A 3D modeling method and system for construction projects based on BIM

Abstract

The present invention provides a 3D modeling method and system for construction projects based on BIM, which relates to the technical field of building information modeling. The method includes: generating a point cloud model through multi-source data acquisition and preprocessing; performing semantic annotation on the model and introducing a structural complexity coefficient and a component association degree parameter; constructing a three-dimensional topological relationship and optimizing the mesh model; embedding construction management information into the model to achieve dynamic update and multi-dimensional data integration; and finally performing verification and output through the matching of the point cloud and the geometric model. The present invention improves the modeling efficiency and accuracy, enhances the data integration and dynamic adaptability of the model, and significantly optimizes the storage performance and computational burden in complex scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of building information modeling, and particularly to a three-dimensional modeling method and system for building engineering 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. Through the method of three-dimensional digital models, BIM technology integrates the geometric information, physical properties, and construction management information of buildings onto a unified platform, 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:

[0003] Low model generation efficiency: Traditional three-dimensional modeling relies on manual operations or single data collection methods, resulting in low model generation efficiency and difficulty in meeting the real-time requirements of large-scale building projects.

[0004] Insufficient data integration: Existing methods have limitations in the integration of construction management information and usually cannot efficiently embed dynamic information such as construction progress, cost estimation, and resource allocation into the model, affecting the optimization and adjustment of construction plans.

[0005] Insufficient model accuracy and applicability: Existing modeling technologies have limited capabilities in processing geometric details and topological relationships of complex building scenes and cannot meet the requirements of high-precision modeling. At the same time, the generated models lack a targeted verification mechanism, which may lead to inapplicability of the models in subsequent construction and management.

[0006] To solve the above problems, the present invention proposes a three-dimensional modeling method and system for building engineering based on BIM. Through multi-source data fusion, semantic annotation and topology construction, construction management information integration, and model optimization and verification, this method improves the modeling efficiency and accuracy, enhances the practicability and dynamic update ability of the model. At the same time, this system optimizes the model construction process under complex scenarios, providing reliable guarantees for design, construction, and later maintenance in building engineering. Summary of the Invention

[0007] 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 building engineering based on BIM.

[0008] The technical solutions provided by the present invention are as follows:

[0009] First aspect:

[0010] A 3D modeling method for construction projects based on BIM provided by the present invention includes:

[0011] S1. Data collection and preprocessing: Collect geometric and environmental data of the target construction site through laser scanning equipment, UAV photogrammetry, or 3D laser mapping system, and perform noise filtering and coordinate system conversion on the data.

[0012] S2. Preliminary model generation: Generate a point cloud model based on the collected data, and perform sparse processing of the point cloud using a spatial indexing method to generate a preliminary 3D geometric model.

[0013] S3. Semantic annotation: Classify the components in the preliminary 3D model, and label each component using the structural complexity coefficient SCC and the component correlation degree (CID). The structural complexity coefficient SCC is used to quantify the geometric complexity of building components, and the component correlation degree CID is used to reflect the spatial and functional relevance of building components to other components.

[0014] S4. Topological relationship construction: Based on the semantic annotation results, construct a 3D topological model using the geometric and spatial relationships between components.

[0015] S5. Model optimization: Optimize the 3D model using a multi-scale mesh simplification algorithm to reduce the storage and computational burden of the model while retaining details.

[0016] S6. Attribute integration: Embed construction management information into the 3D model, where the construction management information includes construction progress, cost estimation, and resource allocation.

[0017] S7. Verification and output: Perform geometric consistency checking and data integrity verification on the generated BIM model, and output the model after ensuring compliance with the design requirements and specifications of the construction project.

[0018] Second aspect:

[0019] A 3D modeling system for construction projects based on BIM provided by the present invention includes:

[0020] A processor;

[0021] A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the 3D modeling method for construction projects based on BIM described in the first aspect is implemented.

[0022] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0023] (1) In the present invention, through the multi-source data fusion and automated semantic annotation method, the efficiency of 3D modeling in construction engineering is significantly improved. Especially in large and complex scenarios, the dependence on manual operations is reduced. 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 enhanced, ensuring that the accuracy of the model meets the requirements of high-demand engineering scenarios.

[0024] (2) In the present invention, construction management information is embedded into the 3D model, and real-time update of construction information and dynamic binding of component attributes are achieved through the dynamic attribute mapping algorithm. This data integration method enables the model to not only have geometric and semantic information but also have the full life cycle management function, meeting the dynamic adjustment and efficient collaboration requirements in complex engineering environments.

[0025] (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 amount, while retaining key details, the storage requirements and computational burden of the model are reduced. The optimized model is more lightweight in rendering and transmission, and still meets the accuracy and function requirements, providing a guarantee for efficient data processing in complex building scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic flow chart of a 3D modeling method for construction engineering based on BIM provided by an embodiment of the present invention;

[0028] Figure 2 It is a schematic flow chart of the specific process of step S6 of a 3D modeling method for construction engineering based on BIM provided by an embodiment of the present invention;

[0029] Figure 3 It is a schematic flow chart of the specific process of step S7 of a 3D modeling method for construction engineering based on BIM provided by an embodiment of the present invention;

[0030] Figure 4 It is a schematic structural diagram of a 3D modeling system for construction engineering based on BIM provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will describe the technical solutions in the present invention with reference to the drawings.

[0032] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0033] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0034] Refer to the attached Figure 1 The figure shows a schematic flow chart of a three-dimensional modeling method for building engineering based on BIM provided by an embodiment of the present invention.

[0035] The embodiments of the present invention provide a three-dimensional modeling method for building engineering based on BIM (Building Information Modeling). This method can be implemented by a three-dimensional modeling device for building engineering based on BIM. This three-dimensional modeling device for building engineering based on BIM can be a terminal or a server. The processing flow of a three-dimensional modeling method for building engineering based on BIM can include the following steps:

[0036] S1. Data collection and preprocessing: Collect the geometric and environmental data of the target building site through a laser scanning device, unmanned aerial vehicle photogrammetry or a three-dimensional laser mapping system, and perform noise filtering and coordinate system conversion on the data.

[0037] It can be understood that the geometric and environmental data of the building site is the basis for three-dimensional 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 basis is provided for subsequent model generation.

[0038] For example, in a high-rise building project, a laser scanner is carried by an unmanned aerial vehicle to perform a full-range scan of the building site, and the geometric data of the terrain, landform and surrounding environment of the target building is collected. Then noise filtering is performed. Specifically, a point cloud denoising algorithm based on statistical analysis is used to remove abnormal points and isolated points; then coordinate system conversion is performed. Specifically, by referring to the global positioning system (GPS) coordinates of the measurement points, the point cloud data is converted from the camera coordinate system to the global coordinate system. The resolution of the finally generated point cloud data reaches 500 points per square centimeter, providing an accurate geometric basis for subsequent modeling.

[0039] S2. Preliminary model generation: Based on the collected data, a point cloud model is generated, and a spatial indexing method is used for sparse processing of the point cloud to generate a preliminary three-dimensional geometric model.

[0040] It can be understood that point cloud data is the core manifestation form of 3D modeling. 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 utilization of the original data.

[0041] In a possible implementation, the sparse processing of the point cloud in S2 adopts an adaptive grid division method, dynamically adjusts the grid size according to the geometric characteristics of the components, and optimizes the uniformity of the point cloud distribution.

[0042] In the example mentioned above, the preprocessed point cloud data is imported into 3D modeling software (such as Revit or Rhino), and a preliminary three-dimensional geometric model is generated using a point cloud-based surface reconstruction algorithm. Then, the above-mentioned point cloud sparsification is performed. Specifically, for redundant data, an octree division method is adopted to compress the point cloud to 40% of the original quantity, reducing the computational overhead while ensuring no loss of details. The generated preliminary model retains the outer facade shape and main geometric features of the building, meeting the subsequent semantic annotation requirements.

[0043] S3. Semantic annotation: Classify the components in the preliminary three-dimensional model, and use the Structure Complexity Coefficient (SCC) and Component Interrelation Degree (CID) to annotate each component. 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 relevance of building components to other components.

[0044] It can be understood that by classifying and annotating the components, semantic information can be introduced into the model, giving it clear engineering attributes. This semantic processing provides important support for construction planning, design optimization, and post-maintenance, making the model more than just a carrier for geometric expression.

[0045] In a possible implementation, the calculation of the Structure Complexity Coefficient (SCC) in S3 is based on the geometric features of the components, including volume, surface area, and the degree of concavity and convexity of the boundary; the evaluation of the Component Interrelation Degree (CID) is achieved by analyzing the geometric adjacency relationship and functional connection relationship of the components in the site.

[0046] In a possible implementation, the calculation formula for the Structure Complexity Coefficient (SCC) is:

[0047] ;

[0048] Among them, represents the surface area of the component, represents the volume of the component, represents the concavity and convexity rate of the component boundary, defined as the ratio of the lengths of the protruding and sunken parts of the boundary, is the adjustment coefficient of the concavity and convexity rate, depending on the geometric shape classification of the component.

[0049] In a possible implementation, the calculation formula for the component association degree CID is:

[0050] ;

[0051] Among them, is the geometric contact quantity of the component, is the potential association quantity of the component, is the functional connection strength of the component, is the weight of the functional connection strength, determined by the number of functional relationships in the actual scenario.

[0052] Taking an office building project as an example, semantic annotation is carried out on the components in its preliminary three-dimensional geometric model:

[0053] Structural complexity SCC: By calculating the surface area (300 ) and volume (30 ) of the building facade curtain wall, ;

[0054] Component association degree CID: For the geometric contact and functional association between the curtain wall and the surrounding window frames, calculate to obtain ;

[0055] The component information generated through semantic annotation provides basic data for subsequent topological relationship components.

[0056] S4. Topological relationship construction: Based on the semantic annotation results, use the geometric and spatial relationships between components to construct a three-dimensional topological model.

[0057] It can be understood 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 requirements in complex scenarios.

[0058] In a possible implementation, the construction of the three-dimensional topological relationship in S4 uses the following formula to calculate the adjacency matrix A:

[0059] ;

[0060] Among them, represents the componenti and components j The relevance, and the geometric contact relationship is calculated through the point cloud overlap ratio; the functional connection relationship is determined according to the explicit dependency relationship in the construction drawings.

[0061] The geometric contact relationship refers to the direct or indirect association between two components in terms of geometric shape or position, including but not limited to the following situations:

[0062] Vertex contact: The geometric vertices of two components coincide, for example, the end point of a beam is connected to the vertex of a column.

[0063] Edge contact: The boundary lines of two components coincide with each other, for example, the side of a wall is connected to the boundary line of the floor.

[0064] Surface contact: The surfaces of two components are in direct contact, for example, the contact surface between a floor slab and a beam.

[0065] Point cloud overlap: Through the calculation of point cloud data, the point cloud data of two components partially or completely overlap, indicating the existence of geometric contact.

[0066] The functional connection relationship refers to the direct or indirect dependency between two components in terms of function, including but not limited to the following situations:

[0067] Load-bearing relationship: One component provides load-bearing support for another component, for example, a column supports a floor slab.

[0068] 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.

[0069] Force transmission relationship: The mechanical action relationship between components, for example, a beam transmits the load to a column.

[0070] Functional cooperation: Components work together in a certain function, for example, a door frame and a door leaf jointly complete the opening and closing functions.

[0071] Functional chain relationship: Multiple components are connected in sequence to achieve a certain function, for example, the connection relationship between valves, connectors and pipes in a pipeline system.

[0072] The determination of the geometric contact relationship is completed through the calculation of the point cloud overlap ratio, and the specific process is as follows:

[0073] Point cloud acquisition: Using laser scanning or other 3D scanning technologies, generate a high-precision point cloud model of the target component. The point cloud sets corresponding to each component are represented by and where each point cloud set contains a large number of points with three-dimensional coordinates ;

[0074] Point cloud registration: For And perform registration to ensure that the point cloud data is aligned in a unified coordinate system. This step usually adopts the Iterative Closest Point (ICP) algorithm;

[0075] Overlap region recognition: After completing the registration, calculate the Euclidean distance between the point clouds , and set the threshold to represent the contact determination distance between two points. When , it is considered that the two points overlap;

[0076] Overlap ratio calculation: Count the number of overlapping points , and calculate the point cloud overlap ratio :

[0077] ;

[0078] Among them, and are the total number of points in the point cloud sets respectively;

[0079] Geometric contact determination: When exceeds the preset threshold (such as 0.1 or 10%), it can be considered that there is a geometric contact relationship between the components i and j .

[0080] The determination of the functional connection relationship is based on the clear dependency information in the construction drawings and models, and the specific description is as follows:

[0081] Extract construction drawing information: Extract the connection or dependency relationships between components from the construction drawings. For example: Mechanical transmission relationship: Clearly define the force and load-bearing relationships between components, such as the mechanical action between beams and columns. Functional relevance: Extract the functional descriptions between door frames and doors, and between pipes and valves.

[0082] Component association annotation: According to the node information in the construction drawings, label the dependency relationships between components as association attributes. The dependency relationships can be divided into:

[0083] Fixed connection: Such as welding, bolt connection.

[0084] Movable connection: Such as door hinges, hinges.

[0085] Functional chain: Such as the sequential connection in a water pipe system.

[0086] Dependency relationship matching: In the 3D model, through the matching of construction drawing information and component parameters, clarify which components have functional connections. For example:

[0087] The load-bearing relationship marked between columns and beams.

[0088] The pipeline and the interface are aligned by number or annotation information.

[0089] Relationship visualization: Visualize the functional connection relationships in a 3D model. For example, use different colors or lines to represent different types of functional dependencies (such as mechanical dependencies, functional collaborations).

[0090] Functional connection determination: The final determination of the functional connection relationships is based on the clear descriptions 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.

[0091] The adjacency matrix A is a matrix, where is the total number of components. represents that there is a relationship (geometric contact or functional connection) between component i and component j , represents that there is no relationship between component i and component j . If the relationship is directed, a directed graph form can be used. If the relationship is undirected, the adjacency matrix is symmetric, i.e., .

[0092] For example, assume there are four components: component 1 is a column, component 2 is a beam, component 3 is a floor slab, and component 4 is a wall. The relationships are as follows: the column is connected to the beam, the beam is connected to the floor slab, the floor slab is in contact with the wall, and there is no direct relationship between the wall and the column. Based on the above relationships, the adjacency matrix A is as follows:

[0093] ;

[0094] The rows and columns of the matrix correspond to the component numbers. For example, the 1st row and the 1st column correspond to component 1, the column. represents the connection between the column and the beam, represents the contact between the floor slab and the wall, represents that there is no direct relationship between the wall and the column. Since the relationship is undirected, the matrix is symmetric. For example, .

[0095] If it is necessary to represent the differences between geometric contact relationships and functional connection relationships, a weighted adjacency matrix or a multi-layer adjacency matrix can be used. For example:

[0096] Weighted adjacency matrix: Use weights (such as contact area, connection strength) as the matrix element values.

[0097] Multi-layer adjacency matrix: Use two groups of matrices and to represent geometric contact relationships and functional connection relationships respectively. For example, for the weighted adjacency matrix of geometric contact area:

[0098] ;

[0099] Among them indicates that the contact area between the beam and the floor slab is 15.0 square meters. As an intuitive representation tool for component relationships, the adjacency matrix is very useful in the component analysis of 3D modeling and can clearly describe and handle complex topological relationships.

[0100] S5, Model Optimization: The 3D model is optimized using a multi-scale grid simplification algorithm to reduce the storage and computational burden of the model while retaining details.

[0101] It can be understood that optimizing the 3D model helps to improve storage and computational efficiency. Especially in multi-scale scenarios, the simplification process can balance the accuracy and performance of the model. The optimized model is more lightweight and convenient for subsequent rendering, transmission, and complex computational analysis.

[0102] In one possible implementation, the multi-scale grid simplification algorithm in S5 is optimized using the following energy function:

[0103] ;

[0104] Among them, is the geometric error term, indicating the deviation between the simplified grid points and the original grid points. , where is the number of simplified grid points. represents the index of the simplified grid points, and all simplified points need to be traversed (from 1 to ). is the topological error term, indicating the degree of change in boundary connections during the simplification process. , where is the number of boundary connections before simplification. represents the index of the boundary connections, and all original boundary connections need to be traversed (from 1 to ); and are both weight factors, which are set by the user or automatically adjusted according to the scenario. and represent the coordinates of the original grid points and the simplified grid points respectively. represents the quantitative value of topological changes.

[0105] Specifically, for example, in a certain construction project, a complex pipeline network model is simplified. The initial grid contains vertices, and the goal is to reduce the number of vertices to while maintaining geometric features and topological relationships.

[0106] The grid connection relationship is represented by an adjacency matrix. For a specific example of the adjacency matrix, refer to the component geometry and function relationship matrix defined in S4 above. The geometric error term represents the deviation between the simplified points and the original points:

[0107] ;

[0108] Initial point set New vertices projected onto the simplified network , and the least squares method is used to ensure the minimum error between points. For example, for the grid points of a short section of pipeline:

[0109] , ;

[0110] The corresponding geometric error is:

[0111] ;

[0112] The topological error term represents the change in the connection of the grid boundary:

[0113] ;

[0114] Boundary connection change amount is defined as the change in the connection relationship of the boundary vertex pairs before and after simplification. If the edge is deleted after simplification, then ; otherwise .

[0115] In a specific embodiment, the initial grid edge set , , , the simplified edge set , , the deleted edge corresponds to , being 0 indicates no change, being 1 indicates that the edge is deleted after simplification. Here, the deleted edge is .

[0116] The total topological error is: .

[0117] Next, the weight factors and can also be adjusted according to the user scenario:

[0118] For scenarios with high requirements for geometric fidelity (such as maintaining the shape of the pipeline): , .

[0119] Scenarios with high connectivity requirements (such as the simplification of load-bearing structures): , .

[0120] In this embodiment, set , to ensure that the main geometric characteristics of the pipeline are maintained after simplification.

[0121] Assume the initial . Total energy:

[0122] ;

[0123] Calculation of the total energy after simplification:

[0124] After simplification . Total energy:

[0125] ;

[0126] The optimization results are compared as follows:

[0127] The number of vertices before simplification is 50,000, and the total energy .

[0128] The number of vertices after simplification is 10,000, and the total energy .

[0129] The deviation of the geometric characteristics of the mesh after simplification is controlled within 0.01 m, and the topological structure is not significantly lost, meeting the engineering requirements.

[0130] The calculation method of the geometric characteristic deviation is as follows:

[0131] Assume the original coordinates of a certain sampling point are:

[0132] ;

[0133] And its corresponding coordinates in the simplified model are:

[0134] ;

[0135] Then the coordinate differences are:

[0136] ;

[0137] Calculate the local deviation of this point:

[0138] ;

[0139] After calculating for all sampling points, take the maximum value among them as the geometric characteristic deviation. In this embodiment, if all points meet , it proves that in the entire simplified model, the geometric property deviation is strictly controlled within 0.01 m within.

[0140] It should be noted that the specific implementation steps of the multi-scale mesh simplification algorithm are as follows:

[0141] First, perform noise filtering, coordinate normalization, etc. on the original triangular mesh or point cloud data to ensure the quality of the data. Then, decompose the data into multiple levels, where the low-resolution levels reflect the overall shape and the high-resolution levels retain local details. According to the mesh density and local features, the data is divided into several levels so that each level can process the rough shape and detail information separately. The coarse levels are mainly used for large-scale data compression, and the fine levels are used for local adjustment and accuracy correction.

[0142] For each mesh vertex, determine its neighboring vertices to form a local neighborhood. Using this neighborhood data, extract local geometric features, including local plane or surface trends. Use the information of the local neighborhood to evaluate the geometric properties of the original vertex in its region. Estimate the candidate simplification operations and judge whether the newly generated vertices after simplification can locally restore the original shape well. If the gap between the new vertex and the original data in this region is small, then this operation is considered geometrically acceptable.

[0143] Select a series of candidate edges in the mesh. These edges 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, through local data analysis, calculate the best position of the new vertex in maintaining local geometric features. This process is similar to finding a "middle position" in the local area so that the merged vertex has a good matching effect with all points in the original area. In actual operation, the idea of least squares optimization can be used to determine this position without specifically giving the formula. Before performing edge collapse, the error changes brought by the simplification operation will be evaluated, including geometric error and topological error. In particular, it will be verified whether the distance between each new vertex and its original corresponding point after simplification still remains within 0.01 meters. If a candidate operation causes the local geometric deviation to exceed this limit, then this edge collapse operation will not be performed.

[0144] At the coarse scale level, first perform a large number of edge collapse operations to achieve significant data compression. At this time, the main focus is on maintaining the overall shape, and the requirements for local details are relatively low. During the entire iteration process, use a mechanism similar to a priority queue to sort the candidate operations and preferentially execute those edge collapse operations that introduce less error and reduce the energy the most. After each operation, the local error information of the affected vertices will be updated to ensure that the global error continues to decrease.

[0145] It should be noted that model simplification is used to reduce data storage and computational burdens and improve processing speed. By adopting multi-scale grid simplification and energy function optimization, not only the number of vertices is reduced, but also the local geometric deviation is ensured not to exceed 0.01 meters, maintaining accuracy and topological consistency, thereby accelerating rendering, matching, and dynamic updates. The overall solution is more efficient and reliable.

[0146] S6. Attribute integration: Embed construction management information into the 3D model. The construction management information includes construction progress, cost estimation, and resource allocation.

[0147] It can be understood that the integration of construction management information endows the 3D model with the ability of dynamic management, such as progress tracking and cost control. By embedding this information, the model can support multi-dimensional decision-making during the construction phase, further enhancing its practicality.

[0148] Refer to the appended Figure 2 illustrates the specific process schematic diagram of step S6 of a BIM-based 3D building modeling method provided by an embodiment of the present invention.

[0149] In a possible implementation manner, S6 further includes:

[0150] The attribute integration adopts a dynamic attribute mapping algorithm, which specifically includes the following steps:

[0151] S601. Generate a construction management information index according to the classification result of the construction management information;

[0152] S602. Adopt a graph algorithm based on the shortest path to map the construction progress and cost estimation attributes in the construction management information to the relevant building components;

[0153] S603. Generate a dynamic BIM model containing the construction management information.

[0154] For example, in a certain building 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.

[0155] Generate a construction management information index: Classify the construction information into three categories: foundation construction, main body construction, and decoration construction according to the construction stage. Each category of construction information contains progress attributes (such as planned start time, actual completion time) and cost attributes (such as budget cost, actual cost).

[0156] Generate an index for the construction management information according to the classification:

[0157] Index structure = {component ID: {progress information, cost information}}

[0158] For example:

[0159] Component A: {Planned start: 2024-01-01, Budget cost: 100,000 yuan}

[0160] Graph algorithm based on the shortest path: Regarding building components and construction management information as nodes in a graph. The edge weights between nodes are calculated according to the degree of association between construction management information and components. Using Dijkstra's algorithm to find the optimal mapping path and calculate the shortest path between a certain construction information and the target component. Edge weight calculation formula:

[0161] ;

[0162] Wherein, represents the edge weight between nodes i and j , represents the combined distance (such as the physical distance between components), is the relevance of the construction stage (such as assigning a lower weight for the same construction stage), and are adjustment factors, satisfying .

[0163] Suppose the construction management information node (main construction progress) needs to be mapped to the component , :

[0164] Initial path weight: , ;

[0165] After algorithm optimization, select the path with the minimum weight .

[0166] Generate a dynamic BIM model: With the change of construction management information (such as schedule delay, cost adjustment), recalculate the path in real time and update the associated component attributes. For example: The actual construction is delayed by 5 days, and the adjusted schedule attribute associated with the component is:

[0167] Actual start time: 2024-01-06

[0168] Each component contains real-time updated construction information:

[0169] Component B: {Actual start time: 2024-01-06, Actual cost 120,000 yuan}

[0170] Through the dynamic attribute mapping algorithm, construction management information is efficiently embedded into the three-dimensional BIM model, ensuring real-time synchronization of model information and providing strong data support for construction management and decision-making.

[0171] S7. Verification and Output: Conduct geometric consistency checks and data integrity verification on the generated BIM model, and output the model after ensuring it meets the design requirements and specifications of the construction project.

[0172] It can be understood that conducting geometric consistency checks and data integrity verification on the model can ensure that it meets engineering specifications and actual 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.

[0173] Refer to the attached instruction manual Figure 3 , which shows the specific process schematic diagram of step S7 of a 3D building modeling method based on BIM provided by an embodiment of the present invention.

[0174] In a possible implementation, the verification in S7 adopts a method based on the matching of point cloud and geometric model, specifically including:

[0175] S701. Extract key points of the model;

[0176] S702. Use the Iterative Closest Point (ICP) algorithm to register the point cloud and the key points of the model;

[0177] S703. Judge whether the model meets the design specifications according to the matching error value.

[0178] For example, after the construction of a high-rise building is completed, it is necessary to verify the consistency between the construction components and 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.

[0179] Definition of key points of the geometric model: The key points are selected from the significant geometric features of the model, such as corner points, edges, and the mutation points of surface normal vectors. For example, the key points of a certain component include the coordinates of its four top corners:

[0180] ;

[0181] Extraction of point cloud key points: Adopt point cloud sparsification and feature extraction algorithms (such as curvature analysis) to obtain the point cloud feature points corresponding to the key points of the geometric model.

[0182] Using the ICP algorithm for point cloud registration: Based on global positioning (such as GPS coordinates or known reference planes), roughly align the point cloud data to the coordinate system of the geometric model. The ICP iteration process is as follows:

[0183] Input: Set of point cloud feature points and set of key points of the model .

[0184] Iterative step: For each point , in PFind the nearest point ;

[0185] Minimize the following objective function to calculate the rigid body transformation matrix (rotation matrix R and translation vector T ):

[0186] ;

[0187] Update the position of the point cloud set :

[0188] ;

[0189] Iterate until the error converges or reaches the set threshold.

[0190] Matching error judgment: The error value is specifically calculated for all point pairs , and the root mean square error (RMSE) is calculated:

[0191] ;

[0192] After that, model verification is performed: Set the error tolerance threshold (such as 5 mm). If RMSE ≤ , the model meets the design specifications; otherwise, it is determined that there is a large deviation and further inspection or rework is required.

[0193] Refer to the attached Figure 4 description, which shows the structural schematic diagram of a 3D building engineering modeling system provided by an embodiment of the present invention.

[0194] The present invention also provides a 3D building engineering modeling system 20 based on BIM, which is applied to the above-mentioned 3D building engineering modeling method based on BIM and includes:

[0195] A processor 201.

[0196] A memory 202, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor 201, the 3D building engineering modeling method based on BIM as in the method embodiment is implemented.

[0197] The 3D building engineering modeling system provided by the present invention can execute the above-mentioned 3D building engineering modeling method based on BIM and achieve the same or similar technical effects. To avoid repetition, the present invention will not be elaborated herein.

[0198] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0199] (1) In the present invention, through the multi-source data fusion and automated semantic annotation method, the efficiency of 3D modeling in construction engineering has been significantly improved. Especially in large and complex scenarios, the dependence on manual operations has been reduced. 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 has been enhanced, ensuring that the model's accuracy meets the requirements of high-demand engineering scenarios.

[0200] (2) In the present invention, construction management information is embedded in the 3D model, and through the dynamic attribute mapping algorithm, the real-time update of construction information and the dynamic binding of component attributes are realized. This data integration method enables the model to not only have geometric and semantic information but also have the full life cycle management function, capable of meeting the dynamic adjustment and efficient collaboration requirements in complex engineering environments.

[0201] (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 amount, while retaining the key details, the storage requirement and computational burden of the model are reduced. The optimized model is more lightweight in rendering and transmission, and still can meet the accuracy and function requirements, providing guarantee for efficient data processing in complex building scenarios.

[0202] The above content is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0203] The following points need to be explained:

[0204] (1) The attached drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the usual designs.

[0205] (2) For clarity, in the attached drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is enlarged or reduced, that is, these drawings are not drawn according to the actual scale. It can be 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 can be "directly" on or under the other element or there can be intermediate elements.

[0206] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0207] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A three-dimensional modeling method for construction projects based on BIM, characterized in that, Including: S1. Data acquisition and preprocessing: Collect the geometric and environmental data of the target construction site through a laser scanning device, UAV 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, and use a spatial indexing method to sparsify the point cloud to generate a preliminary 3D geometric model; S3. Semantic annotation: Classify the components in the preliminary 3D model, and label each component using the structural complexity coefficient SCC and the component correlation degree CID. The structural complexity coefficient SCC is used to quantify the geometric complexity of building components, and the component correlation degree CID is used to reflect the spatial and functional relevance of building components to other components; S4. Topological relationship construction: Based on the semantic annotation results, construct a 3D topological model using the geometric and spatial relationships between components; S5. Model optimization: Optimize the 3D model using a multi-scale mesh simplification algorithm to reduce the storage and computational burden of the model while retaining details; S6. Attribute integration: Embed construction management information into the 3D model, where the construction management information includes construction progress, cost estimation and resource allocation; S7. Verification and output: Perform geometric consistency checks and data integrity verification on the generated BIM model, and output the model after ensuring compliance with the design requirements and specifications of the construction project; The calculation formula of the structural complexity coefficient SCC is: ； Among them, represents the surface area of the component, represents the volume of the component, represents the concavity and convexity rate of the component boundary, defined as the ratio of the lengths of the boundary convex and concave parts, is the adjustment coefficient of the concavity and convexity rate, depending on the geometric shape classification of the component; The calculation formula of the component correlation degree CID is: ； Among them, is the geometric contact number of components, is the potential association number of components, is the functional connection strength of components, is the weight of the functional connection strength, which is determined by the number of functional relationships in the actual scenario.

2. The three-dimensional modeling method of a building project based on BIM according to claim 1, characterized in that, The S3 further includes: The calculation of the structural complexity coefficient SCC is based on the geometric features of the component, including volume, surface area and the concavity and convexity of the boundary; the evaluation of the component correlation degree CID is achieved by analyzing the geometric adjacency relationship and functional connection relationship of the component in the site.

3. A three-dimensional modeling method for building engineering based on BIM according to claim 2, characterized in that, The S4 further includes: The 3D topological model calculates the adjacency matrix A using the following formula: ； Among them, represents the relevance between components i and component j The geometric contact relationship is calculated by the point cloud overlap ratio; the functional connection relationship is determined according to the explicit dependency relationship in the construction drawings.

4. A three-dimensional modeling method for construction projects based on BIM according to claim 2, characterized in that, The S5 further includes: The multi-scale mesh simplification algorithm is optimized using the following energy function: ； Among them, is the geometric error term, representing the deviation between the simplified grid points and the original grid points, , where is the number of simplified grid points, is the topological error term, representing the degree of change in boundary connection during the simplification process, , where is the number of boundary connections before simplification, and are both weight factors, set by the user or automatically adjusted according to the scenario, and represent the coordinates of the original grid points and the simplified grid points respectively, represents the quantitative value of topological change.

5. A 3D modeling method for construction projects based on BIM according to claim 2, characterized in that, The S6 further includes: The attribute integration adopts a dynamic attribute mapping algorithm, which specifically includes the following steps: S601. Generate a construction management information index according to the classification result of the construction management information; S602. Use a graph algorithm based on the shortest path to map the construction progress and cost estimation attributes in the construction management information to the relevant building components; S603. Generate a dynamic BIM model containing construction management information.

6. The three-dimensional modeling method of a building project based on BIM according to claim 1, wherein, The S7 further includes: The verification adopts a method based on the matching of point cloud and geometric model, which specifically includes: S701. Extract the key points of the model; S702. Use the iterative closest point ICP algorithm to register the point cloud and the model key points; S703. Judge whether the model meets the design specifications according to the matching error value.

7. A three-dimensional modeling method for building engineering based on BIM according to claim 1, characterized in that, The S2 further includes: The sparsification of the point cloud adopts an adaptive grid division method, and dynamically adjusts the grid size according to the geometric characteristics of the component to optimize the uniformity of the point cloud distribution.

8. A three-dimensional modeling system for construction projects based on BIM, characterized in that, Including: Processor; A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the three-dimensional modeling method for building engineering based on BIM as described in any one of claims 1 to 7 is implemented.

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