The physics engine calculates structural deformation rate, stress distribution, and resonant frequency frame by frame, writes displacement gradients and stress peaks into vertex attribute buffers, and generates a parametric BIM model with structural response values.

[0059] S2.3: Scan the abnormal node markers in the parametric BIM model with structural response values, and perform risk warnings based on the number of consecutive abnormal nodes to generate a parametric BIM model with risk warning markers.

[0060] For further details, please refer to Figure 3 The system scans the vertex attribute buffer of the parametric BIM model with structural response values, detects the deformation rate, and marks nodes exceeding the deformation rate threshold as anomalous nodes. Based on the spatial clustering density of consecutive anomalous nodes, a three-level early warning mechanism is triggered: isolated nodes are marked in yellow, local clusters are marked in orange, and continuous areas are marked in red. The early warning markers are directly written into the normal map channel of the parametric BIM model, generating a parametric BIM model with risk early warning markers.

[0061] It should be noted that the deformation rate threshold is set based on the yield strength limit in the material physical parameter library, and dynamically adjusted in combination with load conditions. Under static loads, the standard yield strength of the material is used as the basic threshold. When dynamic loads are superimposed, the threshold is adjusted downward according to the vibration frequency and resonance effect of the equipment. In wind and snow load environments, a reduction factor is used to calibrate the threshold. Critical deformation data in the historical accident database are fitted into a spatial distribution function through machine learning, and the final output is the deformation rate threshold that varies with location.

[0062] S2.4: Generate a defect heatmap from the parametric BIM model with risk warning markers using PBR rendering.

[0063] Furthermore, the parametric BIM model with risk warning markers is input into the physically based rendering pipeline. The warning markers are converted into material optical properties: yellow warnings are mapped to attenuation of metallicity parameters, orange warnings to enhancement of roughness parameters, and red warnings to superimposed self-illumination parameters. Screen-space reflection technology is used to render the high-brightness diffuse reflection of deformed areas, and a defect heatmap is output.

[0064] S2.5: Filter the spatial coordinates of high-risk areas based on the RGB channel values ​​in the defect heatmap, and perform clustering on the spatial coordinates to generate accurate coordinates of high-risk areas.

[0065] Furthermore, the RGB channel values ​​of the defect heatmap are sampled using convolutional kernels, and the peak areas of the red channel are identified as high-risk areas. Spatial coordinates are clustered using an octree index to generate 3D bounding boxes centered on consecutive red pixels, and the centroid coordinates of these 3D bounding boxes are used as high-risk points. After Gaussian filtering to reduce noise, the precise coordinates of the high-risk areas are output.

[0066] S3: Match the precise coordinates of the high-risk area with the construction BIM model, collect the initial pose of the AR glasses, establish the AR spatial coordinate system, perform spatial parameter calibration and virtual-real space matrix calculation, and generate the virtual-real space transformation matrix.

[0067] S3.1: Spatially match the precise coordinates of the high-risk area with the construction BIM model to generate the coordinates of the high-risk area with BIM component IDs.

[0068] Furthermore, the precise coordinates of the high-risk area are spatially matched with the vertex positions of the construction BIM model components using an octree index. The nearest neighbor search algorithm is then executed to locate the geometry of the BIM component to which the coordinates belong. The unique identifier of the component is extracted and bound to the coordinate data to generate the coordinates of the high-risk area with the BIM component ID.

[0069] S3.2: Use AR glasses to establish a spatial coordinate system on the actual engineering site, and use a sensor fusion algorithm to collect the initial pose of the AR glasses.

[0070] Furthermore, AR glasses are deployed on-site to activate gyroscope, accelerometer, and magnetometer sensors. Sensor fusion algorithms are then initiated to process multi-source data streams in real time, fusing angle, acceleration, and magnetic field information to calculate initial position coordinates and attitude quaternions, establish a spatial coordinate system, and output the initial pose.

[0071] S3.3: Perform quaternion transformation on the initial pose of the AR glasses to generate a rotation matrix, and combine it with the calculation of the origin offset of the construction BIM model to generate the calibrated spatial parameters of the AR device.

[0072] Furthermore, the initial pose input attitude transformation algorithm performs a mathematical transformation from quaternions to a rotation matrix, generating a three-dimensional rotation matrix to represent the AR glasses' pose orientation; it loads the offset vector data of the origin position of the construction BIM model, representing the displacement of the spatial origin of the construction BIM model relative to the world coordinate system; it performs matrix multiplication of the rotation matrix and the offset vector, applies the homogeneous transformation formula to expand the rotation matrix into a homogeneous matrix and combines it with the offset vector, and outputs the calibrated AR device spatial parameters containing rotation and translation components. The AR device spatial parameters fully describe the pose calibration state of the AR device in virtual and real space.

[0073] S3.4: Calculate the virtual-real space transformation matrix based on the spatial parameters of the calibrated AR device.

[0074] For further details, please refer to Figure 4 The rotation matrix components and translation vector components in the calibrated AR device spatial parameters are input into the spatial transformation algorithm. The homogeneous coordinate transformation formula is applied to integrate the rotation matrix and translation vector into a single model view matrix. The single model view matrix maps the virtual construction BIM model coordinate system to the AR glasses entity space coordinate system through matrix multiplication. The rotation matrix handles direction alignment, and the translation vector handles position offset compensation, generating a virtual-real space transformation matrix. The virtual-real space transformation matrix realizes a precise linear transformation from virtual model coordinates to entity space coordinates.

[0075] S4: Based on the virtual-real space transformation matrix, mark high-risk areas using AR visualization and obtain an acceptance report through deviation analysis.

[0076] S4.1: Input the coordinates of high-risk areas with BIM component IDs into the virtual-real space transformation matrix to obtain the entity space coordinates, and mark the high-risk areas for AR visualization through PBR rendering.

[0077] Furthermore, the coordinates of high-risk areas with BIM component IDs are input into a virtual-to-real space transformation matrix to perform homogeneous coordinate transformation, calculating the three-dimensional coordinates of the entity space. The physically based rendering pipeline loads these entity space coordinates. Red warning coordinates activate self-illumination parameters to generate pulsed red light effects, orange warning coordinates enhance roughness parameters to form granular highlight areas, and yellow warning coordinates attenuate metallic parameters to present semi-transparent ripples. These are then projected in real-time into the AR glasses' field of view using screen space reflection technology, completing the high-risk area marking.

[0078] S4.2: Based on the high-risk area markers in AR visualization, perform deviation analysis and generate an acceptance report for spatial deviation analysis.

[0079] Furthermore, the high-risk area markers visualized by AR are spatially registered with the design coordinates of the construction BIM model, and a sensor fusion algorithm is activated to calculate the Euclidean distance deviation of the marker points. Static load area deviation values ​​are mapped to the structural settlement acceptance form, dynamic load area deviation values ​​are written into the vibration frequency compliance report, and environmental load area deviation values ​​are filled into the wind and snow load tolerance form. Finally, these are integrated into a three-dimensional spatial coordinate deviation matrix, outputting an acceptance report on spatial deviation analysis.

[0080] 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 the computer-executable instructions to realize the engineering digital acceptance simulation method based on VR technology proposed in the above embodiment.

[0081] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices 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 the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0082] This embodiment also provides a storage medium storing a computer program. When executed by a processor, this program implements the VR-based engineering digital acceptance simulation method proposed in the above embodiments. 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 Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0083] In summary, this invention, by constructing a parametric BIM model and achieving precise mapping between virtual and real spaces, deeply integrates physical simulation results with AR on-site visualization. This not only improves the processing efficiency of large-scale models and the ability to predict structural risks, but also realizes a closed loop from virtual analysis to real-world guidance, enhancing the intelligence, precision, and operability of engineering construction and acceptance.

[0084] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for digital acceptance simulation of engineering projects based on VR technology, characterized in that: include: The construction BIM model is imported into the domestic VR platform, asynchronously loaded and converted in format through the resource system, and material physical parameters are bound. The physical calculation engine load conditions are initialized to generate a parametric BIM model. Real-time physical calculations are performed using a parametric BIM model, and risk warnings are triggered by combining structural response values. Defect heatmaps are generated through PBR rendering to obtain the precise coordinates of high-risk areas. The precise coordinates of high-risk areas are matched with the construction BIM model, and the initial pose of the AR glasses is collected. After establishing the AR spatial coordinate system, spatial parameters are calibrated and the virtual-real space matrix is ​​calculated to generate the virtual-real space transformation matrix. The specific steps are as follows: The initial pose of the AR glasses is transformed by quaternion to generate a rotation matrix. Combined with the calculation of the origin offset of the construction BIM model, the spatial parameters of the calibrated AR device are generated. Based on the spatial parameters of the calibrated AR device, calculate the virtual-real space transformation matrix; Based on the virtual-real space transformation matrix, high-risk areas are marked using AR visualization, and an acceptance report is obtained through deviation analysis. The specific steps are as follows: Input the coordinates of high-risk areas with BIM component IDs into the virtual-real space transformation matrix to obtain the entity space coordinates, and mark the high-risk areas for AR visualization through PBR rendering; Based on the high-risk area markers visualized by AR, deviation analysis is performed to generate an acceptance report for spatial deviation analysis.

2. The engineering digital acceptance simulation method based on VR technology as described in claim 1, characterized in that: The specific steps for importing the construction BIM model into the domestic VR platform and performing asynchronous loading and format conversion through the resource system are as follows: Convert the construction BIM model to a new format to generate a construction BIM model that matches the new format. Based on the construction BIM model with matching format, obtain the assigned loading tasks and generate a memory-optimized construction BIM model through asynchronous loading.

3. The engineering digital acceptance simulation method based on VR technology as described in claim 2, characterized in that: The steps for binding material physical parameters, initializing the physical calculation engine load cases, and generating a parametric BIM model are as follows: Select memory-optimized construction BIM model components one by one, bind the corresponding material physical parameters, and generate a construction BIM model with physical parameter binding; Add load conditions to the construction BIM model with physical parameter binding to generate a parametric BIM model.

4. The engineering digital acceptance simulation method based on VR technology as described in claim 3, characterized in that: The specific steps for performing real-time physical calculations using a parametric BIM model and triggering risk warnings based on structural response values ​​are as follows: The parametric BIM model is parsed using a physics calculation engine to generate a memory-resident parametric BIM model. Real-time physical calculations are initiated on the memory-resident parametric BIM model, and combined with the structural response values, a parametric BIM model with structural response values ​​is generated. Scan the abnormal node markers in the parametric BIM model with structural response values, and generate a parametric BIM model with risk warning markers based on the number of consecutive abnormal nodes.

5. The engineering digital acceptance simulation method based on VR technology as described in claim 4, characterized in that: The specific steps for generating a defect heatmap using PBR rendering and obtaining the precise coordinates of high-risk areas are as follows: The parametric BIM model with risk warning markers is rendered using PBR to generate a defect heatmap; Based on the RGB channel values ​​in the defect heatmap, the spatial coordinates of high-risk areas are filtered, and the spatial coordinates are clustered to generate accurate coordinates of high-risk areas.

6. The engineering digital acceptance simulation method based on VR technology as described in claim 5, characterized in that: The specific steps for matching the precise coordinates of high-risk areas with the construction BIM model and acquiring the initial pose of the AR glasses are as follows: Spatially match the precise coordinates of high-risk areas with the construction BIM model to generate high-risk area coordinates with BIM component IDs. AR glasses are used to establish a spatial coordinate system at the physical engineering site, and the initial pose of the AR glasses is collected through sensor fusion algorithms.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the engineering digital acceptance simulation method based on VR technology as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the engineering digital acceptance simulation method based on VR technology as described in any one of claims 1 to 6.

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