Building engineering three-dimensional modeling method and system based on BIM

Abstract

The invention provides a BIM-based building engineering three-dimensional modeling method and system, and relates to the technical field of building information modeling. The method comprises the following steps: generating a point cloud model through multi-source data acquisition and preprocessing; semantic annotation is carried out on the model, and a structure complexity coefficient and a component association degree parameter are introduced; constructing a three-dimensional topological relation and optimizing the grid model; construction management information is embedded into the model, and dynamic updating and multi-dimensional data integration are achieved; and finally, verification and output are carried out through point cloud and geometric model matching. According to the method, the modeling efficiency and precision are improved, the data integration and dynamic adaptability of the model are enhanced, and the storage performance and the calculation burden in a complex scene are remarkably optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and in particular to a method and system for three-dimensional modeling of construction projects based on BIM. Background Art

[0002] With the development of informatization and digitalization in the construction industry, Building Information Modeling (BIM) technology has gradually become an important tool in the field of engineering construction. BIM technology integrates the geometric information, physical properties and construction management information of buildings into a unified platform through three-dimensional digital models, providing important support for engineering design, construction management and operation and maintenance. However, the existing three-dimensional modeling methods and systems still face the following problems in practical applications: Low model generation efficiency: Traditional 3D modeling relies on manual operation or a single data collection method, resulting in low model generation efficiency and difficulty in meeting the real-time requirements of large-scale construction projects.

[0003] Insufficient data integration: Existing methods have limitations in the integration of construction management information. They are usually unable to efficiently embed dynamic information such as construction progress, cost estimation, and resource allocation into the model, which affects the optimization and adjustment of construction plans.

[0004] Insufficient model accuracy and applicability: Existing modeling technologies have limited ability to process geometric details and topological relationships of complex building scenes, and cannot meet the needs of high-precision modeling. At the same time, the generated models lack a targeted verification mechanism, which may lead to the models not being applicable in subsequent construction and management.

[0005] In order to solve the above problems, the present invention proposes a BIM-based three-dimensional modeling method and system for construction projects. The method improves modeling efficiency and accuracy, and enhances the practicality and dynamic update capability of the model through multi-source data fusion, semantic annotation and topology construction, construction management information integration, and model optimization and verification. At the same time, the system optimizes the model building process in complex scenarios, providing reliable guarantees for the design, construction, and later maintenance of construction projects. Summary of the invention

[0006] In order to solve the technical problems of low model generation efficiency, insufficient data integration, and low model accuracy and applicability in the prior art, the present invention provides a three-dimensional modeling method and system for construction projects based on BIM.

[0007] The technical solution provided by the present invention is as follows: First aspect: The present invention provides a BIM-based three-dimensional modeling method for building engineering, comprising: S1. Data collection and preprocessing: Collect the geometric and environmental data of the target building site through laser scanning equipment, drone photogrammetry or 3D laser mapping system, and perform noise filtering and coordinate system conversion on the data; S2. Preliminary model generation: Generate a point cloud model based on the collected data, use the spatial index method to perform sparse processing on the point cloud, and generate a preliminary three-dimensional geometric model; S3, semantic annotation: classify the components in the preliminary 3D model and annotate each component using the structural complexity coefficient SCC and component association degree (CID). The structural complexity coefficient SCC is used to quantify the geometric complexity of the building components, and the component association degree CID is used to reflect the spatial and functional association between the building components and other components. S4, topological relationship construction: Based on the semantic annotation results, the three-dimensional topological model is constructed using the geometric and spatial relationships between components; S5. Model optimization: Use a multi-scale mesh simplification algorithm to optimize the 3D model, retaining details while reducing the model's storage and computational burden; S6. Attribute integration: embedding construction management information into the 3D model, wherein the construction management information includes construction progress, cost estimation, and resource allocation; S7. Verification and output: Perform geometric consistency check and data integrity verification on the generated BIM model to ensure that it meets the design requirements and specifications of the construction project before outputting the model.

[0008] Second aspect: The present invention provides a BIM-based three-dimensional modeling system for building engineering, comprising: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for three-dimensional modeling of building projects based on BIM as described in the first aspect is implemented.

[0009] The beneficial effects brought about by the technical solution provided by the present invention include at least: (1) In the present invention, the efficiency of 3D modeling of construction projects is significantly improved through multi-source data fusion and automatic semantic annotation methods, especially in large and complex scenes, reducing the dependence on manual operations. At the same time, by introducing the structural complexity coefficient (SCC) and component association degree (CID) parameters, the model's ability to express geometric details and spatial relationships is improved, ensuring that the accuracy of the model meets the needs of high-demand engineering scenes; (2) In the present invention, construction management information is embedded in the three-dimensional model, and the real-time update of construction information and the dynamic binding of component attributes are realized through the dynamic attribute mapping algorithm. This data integration method enables the model to have not only geometric and semantic information, but also full life cycle management functions, which can meet the dynamic adjustment and efficient collaboration requirements in complex engineering environments; (3) In the present invention, a multi-scale mesh simplification algorithm is used to optimize the model. By controlling the geometric error and topological change, the storage requirement and computational burden of the model are reduced while retaining key details. The optimized model is lighter in rendering and transmission while still meeting the accuracy and functional requirements, providing a guarantee for efficient data processing in complex architectural scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a method for three-dimensional modeling of a construction project based on BIM provided by an embodiment of the present invention; Figure 2 A schematic diagram of a specific flow chart of step S6 of a method for three-dimensional modeling of a construction project based on BIM provided in an embodiment of the present invention; Figure 3 A schematic diagram of a specific flow chart of step S7 of a method for three-dimensional modeling of a construction project based on BIM provided in an embodiment of the present invention; Figure 4 A schematic structural diagram of a BIM-based building engineering 3D modeling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0013] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0014] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0015] Reference Manual Attached Figure 1 , showing a flow chart of a method for three-dimensional modeling of construction projects based on BIM provided in an embodiment of the present invention.

[0016] The embodiment of the present invention provides a method for 3D modeling of a construction project based on BIM (Building Information Modeling). The method can be implemented by a 3D modeling device for a construction project based on BIM. The 3D modeling device for a construction project based on BIM can be a terminal or a server. The processing flow of the method for 3D modeling of a construction project based on BIM can include the following steps: S1. Data acquisition and preprocessing: Collect the geometric and environmental data of the target building site through laser scanning equipment, drone photogrammetry or 3D laser mapping system, and perform noise filtering and coordinate system conversion on the data.

[0017] It is understandable that the geometry and environmental data of the building site are the basis of 3D modeling, and the quality of the data directly affects the accuracy and reliability of the model. By preprocessing the original data, removing noise and redundant information, and unifying the coordinate system, an accurate and standardized data foundation is provided for subsequent model generation.

[0018] For example, in a high-rise building project, a drone equipped with a laser scanner was used to perform a full-scale scan of the construction site to collect the topography, landforms and geometric data of the target building's surrounding environment. Noise filtering was then performed, specifically using a point cloud denoising algorithm based on statistical analysis to remove abnormal points and isolated points; the coordinate system was then converted, specifically by converting the point cloud data from the camera coordinate system to the global coordinate system through the global positioning system (GPS) coordinates of the reference measurement points. The resulting point cloud data had a resolution of 500 points per square centimeter, providing an accurate geometric basis for subsequent modeling.

[0019] S2. Preliminary model generation: Generate a point cloud model based on the collected data, use the spatial index method to perform sparse processing on the point cloud, and generate a preliminary three-dimensional geometric model.

[0020] It is understandable that point cloud data is the core form of expression for 3D modeling, and its sparse processing can significantly improve data processing efficiency and reduce computational complexity. The generated preliminary model provides a visualization basis for subsequent steps while ensuring efficient use of the original data.

[0021] In a possible implementation, the point cloud in S2 is sparsely distributed using an adaptive meshing method, which dynamically adjusts the mesh size according to the geometric characteristics of the component to optimize the uniformity of the point cloud distribution.

[0022] In the example mentioned above, the pre-processed point cloud data is imported into 3D modeling software (such as Revit or Rhino), and a preliminary 3D geometric model is generated using a point cloud-based surface reconstruction algorithm. Then the point cloud sparseness mentioned above is performed. Specifically, the octree partitioning method is used for redundant data to compress the point cloud to 40% of the original number, ensuring that details are not lost while reducing computational overhead. The generated preliminary model retains the facade shape and main geometric features of the building to meet the subsequent semantic annotation requirements.

[0023] S3. Semantic annotation: Classify the components in the preliminary 3D model and annotate each component using the structure complexity coefficient (SCC) and component interrelation degree (CID). The structure complexity coefficient (SCC) is used to quantify the geometric complexity of building components, and the component interrelation degree (CID) is used to reflect the spatial and functional associations between building components and other components.

[0024] It is understandable that by classifying and labeling components, semantic information can be introduced into the model, giving it clear engineering properties. This semantic processing provides important support for construction planning, design optimization and later maintenance, making the model more than just a carrier of geometric expression.

[0025] In a possible implementation, the calculation of the structural complexity coefficient SCC in S3 is based on the geometric characteristics of the components, including the volume, surface area and the degree of concavity and convexity of the boundary; the evaluation of the component association degree CID is achieved by analyzing the geometric adjacency relationship and functional connection relationship of the components in the site.

[0026] In a possible implementation, the calculation formula of the structural complexity coefficient SCC is: ; in, represents the surface area of ​​the component, represents the volume of the component, It represents the convexity of the component boundary, which is defined as the ratio of the length of the convex and concave parts of the boundary. is the adjustment coefficient of the concave-convex ratio, which depends on the geometric shape classification of the component.

[0027] In a possible implementation manner, the calculation formula of the component association degree CID is: ; in, is the number of geometric contacts of the components, is the number of potential associations of the component, is the functional connection strength of the component, is the weight of the functional connection strength, which is determined by the number of functional relationships in the actual scenario.

[0028] Taking an office building project as an example, the components in its preliminary 3D geometric model are semantically annotated: Structural complexity SCC: The surface area of ​​the building facade curtain wall (300 ) and volume (30 ) calculation, we get ; Component association degree CID: Calculate the geometric contact and functional association between the curtain wall and the surrounding window frame to obtain ; The component information generated through semantic annotation provides basic data for subsequent topological relationship components.

[0029] S4. Topological relationship construction: Based on the semantic annotation results, the three-dimensional topological model is constructed using the geometric and spatial relationships between components.

[0030] It is understandable that the spatial and logical relationships between building components are the core part of the model data. The construction of topological relationships can effectively organize the linkage information between components, make the expression of the model more intuitive, and support the analysis and interaction needs in complex scenarios.

[0031] In a possible implementation, the construction of the three-dimensional topological relationship in S4 uses the following formula to calculate the adjacency matrix A: ; in, Representation component i and components j The geometric contact relationship is calculated by the point cloud overlap ratio; the functional connection relationship is determined according to the dependency relationship clearly defined in the construction drawings.

[0032] Geometric contact refers to the direct or indirect relationship between two components in terms of geometric shape or position, including but not limited to the following: Vertex contact: The geometric vertices of two components coincide, such as the end point of a beam connecting to the vertex of a column.

[0033] Edge contact: The boundary lines of two components coincide with each other, such as the side of a wall connecting to the boundary line of a floor.

[0034] Surface contact: The surfaces of two components are in direct contact, such as the contact surface between a floor slab and a beam.

[0035] Point cloud overlap: Through point cloud data calculation, the point cloud data of two components partially or completely overlap, indicating that geometric contact exists.

[0036] Functional connection refers to the direct or indirect functional dependence between two components, including but not limited to the following: Load-bearing relationship: One component provides load-bearing support for another component, such as a column supporting a floor slab.

[0037] Connector relationship: Two components are connected together through certain connectors (such as bolts, welds, etc.), for example, a steel beam and a steel column are connected by welding.

[0038] Force transfer relationship: the mechanical action relationship between components, such as a beam transferring load to a column.

[0039] Functional collaboration: Components work together on a certain function, such as a door frame and door leaf working together to complete the opening and closing functions.

[0040] Functional chain relationship: Multiple components are connected in sequence to achieve a certain function, such as the connection relationship between valves, connectors and pipes in a pipeline system.

[0041] The determination of geometric contact relationship is completed by calculating the overlap ratio of point clouds. The specific process is as follows: Point cloud acquisition: Use laser scanning or other 3D scanning technology to generate a high-precision point cloud model of the target component. The point cloud set corresponding to each component is and Represents that each point cloud contains a large number of points with three-dimensional coordinates ; Point cloud registration: and Perform registration to ensure that the point cloud data is aligned in a unified coordinate system. This step usually uses the Iterative Closest Point (ICP) algorithm. Overlapping region identification: After completing the registration, calculate the Euclidean distance between the point cloud sets , set the threshold Represents the contact determination distance between two points. When , the two points are considered to overlap; Overlap ratio calculation: count the number of overlapping points , and calculate the point cloud overlap ratio : ; in, and are the total number of points in the point cloud set; Geometric contact determination: When If the threshold value (such as 0.1 or 10%) is exceeded, the component can be considered i and j There is a geometric contact relationship.

[0042] The functional connection relationship is determined based on the dependency information clearly defined in the construction drawings and models, as follows: Extract construction drawing information: Extract the connection or dependency between components from the construction drawings. For example: Mechanical transfer relationship: clarify the force and load relationship between components, such as the mechanical effect between beams and columns. Functional relevance: Extract the functional description between door frames and door leaves, pipes and valves.

[0043] Component association annotation: According to the node information in the construction drawings, the dependency between components is annotated as association attributes. The dependency can be divided into: Fixed connection: such as welding and bolt connection.

[0044] Active connections: such as door hinges and hinges.

[0045] Functional chain: such as the sequential connections in a water pipe system.

[0046] Dependency matching: In the 3D model, by matching the construction drawing information with the component parameters, it is clear which components have functional connections. For example: The load-bearing relationship between columns and beams is marked.

[0047] Pipes and joints are aligned by numbering or labeling information.

[0048] Relationship visualization: Visualize functional connection relationships in a 3D model. For example, use different colors or lines to represent different types of functional dependencies (such as mechanical dependence and functional synergy).

[0049] Functional connection determination: The final determination of the functional connection relationship is based on the clear description in the drawings and the verification in the model. For example, check whether the annotations in the construction drawings are consistent with the relationships in the model.

[0050] The adjacency matrix A is a The matrix of The total number of components. Representation component i and components j There is a relationship (geometric contact or functional connection), Representation component i and components j There is no relationship between them. If the relationship is directed, a directed graph can be used. If the relationship is undirected, the adjacency matrix is ​​symmetric, that is, .

[0051] For example, suppose there are four components, component 1 is a column, component 2 is a beam, component 3 is a floor, and component 4 is a wall. The relationship is as follows: columns and beams are connected, beams and floor are connected, floor and wall are in contact, and there is no direct relationship between walls and columns. Based on the above relationship, the adjacency matrix A is as follows: ; The rows and columns of the matrix correspond to the component numbers, for example, row 1 and column 1 correspond to component 1 column. Indicates the connection between columns and beams. Indicates the contact between floor and wall. indicates that there is no direct relationship between the wall and the column. Since the relationship is undirected, the matrix is ​​symmetric, e.g. .

[0052] If you need to express the difference between geometric contact relationship and functional connection relationship, you can use weighted adjacency matrix or multi-layer adjacency matrix. For example: Weighted adjacency matrix: weights (e.g., contact area, connection strength) are used as matrix element values.

[0053] Multi-layer adjacency matrix: use two sets of matrices respectively and Represents geometric contact relationships and functional connectivity relationships. For example, for the geometric contact area weighted adjacency matrix: ; in It indicates that the contact area between the beam and the floor is 15.0 square meters. As an intuitive representation tool for component relationships, the adjacency matrix is ​​very useful in component analysis of three-dimensional modeling, and can clearly describe and process complex topological relationships.

[0054] S5. Model optimization: A multi-scale mesh simplification algorithm is used to optimize the three-dimensional model, retaining the details while reducing the storage and computational burden of the model.

[0055] It is understandable that optimizing 3D models helps improve storage and computing efficiency, especially in multi-scale scenarios, where simplified processing can balance the accuracy and performance of the model. The optimized model is lighter and easier to render, transmit, and perform complex computations and analysis.

[0056] In a possible implementation, the multi-scale grid simplification algorithm in S5 is optimized using the following energy function: ; in, is the geometric error term, which represents the deviation between the simplified grid points and the original grid points. ,in is the number of simplified grid points, Represents the index of the simplified grid point. It is necessary to traverse all simplified points (from 1 to ), is the topological error term, which indicates the degree of change in boundary connections during the simplification process. ,in is the number of boundary connections before simplification, Represents the index of the boundary connection, and needs to traverse all original boundary connections (from 1 to ); and They are all weight factors, which are set by the user or automatically adjusted according to the scene. and denote the coordinates of the original grid points and the simplified grid points, respectively. A quantitative value indicating topological change.

[0057] Specifically, for example, in a construction project, a complex pipeline network model is meshed and simplified. The initial mesh contains The goal is to reduce the number of vertices to .

[0058] The mesh connection relationship is represented by the adjacency matrix. For a specific adjacency matrix example, refer to the component geometry and function relationship matrix defined in S4 above. The geometric error term represents the deviation between the simplified point and the original point: ; Initial point set Projecting to new vertices of the simplified network , using the least squares method to ensure that the error between points is minimal. For example, for a small section of the pipeline grid points: , ; The corresponding geometric error is: ; The topological error term represents the change in the connectivity of the mesh boundaries: ; Boundary connection change It is defined as whether there is a change in the connection relationship between the boundary vertex pairs before and after simplification. is deleted after simplification, then ;otherwise .

[0059] In a specific embodiment, the initial mesh edge set , , , simplify the following set , , delete the edge correspond , A value of 0 indicates no change. 1 indicates an edge After simplification, it is deleted. The edge deleted here is .

[0060] The sum of the topological errors is: .

[0061] Next, you can also adjust the weight factor by user scenario and : Scenarios that require high geometric fidelity (such as pipeline shape preservation): , .

[0062] Scenarios with high connectivity requirements (such as simplified load-bearing structures): , .

[0063] In this embodiment, setting , , ensuring that the main geometric characteristics of the pipeline are maintained after simplification.

[0064] Assume initial Total Energy: ; Simplified total energy calculation: After simplification Total Energy: ; The optimization results are compared as follows: Number of vertices before simplification: 50,000, total energy .

[0065] After simplification, the number of vertices is 10,000 and the total energy is .

[0066] The geometric characteristic deviation of the simplified grid is controlled within 0.01 meters, and there is no obvious loss of topological structure, which meets the engineering requirements.

[0067] The geometrical property deviation is calculated as follows: Assume that the original coordinates of a sampling point are: ; And its corresponding coordinates in the simplified model are: ; The coordinate differences are: ; Compute the local deviation at this point: ; Calculate for all sampling points Then, the maximum value is taken as the geometric characteristic deviation. In this embodiment, if all points satisfy , it proves that in the entire simplified model, the geometric characteristic deviation is strictly controlled within 0.01 m Within.

[0068] It should be noted that the specific implementation steps of the multi-scale grid simplification algorithm are as follows: First, the original triangular mesh or point cloud data is processed by noise filtering and coordinate normalization to ensure the quality of the data. Then, the data is decomposed into multiple levels, where the low-resolution level reflects the overall shape and the high-resolution level retains the local details. According to the mesh density and local characteristics, the data is divided into several levels so that each level can process the rough shape and detail information respectively. The coarse level is mainly used for large-scale data compression, and the fine level is used for local adjustment and precision correction.

[0069] For each mesh vertex, determine the neighboring vertices around it to form a local neighborhood. Use this neighborhood data to extract local geometric features, including local plane or surface trends. Use the information of the local neighborhood to evaluate the geometric characteristics of the original vertex in its area. Estimate the candidate simplification operations to determine whether the newly generated vertices after simplification can better restore the original shape locally. If the difference between the new vertex and the original data in this area is small, the operation is considered geometrically acceptable.

[0070] A series of candidate edges are selected in the mesh, which are potential simplification objects. For each edge, consider merging the two vertices of the edge into a new vertex to reduce the complexity of the mesh. When considering simplification, the best position of the new vertex in terms of maintaining local geometric features is calculated through local data analysis. This process is similar to finding a "middle position" in the local area so that the merged vertex has a good match with all points in the original area. In practice, the idea of ​​least squares optimization can be used to determine this position without giving a specific formula. Before performing edge collapse, the error changes caused by the simplification operation are evaluated, including geometric error and topological error. In particular, it is verified that the distance between each new vertex and its original corresponding point after simplification remains within 0.01 meters. If a candidate operation causes the local geometric deviation to exceed this limit, the edge collapse operation is not performed.

[0071] At the coarse-scale level, a large number of edge collapse operations are first performed to achieve a significant compression of the data. At this time, the main focus is on maintaining the overall shape, and the requirements for local details are relatively low. Throughout the iteration process, a mechanism similar to a priority queue is used to sort the candidate operations, and those edge collapse operations that introduce less error and reduce the most energy are performed first. After each operation, the local error information of the affected vertices is updated to ensure that the global error continues to decrease.

[0072] It should be noted that model simplification is used to reduce data storage and computing burdens and improve processing speed. Multi-scale mesh simplification and energy function optimization are used to reduce the number of vertices while ensuring that the local geometric deviation does not exceed 0.01 meters, maintaining accuracy and topology consistency, thereby speeding up rendering, matching and dynamic updates, and making the overall solution more efficient and reliable.

[0073] S6. Attribute integration: embed construction management information into the 3D model. Construction management information includes construction progress, cost estimation, and resource allocation.

[0074] It is understandable that the integration of construction management information enables the 3D model to have dynamic management capabilities, such as progress tracking and cost control. By embedding this information, the model can support multi-dimensional decision-making in the construction stage, further enhancing its practicality.

[0075] Reference Manual Attached Figure 2 , shows a specific flow chart of step S6 of a BIM-based construction engineering three-dimensional modeling method provided in an embodiment of the present invention.

[0076] In a possible implementation, S6 further includes: Attribute integration uses a dynamic attribute mapping algorithm, which includes the following steps: S601, generating a construction management information index according to the classification result of the construction management information; S602, using a graph algorithm based on the shortest path to map the construction progress and cost estimation attributes in the construction management information to relevant building components; S603: Generate a dynamic BIM model including construction management information.

[0077] For example, in a construction project, it is necessary to dynamically associate the construction progress information and cost estimation information with the corresponding building components. The project contains 200 components, each with a unique identifier, and the construction management information is stored in the database.

[0078] Generate construction management information index: According to the construction stage, the construction information is divided into three categories: basic construction, main construction and decoration construction. Each type of construction information contains progress attributes (such as planned start time, actual completion time) and cost attributes (such as budgeted cost, actual cost).

[0079] Generate indexes for construction management information by category: Index structure = {Component ID: {Progress information, cost information}} For example: {Component A: {Planned start: 2024-01-01, budget cost: 100,000 yuan}} Graph algorithm based on the shortest path: Building components and construction management information are regarded as nodes in the graph. The edge weights between nodes are calculated based on the degree of association between construction management information and components. The Dijkstra algorithm is used to find the optimal mapping path and calculate the shortest path between a certain construction information and the target component. Edge weight calculation formula: ; in, Representation Node i and j The edge weights between represents the combined distance (such as the physical distance between components), Relevance of construction stages (e.g. assigning lower weight to the same construction stage), and is the adjustment factor, satisfying .

[0080] Assume that the construction management information node (Main construction progress) needs to be mapped to components , : Initial path weight: , ; After algorithm optimization, the path with the minimum weight is selected .

[0081] Generate dynamic BIM model: As construction management information changes (such as progress delays, cost adjustments), the path is recalculated in real time and the associated component attributes are updated. For example: if the actual construction is delayed by 5 days, the progress attribute associated with the component is adjusted to: Actual start time: 2024-01-06 Each component contains construction information that is updated in real time: Component B: {Actual start time: 2024-01-06, actual cost: RMB 120,000} Through the dynamic attribute mapping algorithm, construction management information is efficiently embedded in the three-dimensional BIM model, ensuring real-time synchronization of model information and providing strong data support for construction management and decision-making.

[0082] S7. Verification and output: Perform geometric consistency check and data integrity verification on the generated BIM model to ensure that it meets the design requirements and specifications of the construction project before outputting the model.

[0083] It is understandable that geometric consistency checking and data integrity verification of the model can ensure that it meets engineering specifications and practical application requirements. The verified model has high reliability and can be directly applied to all aspects of the entire life cycle of building design, construction and operation and maintenance.

[0084] Reference Manual Attached Figure 3 , shows a specific flow chart of step S7 of a BIM-based construction engineering three-dimensional modeling method provided in an embodiment of the present invention.

[0085] In a possible implementation, the verification in S7 is based on matching of the point cloud with the geometric model, specifically including: S701, extracting key points of the model; S702, using an iterative closest point ICP algorithm to align the point cloud and the model key points; S703: Determine whether the model meets the design specification according to the matching error value.

[0086] For example, after the construction of a high-rise building is completed, it is necessary to verify the consistency of the construction components with the design model. The point cloud data of the components is obtained through laser scanning and matched with the 3D geometric model in the design stage.

[0087] Definition of key points of geometric models: Key points are selected from significant geometric features of the model, such as corners, edges, and places where surface normal vectors change suddenly. For example, the key points of a component include the coordinates of its four corners: ; Point cloud key point extraction: Point cloud sparsification and feature extraction algorithms (such as curvature analysis) are used to obtain point cloud feature points corresponding to the key points of the geometric model.

[0088] Use the ICP algorithm for point cloud registration: Based on global positioning (such as GPS coordinates or known reference surfaces), the point cloud data is roughly aligned to the geometric model coordinate system. The ICP iterative process is as follows: Input: point cloud feature point set And the model key point set .

[0089] Iteration step: for each point ,exist P Find the closest point ; Minimize the following objective function to calculate the rigid body transformation matrix (rotation matrix R and translation vectorsT ): ; Update Point Cloud Collection Location: ; Iterate until error Converge or reach the set threshold.

[0090] Matching error judgment: The error value is calculated for all point pairs. , calculate the root mean square error (RMSE): ; Then perform model validation: set the error tolerance threshold (e.g. 5 mm). If RMSE≤ , the model meets the design specifications; otherwise, it is judged that the deviation is large and further inspection or rework is required.

[0091] Reference Manual Attached Figure 4 , showing a structural schematic diagram of a BIM-based construction engineering three-dimensional modeling system provided in an embodiment of the present invention.

[0092] The present invention further provides a BIM-based construction engineering 3D modeling system 20, which is applied to the BIM-based construction engineering 3D modeling method, comprising: Processor 201.

[0093] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the BIM-based three-dimensional modeling method for building engineering in the method embodiment is implemented.

[0094] The BIM-based three-dimensional modeling system for construction projects provided by the present invention can execute the BIM-based three-dimensional modeling method for construction projects and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0095] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: (1) In the present invention, the efficiency of 3D modeling of construction projects is significantly improved through multi-source data fusion and automatic semantic annotation methods, especially in large and complex scenes, reducing the dependence on manual operations. At the same time, by introducing the structural complexity coefficient (SCC) and component association degree (CID) parameters, the model's ability to express geometric details and spatial relationships is improved, ensuring that the accuracy of the model meets the needs of high-demand engineering scenes; (2) In the present invention, construction management information is embedded in the three-dimensional model, and the real-time update of construction information and the dynamic binding of component attributes are realized through the dynamic attribute mapping algorithm. This data integration method enables the model to have not only geometric and semantic information, but also full life cycle management functions, which can meet the dynamic adjustment and efficient collaboration requirements in complex engineering environments; (3) In the present invention, a multi-scale mesh simplification algorithm is used to optimize the model. By controlling the geometric error and topological change, the storage requirement and computational burden of the model are reduced while retaining key details. The optimized model is lighter in rendering and transmission while still meeting the accuracy and functional requirements, providing a guarantee for efficient data processing in complex architectural scenes.

[0096] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0097] There are a few points to note: (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0098] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0099] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0100] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A three-dimensional modeling method for building engineering based on BIM, characterized in that: include: S1. Data collection and preprocessing: Collect the geometric and environmental data of the target building site through laser scanning equipment, drone photogrammetry or 3D laser mapping system, and perform noise filtering and coordinate system conversion on the data; S2. Preliminary model generation: Generate a point cloud model based on the collected data, use the spatial index method to perform sparse processing on the point cloud, and generate a preliminary three-dimensional geometric model; S3, semantic annotation: classify the components in the preliminary 3D model, and annotate each component using the structural complexity coefficient SCC and the component association degree CID. The structural complexity coefficient SCC is used to quantify the geometric complexity of the building components, and the component association degree CID is used to reflect the spatial and functional association between the building components and other components; S4, topological relationship construction: Based on the semantic annotation results, the three-dimensional topological model is constructed using the geometric and spatial relationships between components; S5. Model optimization: Use a multi-scale mesh simplification algorithm to optimize the 3D model, retaining details while reducing the model's storage and computational burden; S6. Attribute integration: embedding construction management information into the 3D model, wherein the construction management information includes construction progress, cost estimation, and resource allocation; S7. Verification and output: Perform geometric consistency check and data integrity verification on the generated BIM model to ensure that it meets the design requirements and specifications of the construction project before outputting the model.

2. A method for three-dimensional modeling of construction engineering based on BIM according to claim 1, characterized in that: The S3 further comprises: The calculation of the structural complexity coefficient SCC is based on the geometric characteristics of the components, including the volume, surface area and the degree of concavity and convexity of the boundary; the evaluation of the component association degree CID is achieved by analyzing the geometric adjacency relationship and functional connection relationship of the components in the site.

3. A method for three-dimensional modeling of construction engineering based on BIM according to claim 2, characterized in that: The S4 further comprises: The three-dimensional topological model can use the following formula to calculate the adjacency matrix A: ； in, Representation component i and components j The geometric contact relationship is calculated by the point cloud overlap ratio; the functional connection relationship is determined according to the dependency relationship clearly defined in the construction drawings.

4. The method for three-dimensional modeling of a construction project based on BIM according to claim 2, characterized in that: The S5 further comprises: The multi-scale grid simplification algorithm uses the following energy function optimization: ； in, is the geometric error term, which represents the deviation between the simplified grid points and the original grid points. ,in is the number of simplified grid points, is the topological error term, which indicates the degree of change in boundary connections during the simplification process. ,in is the number of boundary connections before simplification, and They are all weight factors, which are set by the user or automatically adjusted according to the scene. and denote the coordinates of the original grid points and the simplified grid points, respectively. A quantitative value indicating topological change.

5. The method for three-dimensional modeling of a construction project based on BIM according to claim 2, characterized in that: The S6 further comprises: The attribute integration adopts a dynamic attribute mapping algorithm, which specifically includes the following steps: S601, generating a construction management information index according to the classification result of the construction management information; S602, using a graph algorithm based on the shortest path to map the construction progress and cost estimation attributes in the construction management information to relevant building components; S603: Generate a dynamic BIM model including construction management information.

6. The method for three-dimensional modeling of a construction project based on BIM according to claim 1, characterized in that: The structural complexity coefficient specifically includes: The calculation formula of the structural complexity coefficient SCC is: ； in, represents the surface area of ​​the component, represents the volume of the component, It represents the convexity of the component boundary, which is defined as the ratio of the length of the convex and concave parts of the boundary. is the adjustment coefficient of the concave-convex ratio, which depends on the geometric shape classification of the component.

7. The method for three-dimensional modeling of a construction project based on BIM according to claim 2, characterized in that: The component association degree specifically includes: The calculation formula of the component association degree CID is: ； in, is the number of geometric contacts of the components, is the number of potential associations of the component, is the functional connection strength of the component, is the weight of the functional connection strength, which is determined by the number of functional relationships in the actual scenario.

8. The method for three-dimensional modeling of a construction project based on BIM according to claim 1, characterized in that: The S7 further comprises: The verification is based on the matching of point cloud and geometric model, which specifically includes: S701, extracting key points of the model; S702, using an iterative closest point ICP algorithm to align the point cloud and the model key points; S703: Determine whether the model meets the design specification according to the matching error value.

9. The method for three-dimensional modeling of a construction project based on BIM according to claim 1, characterized in that: The S2 further comprises: The point cloud is sparsely distributed using an adaptive grid division method, which dynamically adjusts the grid size according to the geometric characteristics of the component and optimizes the uniformity of point cloud distribution.

10. A BIM-based three-dimensional modeling system for building engineering, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for three-dimensional modeling of a construction project based on BIM as described in any one of claims 1 to 9 is implemented.

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