An Engineering Design Modeling Method and System Based on BIM Visualization Technology

Abstract

This invention discloses an engineering design modeling method and system based on BIM visualization technology, belonging to the field of building information modeling technology. The method includes: assembling parametric component family files into a BIM model; performing collision detection; dynamically generating avoidance schemes; and performing lightweight processing to generate a 3D lightweight BIM model. The method also involves locating optimization areas and extracting geometric parameters, combining these with a mechanical property dataset, generating an engineering modeling scheme from the 3D lightweight BIM model, and simultaneously performing 3D verification. Based on the verification results, the method dynamically optimizes the engineering modeling scheme. Furthermore, this invention dynamically determines the stress state of components using real-time location information and generates a high-stress area boundary dataset, achieving accurate location and visual early warning of structural anomalies. This replaces traditional static analysis, shortens the design iteration cycle, and improves structural safety.

Description

Technical Field

[0001] This invention relates to the field of building information modeling technology, and in particular to an engineering design modeling method and system based on BIM visualization technology. Background Technology

[0002] BIM technology, through 3D visualization modeling, enables the digital representation and information integration of building components, providing crucial support for collaborative engineering design, clash detection, construction simulation, and operation and maintenance management. In recent years, the deep integration of BIM with cutting-edge technologies such as artificial intelligence, big data, and the Internet of Things has further promoted the intelligent and refined management of engineering projects. For example, in complex structural design, BIM technology can optimize component layout through parametric modeling and improve structural performance through mechanical simulation analysis; during the construction phase, BIM+5G technology enables remote hoisting and dynamic progress monitoring of large components; and during the operation and maintenance phase, digital twin technology empowers the health monitoring and risk warning of building structures.

[0003] Current BIM technology, in the engineering modeling process, mainly relies on static design parameters and preset rules for component layout and mechanical verification, making it difficult to respond in real time to the impact of changes in the construction environment or fluctuations in material properties on the stress distribution of the structure. Furthermore, traditional parametric design methods typically rely on empirical formulas or finite element analysis results for local optimization, lacking the ability to dynamically adjust the global structural topology, resulting in long design iteration cycles and limited optimization effectiveness. Simultaneously, in the collision detection and avoidance scheme generation process, existing BIM models often rely on manual intervention or fixed algorithms, failing to fully integrate mechanical property datasets and geometric parameters for automated optimization, leading to deviations in the mechanical performance verification of the lightweighted model. Summary of the Invention

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

[0005] Therefore, this invention provides an engineering design modeling method based on BIM visualization technology to solve the problems of component stress response lag, low structural optimization efficiency, and deviation of mechanical properties in lightweight models in existing BIM technologies.

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

[0007] In a first aspect, the present invention provides an engineering design modeling method based on BIM visualization technology, which includes,

[0008] Collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information;

[0009] Based on a dataset of engineering components with location information, the stress state of the components is dynamically determined to drive the loading of the BIM model, and a visual stress cloud map is constructed to generate a dataset of high-stress area boundaries.

[0010] Based on the high-stress region boundary dataset, the structural topology is optimized in the parameter space using reinforcement learning methods, generating a parameterized component family file and a mechanical verification report.

[0011] The parametric component family file is assembled into the BIM model, collision detection is performed, avoidance schemes are dynamically generated, and lightweight processing is performed to generate a 3D lightweight BIM model.

[0012] The optimization area is located and geometric parameters are extracted. Combined with the mechanical property dataset, an engineering modeling scheme is generated through a 3D lightweight BIM model. At the same time, 3D verification is performed, and the engineering modeling scheme is dynamically optimized based on the verification results.

[0013] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the engineering component dataset with location information is generated by extracting the spatial correlation coefficient of engineering data component features.

[0014] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the specific steps for dynamically determining the stress state of components based on a dataset of engineering components with location information to drive the loading of the BIM model are as follows.

[0015] The dataset of engineering components with location information is matched with the BIM component mechanical property library to dynamically establish the association and generate a dataset that integrates location and mechanical properties.

[0016] Based on the fusion of location and mechanical property datasets, stress state labels are dynamically marked by the ratio of stress values ​​to generate a stress state label dataset;

[0017] The accuracy level of the BIM model is dynamically selected based on the stress value state labels in the stress state label dataset, and a hierarchical BIM model set is generated.

[0018] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the specific steps for constructing a visualized stress cloud map and generating a high-stress area boundary dataset are as follows.

[0019] Based on a hierarchical BIM model set, the real-time coordinates of high-stress risk components are mapped onto the surface of the BIM model, the stress value is dynamically converted to a color value, and a high-stress area pulsation warning effect is added to generate a visual stress cloud map.

[0020] Extract the coordinates of the polygon vertices of the isosurface geometry that equals the boundary stress from the visualized stress cloud map, and attach the stress extremum attribute of the corresponding region to generate a high-stress region boundary dataset.

[0021] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the specific steps of optimizing the structural topology in the parameter space using reinforcement learning methods based on the high-stress region boundary dataset to generate a parametric component family file and a mechanical verification report are as follows.

[0022] Based on the coordinates of the polygon vertices of the isosurface geometry in the high-stress region boundary dataset, and combined with the stress extremum attribute, the parameter space range and constraint conditions are defined to generate a parameter space constraint file.

[0023] Based on the parameter space constraint file, the coordinates of polygon vertex are discretized into a mesh matrix, and structural parameter adjustment actions are defined, a reward function is established, and the optimal parameter combination file is generated by filtering.

[0024] Input the optimal parameter combination file into the trapezoidal stiffening rib template, perform parameter-driven modeling, and perform assembly verification to generate a parameterized component family file.

[0025] Apply amplified design loads to the parametric component family file, extract key index data, and generate a mechanical verification report.

[0026] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the steps of assembling parametric component family files into the BIM model, performing collision detection, and dynamically generating avoidance schemes are as follows.

[0027] Parse the parametric component family file, match the beam and column components at the corresponding coordinate positions in the BIM model, perform precise alignment, and generate the assembled BIM model.

[0028] Based on the assembled BIM model, collision rules are set, and a full-discipline collision scan is performed to identify collision types, mark the 3D coordinates of collision locations, and generate a BIM model with collision markers.

[0029] Based on the BIM model with collision markers, the optimal avoidance path is obtained through gradient descent, and dynamic avoidance is performed to generate the optimized BIM model.

[0030] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the step of performing lightweight processing to generate a three-dimensional lightweight BIM model refers to performing model simplification operations, compressing texture maps, removing hidden components and non-load-bearing structural patches based on the optimized and avoided BIM model, and generating a three-dimensional lightweight BIM model.

[0031] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the steps of locating and optimizing the region, extracting geometric parameters, combining them with a mechanical property dataset, and generating an engineering modeling scheme through a three-dimensional lightweight BIM model are as follows.

[0032] Locate the optimization region and extract its geometric parameters, read the mechanical property dataset, and generate an optimization region feature report;

[0033] Based on the geometric parameters in the optimized regional feature report, construction drawings are drawn using a 3D lightweight BIM model, and an engineering modeling scheme is generated by combining the mechanical property dataset.

[0034] As a preferred embodiment of the engineering design modeling method based on BIM visualization technology described in this invention, the specific steps for performing 3D verification and dynamically optimizing the engineering modeling scheme based on the verification results are as follows.

[0035] The structural stress extrema are mapped to the vertex shader of the 3D lightweight BIM model, interactive stress visualization rules are configured, and component decomposition function is built to generate a 3D verification program.

[0036] By operating and verifying the scene file through VR devices, when an abnormal stress area is detected, the abnormal coordinate data is recorded to generate a reinforcement plan and an optimized engineering modeling plan document is generated.

[0037] Secondly, this invention provides an engineering design modeling system based on BIM visualization technology, comprising,

[0038] The data processing module is used to collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information.

[0039] The stress assessment module is used to dynamically assess the stress state of engineering components based on a dataset of engineering components with location information to drive the loading of the BIM model, construct a visual stress cloud map, and generate a dataset of high-stress area boundaries.

[0040] The topology optimization module is used to optimize the structural topology in the parameter space based on the high-stress region boundary dataset using reinforcement learning methods, and generate parameterized component family files and mechanical verification reports.

[0041] The model processing module is used to assemble parametric component family files into the BIM model, perform collision detection, dynamically generate avoidance schemes, and perform lightweight processing to generate a three-dimensional lightweight BIM model.

[0042] The scheme generation module is used to locate the optimization area and extract geometric parameters. Combined with the mechanical property dataset, it generates an engineering modeling scheme through a 3D lightweight BIM model, performs 3D verification, and dynamically optimizes the engineering modeling scheme based on the verification results.

[0043] The beneficial effects of this invention are as follows: By dynamically judging the stress state of components through real-time location information and generating a high-stress region boundary dataset, it achieves accurate location and visual early warning of structural anomalies, replacing traditional static analysis, shortening the design iteration cycle, and improving structural safety. Furthermore, by optimizing the structural topology through reinforcement learning to generate parameterized component family files, it achieves data-driven global intelligent optimization, replacing empirical formula design, reducing manual costs, and improving the model's mechanical performance and adaptability. Attached Figure Description

[0044] 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.

[0045] Figure 1 This is a flowchart of an engineering design modeling method based on BIM visualization technology.

[0046] Figure 2 This is a schematic diagram of an engineering design modeling system based on BIM visualization technology.

[0047] Figure 3 A flowchart for generating a visualization of stress cloud diagrams.

[0048] Figure 4 This is a flowchart for the generation and verification of parametric components. Detailed Implementation

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Reference Figures 1-4 This is one embodiment of the present invention, which provides an engineering design modeling method based on BIM visualization technology, including the following steps:

[0053] S1: Collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information;

[0054] S1.1: Engineering data includes the geometric properties of components, real-time location information, mechanical performance parameters, and management business data;

[0055] It should be noted that engineering data acquisition involves the geometric properties of components, such as component dimensions, shape parameters, and spatial coordinate data; real-time location information, such as the original location dataset; mechanical performance parameters, such as maximum stress value data, safety margin data, and material yield strength data; and management business data, such as construction progress node data, material cost data, and quality acceptance document data.

[0056] S1.2: Preprocessing includes data cleaning and timestamp alignment;

[0057] It should be noted that the data cleaning operation performs median filtering on the original dataset with a window size of 5 to eliminate impulse noise and remove outliers with excessively large velocity changes in the original UWB positioning dataset; the timestamp alignment operation unifies the time reference between the original nine-axis inertial sensor dataset and the original UWB positioning dataset through an interpolation algorithm.

[0058] S1.3: Extract the spatial correlation coefficient of the engineering data components and generate an engineering component dataset with location information.

[0059] Furthermore, a spatiotemporal fusion algorithm based on time-aligned nine-axis sensor data and spatially calibrated UWB positioning data is used to obtain the spatial correlation coefficient of component features; combined with the mechanical properties of BIM components and historical stress extreme value records, an engineering component dataset with location information is generated, including the component's unique identifier, real-time coordinate X value, real-time coordinate Y value, real-time coordinate Z value, component spatial correlation coefficient value, design strength value, and historical stress extreme value record fields.

[0060] S2: Based on the engineering component dataset with location information, dynamically determine the stress state of the component to drive the loading of the BIM model, and build a visual stress cloud map to generate a high-stress area boundary dataset.

[0061] S2.1: Match the data of the engineering component dataset with location information with the BIM component mechanical property library, dynamically establish the association relationship, and generate a dataset that integrates location and mechanical properties;

[0062] Furthermore, the unique identifier of the component is used to match the engineering component dataset with location information with the BIM component mechanical property knowledge graph. When the spatial correlation coefficient of the component is greater than the set correlation coefficient value (example value: 0.8, based on the four-dimensional constraints of engineering measurement accuracy standards, mechanical matching reliability, dynamic response requirements, and economic balance), the real-time coordinate X value, real-time coordinate Y value, and real-time coordinate Z value are bound to the design strength value and historical stress extreme value record in the BIM component mechanical property knowledge graph. The binding relationship is a dynamic coupling relationship between spatial coordinates and mechanical properties, realizing real-time interactive verification of geometric positioning data and material properties. The data fusion operation generates structured fields: component unique identifier field, real-time coordinate set, design strength value, and historical stress extreme value record field. Finally, a fused location and mechanical property dataset is generated.

[0063] S2.2: Based on the fusion of location and mechanical property datasets, stress state labels are dynamically marked by the ratio of stress values ​​to generate a stress state label dataset;

[0064] Furthermore, a safety margin value is obtained by calculating the ratio of the design strength value to the maximum stress value in the historical stress extreme value record. When the safety margin value is less than the safety margin threshold (example value 1.2, determined by multi-objective optimization convergence through reinforcement learning method), the stress state label is marked as a high-stress component. When the safety margin value is greater than or equal to the safety margin threshold, the stress state label is marked as a safe component. The component unique identifier field, real-time coordinate X value, real-time coordinate Y value, real-time coordinate Z value and stress state label field are bound together. Finally, a stress state label dataset is generated.

[0065] S2.3: Dynamically select the BIM model accuracy level based on the stress value state labels in the stress state label dataset, and generate a hierarchical BIM model set;

[0066] Furthermore, the stress state labels in the stress state label dataset are read. When the label field is a high-stress component, the LOD450 level BIM model accuracy is selected, and when the label field is a safe component, the LOD350 level BIM model accuracy is selected. The BIM model loading operation is performed, the texture compression rate and geometric simplification values ​​are set, and a hierarchical BIM model set is generated.

[0067] It should be noted that the LOD350 level BIM model accuracy level refers to the precise geometric expression that reaches the depth of construction drawing design (e.g., beam web height 800mm, flange thickness 20mm), which includes the basic dimensions and positional relationships of components but lacks construction details; the LOD450 level BIM model accuracy level adds construction-level detailed details on the basis of LOD350 (e.g., bolt hole diameter ±0.5mm, stiffening rib gradient size 280→450mm), which includes precise information on hole positions and bevels required for manufacturing and installation. The core difference between the two is that LOD350 is used for design coordination and quantity statistics, while LOD450 directly guides prefabrication and on-site installation.

[0068] The purpose of setting the texture compression rate and geometry simplification threshold (e.g., texture compression rate set to 85%, geometry simplification threshold set to 0.1mm) is to compress texture maps (e.g., compress the original size by 30%) to reduce storage and transmission load, while preserving the accuracy of the outer contour of key features at LOD350 level, ensuring efficient loading of the 3D lightweight BIM model on mobile and web devices, and maintaining the rendering accuracy of the visualized stress cloud map.

[0069] S2.4: Based on the hierarchical BIM model set, the real-time coordinates of high-stress risk components are mapped to the surface of the BIM model, the stress value is dynamically converted to the color value, and a high-stress area pulsation warning effect is added to generate a visual stress cloud map.

[0070] Specifically, based on a hierarchical BIM model set, the real-time X, Y, and Z coordinates of the components are mapped to the vertices of the BIM model surface using the Kriging interpolation method. The material yield strength region is color-coded according to three stress thresholds. For example, the range of the first-level stress threshold is set to [0.8, 1.0], the range of the second-level stress threshold is [0.6, 0.8), and the range of the third-level stress threshold is [0, 0.6]. When the stress value is within the first-level stress threshold range, the material yield strength region is rendered in red; when the stress value is within the second-level stress threshold range, the material yield strength region is rendered in yellow; and when the stress value is within the third-level stress threshold range, the material yield strength region is rendered in green. A pulsating warning halo effect is superimposed on the red region. Finally, a visualized stress cloud map is generated.

[0071] It should be noted that the three stress thresholds are set based on the scientific classification of the material's yield strength. The first threshold corresponds to the critical point of plastic deformation, the second threshold corresponds to the fatigue strength limit, and the third threshold ensures the elastic working state.

[0072] S2.5: Extract the coordinates of the polygon vertices of the isosurface geometry that are equal to the boundary stress in the visualized stress cloud map, and attach the stress extremum attribute of the corresponding region to generate a high-stress region boundary dataset.

[0073] Specifically, based on the visualized stress cloud map, the isosurface geometry equal to the boundary stress is extracted, the vertex index sequence of the isosurface triangular mesh is traversed, and the polygon vertex coordinate data is recorded; at the same time, the stress extreme value attribute data of the corresponding region of the isosurface is retrieved; the polygon vertex coordinate data and stress extreme value attribute data are encapsulated, including type field, geometry type field, coordinate array field and attribute field; finally, a high-stress region boundary dataset is generated.

[0074] It should be noted that the operation of extracting the isosurface geometry equal to the boundary stress is as follows: the three-dimensional stress field is discretized into cubic mesh nodes (example value: 1 cm), and the stress value of the vertex in each voxel node is sampled; then, the spatial interpolation of the boundary stress value and the vertex stress value is obtained, and the coordinates of the intersection point of the isosurface and the voxel edge are determined; then, the intersection points of adjacent voxel nodes are connected to form a triangular facet topology, and the polygon vertex coordinate sequence of the isosurface geometry is generated.

[0075] S3: Based on the high-stress region boundary dataset, optimize the structural topology in the parameter space using reinforcement learning methods to generate a parameterized component family file and a mechanical verification report;

[0076] S3.1: Based on the coordinates of the polygon vertices of the isosurface geometry in the high-stress region boundary dataset, and combined with the stress extremum attribute, define the parameter space range and constraint conditions, and generate a parameter space constraint file;

[0077] Specifically, the algorithm reads the polygon vertex coordinates field of the isosurface geometry in the high-stress region boundary dataset to obtain the minimum bounding rectangle range; simultaneously, it extracts the stress extremum attribute data from the attribute field; defines the parameter space range: sets the beam height parameter range, sets the flange thickness parameter range, and sets the stiffener spacing parameter range; sets geometric constraints: the stiffener positions must be within the minimum bounding rectangle range; and sets mechanical constraints: the optimized stress value and the design strength value; finally, it encapsulates the parameter range data, bounding rectangle coordinate data, and stress value data into a JSON format parameter space constraint file.

[0078] S3.2: Based on the parameter space constraint file, the coordinates of the polygon vertex are discretized into a mesh matrix, and the structural parameter adjustment actions are defined, a reward function is established, and the optimal parameter combination file is generated by filtering.

[0079] Specifically, the process involves reading the boundary rectangle coordinates, stress values, and parameter range data from the parameter space constraint file; discretizing the polygon vertex coordinates within the boundary rectangle coordinates into a 20-row × 20-column grid matrix, and assigning average stress values ​​to each grid node; defining structural parameter adjustment actions: beam height adjustment, flange thickness enhancement, and stiffener densification, and establishing reward function calculation rules; selecting the parameter combination with the highest reward value; and finally generating the optimal parameter combination file, which includes specific parameter values ​​and stress verification estimates.

[0080] The reward function calculation rule is as follows:

[0081] ;

[0082] in, This represents the overall reward value. Indicates the initial stress extremum. This represents the extreme stress value after optimization. Indicates the quality of the newly added materials. This indicates the total mass of the component.

[0083] It should be noted that 0.7 represents the safety weight coefficient and 0.3 represents the economic weight coefficient. These values ​​are set based on engineering failure case analysis and parameter optimization marginal effect verification, and are determined by solving the Pareto front to find the optimal balance between safety and economy.

[0084] S3.3: Input the optimal parameter combination file into the trapezoidal stiffening rib template, perform parameter-driven modeling operation, perform assembly verification, and generate a parameterized component family file;

[0085] Specifically, the process reads the beam height, flange thickness, and stiffener spacing values ​​from the optimal parameter combination file; calls the trapezoidal stiffener template, inputs the beam height value into the template's beam height parameter slot, the flange thickness value into the flange thickness parameter slot, and the stiffener spacing value into the stiffener spacing parameter slot; executes parameter-driven modeling operations to perform assembly verification: checks whether the gap between the stiffener and the beam web is greater than or equal to the standard value (example value: 10 mm, the optimal engineering solution determined by comprehensively considering the minimum operating space requirements of welding processes, steel thermal deformation compensation, and fatigue performance verification), and finally generates a parametric component family file.

[0086] It should be noted that the parameter-driven modeling operation process is as follows: the upper width parameter is set to the preset gradient start value (example value: 280 mm) to trigger the gradient deformation mechanism; the lower width parameter is set to the preset gradient end value (example value: 450 mm) to trigger the bottom flaring reinforcement mechanism; and the height parameter is set to a fixed value (example value: 300 mm) to trigger the uniform section stretching mechanism. The preset gradient start value and preset gradient end value are set by the structural requirements of the web stiffening ribs and the constraints of the cold bending forming process.

[0087] S3.4: Apply amplified design loads to the parametric component family file, extract key index data, and generate a mechanical verification report.

[0088] Specifically, an amplified reference load is applied to the parametric component family file, and key index data is extracted after static structural analysis is performed: maximum stress value data is obtained by scanning the nodal stress cloud map, displacement deformation data is obtained by beam end deflection monitoring points, and a mechanical verification report is generated that includes a unique component identifier field, an optimization scheme description field, maximum stress value data and its reduction percentage data, safety margin data and its specification compliance conclusion, and a comparison of displacement deformation data with allowable values.

[0089] S4: Assemble the parametric component family file into the BIM model, perform collision detection, dynamically generate avoidance schemes, and perform lightweight processing to generate a 3D lightweight BIM model.

[0090] S4.1: Parse the parametric component family file, match the beam and column components at the corresponding coordinate positions in the BIM model, perform precise alignment, and generate the assembled BIM model;

[0091] Specifically, the parametric component family file is parsed, the spatial position data of beam and column components are scanned in the BIM model, and beam web components that are too close to the target coordinates are matched using a spatial indexing algorithm. The coordinate transformation parameters, translation vector and rotation angle, are obtained, and a high-precision alignment operation is performed: the trapezoidal stiffening rib component is moved along the translation vector and rotated around the Z-axis, the bolt hole alignment and component gap are checked, and finally the assembled BIM model is generated, which includes the topological connection relationship between the trapezoidal stiffening rib and the beam and column components and the bolt hole alignment mark.

[0092] S4.2: Based on the assembled BIM model, set collision rules, perform a full-discipline collision scan, identify collision types, mark the 3D coordinates of collision locations, and generate a BIM model with collision markers;

[0093] Furthermore, based on the assembled BIM model, collision rules are set: the minimum clearance between structural components and MEP pipelines is set, and collision types are distinguished as hard collisions and gap collisions; a full-discipline collision scan is performed, including building structure, HVAC piping, and electrical cable trays; collision types are identified and locations are marked: hard collision points are marked with red cubes to indicate their 3D coordinates, and gap collision points are marked with yellow spheres, generating a BIM model with collision markers.

[0094] S4.3: Based on the BIM model with collision markers, obtain the optimal avoidance path through the gradient descent method, perform dynamic avoidance, and generate the optimized avoidance BIM model;

[0095] Furthermore, the 3D coordinates of collision points and collision components in the BIM model with collision markers are read, and the optimal avoidance path is calculated using the gradient descent algorithm: a path length function and a collision penalty function are established, and the pipeline path coordinates are iteratively adjusted along the negative gradient direction in 3D space. When a hard collision point is detected, a vertical avoidance strategy is prioritized, and a horizontal avoidance strategy is switched when vertical space is insufficient. After dynamically generating the avoidance scheme, the minimum clearance is re-verified to ensure it meets the standard (e.g., greater than 0.050 meters) and the slope change meets the standard (e.g., less than 0.5%). Finally, the optimized avoidance BIM model is generated.

[0096] The formula for calculating the optimal avoidance path is:

[0097] ;

[0098] in, This represents the optimal avoidance path. Indicates the total path length. This represents the collision penalty value. Indicates the rate of change of slope. This represents the path length weighting coefficient (example value: 0.7). This represents the collision risk weighting coefficient (example value: 0.3). This represents the slope variation weighting coefficient (example value: 0.1).

[0099] S4.4: Based on the optimized and avoided BIM model, perform model simplification operations, compress texture maps, remove hidden components and non-load-bearing structural patches, and generate a three-dimensional lightweight BIM model.

[0100] Specifically, based on the optimized and avoided BIM model, model simplification operations are performed: the number of geometric patches is reduced by using lightweight tools while preserving the accuracy of key feature outlines; texture map sizes are compressed using a standard compression format; hidden components and non-load-bearing structural patches are removed; finally, a 3D lightweight BIM model is generated, which includes compressed geometric data, texture maps, and lightweight parameter logs.

[0101] S5: Locate the optimization area and extract geometric parameters. Combine the mechanical property dataset to generate an engineering modeling scheme through a 3D lightweight BIM model. At the same time, perform 3D verification and dynamically optimize the engineering modeling scheme based on the verification results.

[0102] S5.1: Locate the optimization region and extract geometric parameters, read the mechanical property dataset, and generate an optimization region feature report;

[0103] Specifically, in the 3D lightweight BIM model, the optimized area components are located, and the vertex coordinate data and the topological relationship of the triangular facets are analyzed; geometric parameter data are extracted: the upper width value of the trapezoidal stiffener, the lower width value of the trapezoidal stiffener, the height value of the trapezoidal stiffener, and the flange thickness value; the mechanical property dataset is read: the maximum stress value data, the safety margin data, and the displacement deformation data; and the geometric parameter data and mechanical property data are integrated to generate an optimized area feature report.

[0104] S5.2: Based on the geometric parameters in the optimized area feature report, draw construction drawings using a 3D lightweight BIM model, and generate an engineering modeling scheme by combining the mechanical property dataset;

[0105] Specifically, based on the set of geometric parameters in the optimized regional feature report, the geometric topology data of the 3D lightweight BIM model is called to draw construction drawings: the gradient dimension chain from the upper width of the trapezoidal stiffener to the lower width of the trapezoidal stiffener, and the leader line of the flange thickness value are marked; at the same time, the maximum stress value data, safety margin data and displacement deformation data in the mechanical property dataset are read, and the mechanical verification conclusions are written in the annotation column of the drawing: the maximum stress value data and its reduction percentage, the safety margin data and its specification compliance judgment, and the displacement deformation data and the ratio of allowable deformation, and finally integrated into the engineering modeling scheme document.

[0106] S5.3: Map the structural stress extrema to the vertex shader of the 3D lightweight BIM model, configure interactive stress visualization rules, build component decomposition function, and generate a 3D verification program;

[0107] Specifically, the structural stress extreme value data is mapped to the vertex shader of the 3D lightweight BIM model, and interactive stress visualization rules are configured: for example, the range of the first-level stress threshold is set to [0.8, 1.0], the range of the second-level stress threshold is [0.6, 0.8), and the range of the third-level stress threshold is [0, 0.6]. When the stress value is within the first-level stress threshold range, the material yield strength area is rendered in red; when the stress value is within the second-level stress threshold range, the material yield strength area is rendered in yellow; and when the stress value is within the third-level stress threshold range, the material yield strength area is rendered in green. A component decomposition function is constructed: the separation distance between the trapezoidal stiffener and the beam web is controlled by a slider to expose the internal stress distribution. A real-time verification node is developed: when the user clicks on any vertex, the local stress value is displayed and compared with the maximum stress value data in the feature report. Finally, it is packaged into an executable 3D verification program.

[0108] S5.4: Verify the scene file through VR device operation. When an abnormal stress area is detected, record the abnormal coordinate data to generate a reinforcement plan and generate an optimized engineering modeling plan document.

[0109] Specifically, the operator loads the 3D verification program into the verification scene file through the VR device. When an abnormal stress area is detected, the operator records the abnormal coordinate data by clicking the controller. The parametric template library is called to automatically generate a reinforcement scheme. The engineering modeling scheme document is updated: new stiffening rib node details are inserted into the construction drawings, and the safety calculation report is revised. Finally, the optimized engineering modeling scheme document is output, which includes the updated plan layout, section details, and mechanical verification conclusion page.

[0110] This embodiment also provides an engineering design modeling system based on BIM visualization technology, including:

[0111] The data processing module is used to collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information.

[0112] The stress assessment module is used to dynamically assess the stress state of engineering components based on a dataset of engineering components with location information to drive the loading of the BIM model, construct a visual stress cloud map, and generate a dataset of high-stress area boundaries.

[0113] The topology optimization module is used to optimize the structural topology in the parameter space based on the high-stress region boundary dataset using reinforcement learning methods, and generate parameterized component family files and mechanical verification reports.

[0114] The model processing module is used to assemble parametric component family files into the BIM model, perform collision detection, dynamically generate avoidance schemes, and perform lightweight processing to generate a three-dimensional lightweight BIM model.

[0115] The scheme generation module is used to locate the optimization area and extract geometric parameters. Combined with the mechanical property dataset, it generates an engineering modeling scheme through a 3D lightweight BIM model, performs 3D verification, and dynamically optimizes the engineering modeling scheme based on the verification results.

[0116] This embodiment also provides a computer device applicable to engineering design modeling methods based on BIM visualization technology, 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 design modeling method based on BIM visualization technology proposed in the above embodiment.

[0117] 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.

[0118] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the engineering design modeling method based on BIM visualization technology as 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.

[0119] In summary, this invention achieves precise location and visual early warning of structural anomalies by dynamically determining the stress state of components using real-time location information and generating a high-stress region boundary dataset. This replaces traditional static analysis, shortens the design iteration cycle, and improves structural safety. Furthermore, by optimizing the structural topology through reinforcement learning to generate parameterized component family files, it achieves data-driven global intelligent optimization, replacing empirical formula design, reducing manual costs, and improving the model's mechanical performance and adaptability.

[0120] 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. An engineering design modeling method based on BIM visualization technology, characterized in that: include, Collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information; Based on a dataset of engineering components with location information, the stress state of the components is dynamically determined to drive the loading of the BIM model, and a visual stress cloud map is constructed to generate a dataset of high-stress area boundaries. Based on the high-stress region boundary dataset, the structural topology is optimized in the parameter space using reinforcement learning methods, generating a parameterized component family file and a mechanical verification report. The specific steps are as follows. Based on the coordinates of the polygon vertices of the isosurface geometry in the high-stress region boundary dataset, and combined with the stress extremum attribute, the parameter space range and constraint conditions are defined to generate a parameter space constraint file. Based on the parameter space constraint file, the coordinates of polygon vertex are discretized into a mesh matrix, and structural parameter adjustment actions are defined, a reward function is established, and the optimal parameter combination file is generated by filtering. Input the optimal parameter combination file into the trapezoidal stiffening rib template, perform parameter-driven modeling, and perform assembly verification to generate a parameterized component family file. Apply amplified design loads to the parametric component family file, extract key index data, and generate a mechanical verification report; The parametric component family files are assembled into the BIM model, collision detection is performed, avoidance schemes are dynamically generated, and lightweight processing is applied to generate a lightweight 3D BIM model. The specific steps are as follows. Parse the parametric component family file, match the beam and column components at the corresponding coordinate positions in the BIM model, perform precise alignment, and generate the assembled BIM model. Based on the assembled BIM model, collision rules are set, and a full-discipline collision scan is performed to identify collision types, mark the 3D coordinates of collision locations, and generate a BIM model with collision markers. Based on the BIM model with collision markers, the optimal avoidance path is obtained through gradient descent, and dynamic avoidance is performed to generate the optimized BIM model. Based on the optimized and avoided BIM model, a model simplification operation is performed: the number of geometric patches is reduced by a lightweight tool while retaining the accuracy of the outer contour of key features, and the size of the compressed texture map is compressed using a standard compression format. Remove hidden components and non-load-bearing structural patches; finally generate a lightweight 3D BIM model, including compressed geometric data, texture maps, and lightweight parameter logs; The optimization area is located and geometric parameters are extracted. Combined with the mechanical property dataset, an engineering modeling scheme is generated through a 3D lightweight BIM model. At the same time, 3D verification is performed, and the engineering modeling scheme is dynamically optimized based on the verification results.

2. The engineering design modeling method based on BIM visualization technology as described in claim 1, characterized in that: The dataset of engineering components with location information is generated by extracting the spatial correlation coefficient of the engineering data components.

3. The engineering design modeling method based on BIM visualization technology as described in claim 2, characterized in that: The BIM model loading is driven by dynamically determining the stress state of the components based on the engineering component dataset with location information. The specific steps are as follows. The dataset of engineering components with location information is matched with the BIM component mechanical property library to dynamically establish the association and generate a dataset that integrates location and mechanical properties. Based on the fusion of location and mechanical property datasets, stress state labels are dynamically marked by the ratio of stress values ​​to generate a stress state label dataset; The accuracy level of the BIM model is dynamically selected based on the stress value state labels in the stress state label dataset, and a hierarchical BIM model set is generated.

4. The engineering design modeling method based on BIM visualization technology as described in claim 3, characterized in that: The specific steps for constructing a visualized stress cloud map and generating a high-stress region boundary dataset are as follows. Based on a hierarchical BIM model set, the real-time coordinates of high-stress risk components are mapped onto the surface of the BIM model, the stress value is dynamically converted to a color value, and a high-stress area pulsation warning effect is added to generate a visual stress cloud map. Extract the coordinates of the polygon vertices of the isosurface geometry that equals the boundary stress from the visualized stress cloud map, and attach the stress extremum attribute of the corresponding region to generate a high-stress region boundary dataset.

5. The engineering design modeling method based on BIM visualization technology as described in claim 1, characterized in that: The location optimization area is determined, geometric parameters are extracted, and combined with the mechanical property dataset, an engineering modeling scheme is generated using a 3D lightweight BIM model. The specific steps are as follows: Locate the optimization region and extract its geometric parameters, read the mechanical property dataset, and generate an optimization region feature report; Based on the geometric parameters in the optimized regional feature report, construction drawings are drawn using a 3D lightweight BIM model, and an engineering modeling scheme is generated by combining the mechanical property dataset.

6. The engineering design modeling method based on BIM visualization technology as described in claim 5, characterized in that: The process of performing 3D verification and dynamically optimizing the engineering modeling scheme based on the verification results involves the following specific steps. The structural stress extrema are mapped to the vertex shader of the 3D lightweight BIM model, interactive stress visualization rules are configured, and component decomposition function is built to generate a 3D verification program. By operating and verifying the scene file through VR devices, when an abnormal stress area is detected, the abnormal coordinate data is recorded to generate a reinforcement plan and an optimized engineering modeling plan document is generated.

7. An engineering design modeling system based on BIM visualization technology, based on the engineering design modeling method based on BIM visualization technology according to any one of claims 1 to 6, characterized in that: include, The data processing module is used to collect engineering data and preprocess it, extract the features of engineering data components, and generate an engineering component dataset with location information. The stress assessment module is used to dynamically assess the stress state of engineering components based on a dataset of engineering components with location information to drive the loading of the BIM model, construct a visual stress cloud map, and generate a dataset of high-stress area boundaries. The topology optimization module is used to optimize the structural topology in the parameter space based on the high-stress region boundary dataset using reinforcement learning methods, and generate parameterized component family files and mechanical verification reports. The model processing module is used to assemble parametric component family files into the BIM model, perform collision detection, dynamically generate avoidance schemes, and perform lightweight processing to generate a three-dimensional lightweight BIM model. The scheme generation module is used to locate the optimization area and extract geometric parameters. Combined with the mechanical property dataset, it generates an engineering modeling scheme through a 3D lightweight BIM model, performs 3D verification, and dynamically optimizes the engineering modeling scheme based on the verification results.

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