Multi-party cooperative construction management and control method and system based on BIM model

Abstract

The invention relates to the technical field of data processing, in particular to a BIM model-based multi-party cooperative construction management and control method and system, and the method comprises the steps: obtaining the point cloud data of a real-time construction project, and determining the optimal thick side length of a voxel block according to the down-sampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data; voxelization is carried out on the point cloud data based on the optimal coarse side length to obtain a plurality of voxel blocks, and the average density of point cloud distribution of the point cloud data in the voxel blocks is determined; determining the optimal side length of the voxel block according to the average density, the down-sampling rate, the optimal coarse side length and the unit side length, and performing down-sampling on the point cloud data based on the optimal side length to obtain a down-sampling project point cloud set of the real-time construction project; and constructing a BIM model of the real-time construction project by using the down-sampling project point cloud set, and managing and controlling the real-time construction project by using the BIM model. Therefore, the construction accuracy of the BIM three-dimensional model is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a multi-party collaborative construction management method and system based on a BIM model. Background Technology

[0002] In the field of modern architectural engineering, Building Information Modeling (BIM), as the core carrier of a building's digital twin, is a virtual digital model integrating data from multiple stages throughout the building's lifecycle, including design, construction, and operation and maintenance. Based on this model, real-time querying of component attributes and dynamic tracking of construction progress can be achieved. During the construction phase, the BIM model enables real-time monitoring of the progress of each construction stage. A key prerequisite for BIM models to accurately support the construction process is building a real-time, accurate 3D model based on data collected at the construction site, dynamically updating the model to reflect the construction status. In the construction of the BIM 3D model, point cloud data from the construction site is a crucial data source, acquired through technologies such as laser scanning, and contains key information such as the building's spatial location and shape. However, the amount of raw point cloud data collected on-site is usually extremely large, and directly using it for modeling would generate a very high computational load, severely impacting modeling efficiency. Therefore, it is necessary to downsample the raw point cloud data to reduce the data volume, lower computational costs, and ensure the real-time nature of modeling.

[0003] In some scenarios, the voxel downsampling method is often used for point cloud data downsampling in the construction field. This method reduces the data volume by dividing the point cloud space into a voxel grid with fixed side lengths and retaining representative points within each voxel. However, the construction process has significant phased characteristics, and the structural form and component precision requirements of buildings vary considerably at different construction stages, resulting in different distribution characteristics of the point cloud data. Traditional voxel downsampling methods use fixed voxel side lengths, which cannot adapt to the characteristics of point cloud data at different construction stages. This can easily lead to blunting of critical structural edges, destroying the structural details represented by the point cloud data. This distortion of point cloud data due to edge blunting directly causes inaccuracies in the constructed BIM 3D real-time model, leading to deviations in the construction progress assessment based on the model and failing to accurately reflect the on-site construction status. Thus, the accuracy of the BIM 3D model constructed using the above method is low, resulting in low accuracy and reliability of construction progress assessment. Summary of the Invention

[0004] To address the technical problem of low accuracy in BIM 3D model construction, the present invention aims to provide a multi-party collaborative construction management method based on BIM models.

[0005] To solve the above technical problems, the specific technical solution adopted is as follows: This invention provides a multi-party collaborative construction management method based on a BIM model, comprising: acquiring point cloud data of a real-time construction project, and determining the optimal coarse side length of a voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data; voxelizing the point cloud data based on the optimal coarse side length to obtain multiple voxel blocks, and determining the average density of the point cloud data distribution in the voxel blocks based on the first number of point cloud data in the voxel blocks, the point cloud volume parameters of the voxel blocks, the first volume of the voxel blocks, and the second number of each voxel block; determining the optimal side length of the voxel blocks based on the average density, downsampling rate, optimal coarse side length, and unit side length, and downsampling the point cloud data based on the optimal side length to obtain a downsampled project point cloud set of the real-time construction project; constructing a BIM model of the real-time construction project using the downsampled project point cloud set, and using the BIM model to manage the real-time construction project.

[0006] Optionally, determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: determining the unit side length based on the second volume and the third quantity of the point cloud data; and determining the second product between the reciprocal of the downsampling rate and the unit side length as the optimal coarse side length.

[0007] Optionally, the second volume is the first product of the ranges of the three dimensions of the point cloud data.

[0008] Optionally, determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: determining the unit side length based on the proximity distance of each point cloud data; and determining the second product between the reciprocal of the downsampling rate and the unit side length as the optimal coarse side length.

[0009] Optionally, the point cloud volume parameter includes the point cloud volume. Determining the average density of the point cloud data distribution within the voxel block based on the first quantity of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second quantity of each voxel block includes: determining the local aggregation index of the voxel block based on the point cloud volume and the first volume; normalizing the local aggregation index to obtain a normalized aggregation index; calculating the third product between the normalized aggregation index of each voxel block and the first quantity, and superimposing the third products to obtain a superimposed value; and determining the first ratio between the superimposed value and the second quantity as the average density of the point cloud data distribution within the voxel block.

[0010] Optionally, determining the local aggregation index of the voxel block based on the point cloud volume and the first volume includes: calculating a first sum between the point cloud volume and the first volume; and determining a second ratio between the first quantity and the first sum as the local aggregation index.

[0011] Optionally, the point cloud volume parameter includes the convex point cloud volume. The average density of the point cloud data distribution in the voxel block is determined based on the first quantity of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second quantity of each voxel block. This includes: determining the local aggregation index of the voxel block based on the convex point cloud volume and the first volume; normalizing the local aggregation index to obtain the normalized aggregation index; calculating the third product between the normalized aggregation index of each voxel block and the first quantity, and superimposing the third products to obtain the superimposed value; and determining the third ratio between the superimposed value and the second quantity as the average density of the point cloud data distribution in the voxel block.

[0012] Optionally, determining the local aggregation index of the voxel block based on the convex point cloud volume and the first volume includes calculating a second sum between the convex point cloud volume and the first volume; and determining a fourth ratio between the first quantity and the second sum as the local aggregation index.

[0013] Optionally, determining the optimal side length of the voxel block based on the average density, downsampling rate, optimal thick side length, and unit side length includes: when the average density is greater than the reciprocal of the downsampling rate, determining the difference between the optimal thick side length and the unit side length as the optimal side length; when the average density is less than the reciprocal of the downsampling rate, determining the sum between the optimal thick side length and the unit side length as the optimal side length.

[0014] This invention offers the following advantages: By calculating the optimal coarse side length of voxel blocks adapted to the current construction stage using downsampling rate and unit side length of point cloud spatial distribution, and then deriving the average density based on data such as the number of point clouds and volume parameters within the voxel blocks, the optimal voxel side length is finally determined. This achieves dynamic matching between voxel side length and construction stage characteristics. Specifically, through two-level optimization of the optimal coarse side length and the optimal side length, on the one hand, the accurate determination of the optimal coarse side length ensures that the voxel blocks accurately fit the point cloud distribution range of the current construction stage, avoiding the merging or loss of key edge points due to excessively large voxels; on the other hand, the optimal side length optimization based on average density further refines the point cloud filtering logic, simplifying redundant data while prioritizing the retention of point cloud data in key areas such as structural edges and component connection points, ensuring that the point cloud data can realistically and completely represent the structural form and detailed features of the building at the current stage, eliminating distortion problems caused by edge blunting. The accuracy of the BIM model directly depends on the quality of the input point cloud data. This invention significantly improves the construction accuracy of real-time construction project BIM models by optimizing the point cloud processing flow. High-precision BIM models provide a reliable data foundation for construction management and control, enabling the model to accurately reflect core information such as on-site construction progress and component installation quality, thereby improving the accuracy and reliability of construction progress assessment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a multi-party collaborative construction management method based on a BIM model, as provided in one embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-party collaborative construction management and control system based on a BIM model, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a multi-party collaborative construction management method and system based on a BIM model proposed in accordance with the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a multi-party collaborative construction management method based on a BIM model provided by this invention.

[0021] Example 1: Please see Figure 1 The flowchart illustrates a multi-party collaborative construction management method based on a BIM model provided in an embodiment of the present invention, including: Step S101: Obtain point cloud data of the real-time construction project, and determine the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data.

[0022] Specifically, in this embodiment of the invention, before construction begins on a project, architectural design drawings are required. Different building components have different drawings. The drawings of each part of the building are used as input to CAD software, and the output of the CAD software is a three-dimensional model of each part and structure, used to represent the three-dimensional model of the building under standard construction conditions.

[0023] Furthermore, point cloud data of the project building under real-time construction is scanned using 3D laser scanners. Specifically, multiple 3D laser scanners are evenly installed around the project building. During use, the origin and three-axis directions of the multiple 3D laser scanners need to be synchronously calibrated to ensure that the point cloud data measured by the multiple 3D laser scanners at the same point are identical. Alternatively, a drone carrying a 3D laser scanner can be used to fly at a constant speed around and above the project building under real-time construction, scanning the point cloud data of all locations of the project building. Next, the Iterative Closest Point (ICP) algorithm is used to align the real-time scanned point cloud data with the coordinate system of the 3D model of the project building under standard conditions. Since the location of the project building is fixed, the point cloud data of the project building is within a fixed range. Pass-through filtering can effectively eliminate point cloud data outside the fixed range. Therefore, in this embodiment, pass-through filtering is used to filter the collected and aligned point cloud data, and the filtered point cloud data is recorded as the real-time project point cloud set. This is used to characterize the point cloud distribution of the project building during real-time construction. The calculation of pass-through filtering and the ICP algorithm can refer to known techniques, and the specific calculation steps are not described in detail here. It is worth noting that the point cloud data is scanned every hour.

[0024] Furthermore, for voxel downsampling of point cloud data, the mean value of the point cloud data within a voxel block is typically used to replace all points in the voxel block. Therefore, the size of the voxel block determines the amount of point cloud data retained. For different stages of BIM construction, such as the rebar tying stage and the concrete pouring stage, the spatial structure differs. The rebar tying stage has a slender spatial structure, characterized by a longitudinally extending linear network; while after concrete pouring, it transforms into a continuous, dense block structure, with the spatial form becoming a horizontally unfolding plate-like shape. Therefore, using a larger voxel block in the rebar tying stage will cause the mean value of the point cloud data within the voxel block to deviate from the distribution of the rebar structure point cloud data, making it impossible for the mean value of the point cloud data in the voxel block to represent the rebar structure. Thus, the side length of the voxel block determines the effectiveness of the next sampling.

[0025] Furthermore, to determine the optimal voxel block side length, this invention employs a two-stage adaptive method of coarse estimation and fine estimation. The coarse estimation can roughly estimate the optimal voxel block side length, effectively reducing the computation time for calculating this length, thus decreasing the point cloud processing time at different construction stages of BIM architecture and improving the modeling speed for different stages of BIM architecture.

[0026] Furthermore, the unit density of point cloud data can characterize the spatial distribution of point cloud data. Therefore, the side length of the average distribution space of all point cloud data can characterize the overall distribution range of point cloud in three-dimensional space. The ratio between its side length and downsampling rate can roughly estimate the side length of voxel blocks. The larger the ratio, the sparser the distribution of point cloud data, and the corresponding voxel block side length also needs to be increased to ensure that each voxel block can still accommodate enough points for subsequent processing.

[0027] Furthermore, as an optional embodiment of the present invention, determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: determining the unit side length based on the second volume of the point cloud data and the third quantity of the point cloud data; and determining the second product between the reciprocal of the downsampling rate and the unit side length as the optimal coarse side length. Wherein, the second volume is the first product of the range values ​​of the three dimensions of the point cloud data.

[0028] Specifically, the embodiment of the present invention uses the following formula to calculate the optimal thick side length: In the above formula, This represents the optimal thickness of the voxel block. This represents the downsampling rate, with a default value range of (0, 1). In this embodiment of the invention, the value is 0.1, meaning that one point cloud data point is retained for every 10 point cloud data points. The second volume of the point cloud data is calculated as follows: obtain the maximum and minimum values ​​of the point cloud data on the X-axis, Y-axis and Z-axis respectively, subtract the maximum and minimum values ​​of the three dimensions respectively to obtain three range values, and multiply the range values ​​of the three dimensions to obtain the second volume. This indicates the third quantity of point cloud data. This represents the unit side length of the spatial distribution of point cloud data. The downsampling rate is a dimensionless parameter. The ratio of the second volume of all point cloud data to the third quantity of point cloud data characterizes the spatial size of a single point cloud data distribution. Taking the cube root yields the spatial side length of the single point cloud data distribution, which is also a dimensionless parameter. The product of two dimensionless parameters is a dimensionless parameter.

[0029] Furthermore, as an optional embodiment of the present invention, determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: determining the unit side length based on the proximity distance of each point cloud data; and determining the second product between the reciprocal of the downsampling rate and the unit side length as the optimal coarse side length.

[0030] Specifically, embodiments of the present invention also employ the following formula to calculate the optimal thick side length: In the above formula, This represents the optimal thickness of the voxel block. This represents the downsampling rate, with a default value range of (0, 1). In this embodiment of the invention, the value is 0.1, meaning that one point cloud data point is retained for every 10 point cloud data points. The unit side length representing the spatial distribution of point cloud data is calculated as follows: All point cloud data are input into a k-dimensional tree (kd-tree), and the algorithm outputs a kd-tree of the real-time project point cloud set. Next, the nearest neighbor distances of each point cloud data point are obtained through the kd-tree of all point cloud data, and the average of all nearest neighbor distances is calculated as the unit side length of the point cloud data spatial distribution. .

[0031] It is worth noting that while the distribution distance between point cloud data can be analyzed by the proximity distance between them, the building structure is not a continuous entity but rather a structure with some blank space. Therefore, during downsampling, the downsampling rate can characterize the rate at which the real-time project point cloud set shrinks, while the spatial distribution side length of the point cloud data can characterize the length of a point cloud data point in space. Thus, the ratio of the spatial distribution side length of the point cloud data to the downsampling rate can characterize the side length of the voxel distribution at that downsampling rate. Since the building structure varies at different construction stages of BIM construction, resulting in different point cloud distribution densities, this embodiment of the invention calculates the optimal coarse side length using the two methods described above.

[0032] Step S102: Voxelize the point cloud data based on the optimal coarse side length to obtain multiple voxel blocks. Then, determine the average density of the point cloud data distribution in the voxel blocks based on the first number of point cloud data in the voxel blocks, the point cloud volume parameters of the voxel blocks, the first volume of the voxel blocks, and the second number of each voxel block.

[0033] Specifically, in this embodiment of the invention, the real-time project point cloud is voxelized using the optimal thick side length of the voxel block to obtain multiple voxel blocks. For the portion of the point cloud distribution in the real-time project point cloud that is less than the optimal thick side length of the voxel block, the space of the insufficient portion is extended to obtain a voxel block. Furthermore, the number of points in the real-time project point cloud for each voxel block is obtained. Voxel blocks with a point cloud count of 0 are deleted to eliminate the influence of blank areas. For each voxel block, the more point cloud data of the building within the voxel block, the higher the density of the voxel block, indicating that the voxel block has a continuous, dense block structure. High-density areas of BIM buildings not only have slab-like structures but also linear structures, such as the locations of intersections of horizontal and vertical reinforcing bars, which also have high-density point cloud data distribution. If the voxel block is the edge of a reinforcing steel structure or the edge of a concrete structure, it is connected to blank areas. Although the BIM building structure entities all have point cloud distributions, the point cloud distribution in the blank areas is almost non-existent, affecting the size of the voxel block's point cloud count. Therefore, by comparing the number of point clouds within a voxel block with the volume of the actual point cloud data distribution, we can characterize the maximum number of point clouds distributed in a voxel block under that distribution state.

[0034] Furthermore, as an optional embodiment of the present invention, the point cloud volume parameter includes the point cloud volume. Determining the average density of the point cloud data distribution in the voxel block based on the first quantity of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second quantity of each voxel block includes: determining the local aggregation index of the voxel block based on the point cloud volume and the first volume; normalizing the local aggregation index to obtain the normalized aggregation index; calculating the third product between the normalized aggregation index of each voxel block and the first quantity, and superimposing the third products to obtain the superimposed value; determining the first ratio between the superimposed value and the second quantity as the average density of the point cloud data distribution in the voxel block.

[0035] In one optional embodiment of the present invention, determining the local aggregation index of the voxel block based on the point cloud volume and the first volume includes: calculating a first sum between the point cloud volume and the first volume; and determining a second ratio between the first quantity and the first sum as the local aggregation index.

[0036] Specifically, the embodiments of the present invention use the following formula to calculate the local aggregation index: In the above formula, denoted as the local aggregation index of the i-th voxel block. This represents the first number of point cloud data in the i-th voxel block. The point cloud volume of the i-th voxel block is calculated as follows: the product of the ranges of the three dimensions (X, Y, and Z) in the voxel block is used as the point cloud volume of the point cloud data in the voxel block. Specifically, the maximum and minimum values ​​of the point cloud data in the voxel block on the X-axis, Y-axis, and Z-axis are obtained respectively, and the maximum and minimum values ​​of the three dimensions are subtracted to obtain the three range values. The point cloud volume is obtained by multiplying the range values ​​of the three dimensions. The first volume of the voxel block is represented by the first volume. To prevent the voxel block's point cloud volume from being zero due to the point cloud data being distributed on the same plane, resulting in a denominator of 0, the first volume of the voxel block in this embodiment of the invention is calculated as follows: the cube of the optimal thick side length of the voxel block is the first volume.

[0037] In this calculation, the first volume of the voxel block in the denominator and the point cloud volume of the voxel block have the same dimensions, the sum has the dimension of volume, and the numerator, the number of points, is a dimensionless parameter. Therefore, the ratio of the numerator to the denominator is the maximum density of the point cloud under this distribution state in a unit space, and the ratio calculation is a conflict between dimensions.

[0038] Furthermore, as an optional embodiment of the present invention, the point cloud volume parameter includes the convex point cloud volume. Determining the average density of the point cloud data distribution in the voxel block based on the first quantity of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second quantity of each voxel block includes: determining the local aggregation index of the voxel block based on the convex point cloud volume and the first volume; normalizing the local aggregation index to obtain a normalized aggregation index; calculating the third product between the normalized aggregation index of each voxel block and the first quantity, and superimposing the third products to obtain a superimposed value; and determining the third ratio between the superimposed value and the second quantity as the average density of the point cloud data distribution in the voxel block.

[0039] In one optional embodiment of the present invention, determining the local aggregation index of the voxel block based on the convex point cloud volume and the first volume includes: calculating a second sum between the convex point cloud volume and the first volume; and determining a fourth ratio between the first quantity and the second sum as the local aggregation index.

[0040] Specifically, the embodiments of the present invention use the following formula to calculate the local aggregation index: In the above formula, denoted as the local aggregation index of the i-th voxel block. This represents the first number of point clouds in the i-th voxel block. The volume of the convex point cloud of the i-th voxel is represented by the following method: the point cloud data within the voxel is used as... The input to the algorithm is the output of the algorithm, which is used as the input to the Delaunay triangulation algorithm. The sum of the volumes of all tetrahedrons in the algorithm output is used as the volume of the convex point cloud of the voxel block. This represents the first volume of the voxel block, used to prevent the voxel block's point cloud volume from being zero because the point cloud data is distributed on the same plane. The algorithms, Delaunay triangulation algorithm, and tetrahedral volume calculation formula can be referred to in the known technologies. The specific calculation steps of the embodiments of the present invention will not be repeated here.

[0041] It is worth noting that in the two methods of calculating the local aggregation index mentioned above, the average density of the point cloud distribution in a voxel block within a unit space can be obtained by the ratio of the number of point cloud data in the voxel block to the point cloud volume parameter of the voxel block. In this embodiment of the invention, by eliminating the influence of blank areas, the number of point cloud data within the voxel block can characterize the local density within the voxel block, while the volume parameter of the point cloud data distribution within the voxel block can characterize the dispersion of the point cloud data distribution. Therefore, the ratio of the two parameters can characterize the degree of aggregation of point cloud data under the point cloud distribution within the voxel block. The larger the value, the more effective points can be accommodated in the same space, which can reduce the volume of the voxel block and retain more detailed structure. This allows it to cope with changes in the building structure at various construction stages of BIM architecture, thereby improving the accuracy of subsequent 3D reconstruction of the construction building, thus better coordinating the allocation of personnel from various construction parties and reducing the construction period. The method for normalizing the local aggregation index can utilize the maximum-minimum value normalization method.

[0042] Furthermore, for different voxel blocks, a higher local aggregation index indicates a more complex point cloud distribution within the voxel block, suggesting a more complex building structure in BIM architecture, such as a reinforced concrete structure. To increase the bond strength between the steel bars and concrete, transverse, longitudinal, or spiral ribs are typically rolled onto the surface of the steel structure, making the surface more complex and resulting in a greater distribution of point cloud data for the same volume. Therefore, for complex structures, it is necessary to reduce the side length of the voxel block to retain more point cloud data after voxel downsampling, improving the 3D modeling effect of complex BIM building structures and thus better managing multiple personnel.

[0043] The larger the local aggregation index of a voxel block, the more it represents that the internal structure of the BIM building real-time construction includes key structures such as steel ribs, welds, or dense electromechanical nodes. It is given higher weight in the global density estimation of the real-time construction building, so that subsequent downsampling in fine areas can characterize the detailed attributes of fine areas.

[0044] Therefore, this embodiment of the invention calculates the average voxel density of all point cloud data to reflect the average density of point cloud data distribution in voxel blocks in real-time construction projects. Specifically, the calculation is performed using the following formula: In the above formula, This represents the average density of the point cloud data distribution within the voxel block. This represents the normalized aggregation index. This represents the first number of point cloud data in the i-th voxel block. This indicates the second number of voxel blocks. It is worth noting that the voxel blocks in this embodiment do not include voxel blocks with a point cloud count of 0.

[0045] In this calculation, the normalized value of the local aggregation index of the voxel block is a dimensionless parameter, the number of point cloud data in the voxel block is also a dimensionless parameter, and the product of the two parameters is a dimensionless parameter. The denominator, the number of voxel blocks, is a dimensionless parameter. Therefore, the ratio of the numerator to the denominator is a dimensionless parameter. The distribution state of the point cloud in the voxel block is characterized by the product of the number of point cloud distributions in different voxel blocks and the local aggregation index of the spatial sparsity characteristics of the point cloud. Then, the average voxel density of the point cloud data of the real-time construction project is obtained by averaging the products of the number of point cloud distributions in all voxel blocks and the local aggregation index of the spatial sparsity characteristics of the point cloud.

[0046] Step S103: Determine the optimal side length of the voxel block based on the average density, downsampling rate, optimal coarse side length, and unit side length, and downsample the point cloud data based on the optimal side length to obtain the downsampled project point cloud set of the real-time construction project.

[0047] Specifically, embodiments of the present invention determine the optimal side length of a voxel block by the relative magnitude between the average density and the downsampling rate. As an optional embodiment of the present invention, determining the optimal side length of the voxel block based on the average density, downsampling rate, optimal thick side length, and unit side length includes: when the average density is greater than the reciprocal of the downsampling rate, determining the difference between the optimal thick side length and the unit side length as the optimal side length; when the average density is less than the reciprocal of the downsampling rate, determining the sum of the optimal thick side length and the unit side length as the optimal side length.

[0048] Specifically, when the average density of the point cloud data in a real-time construction project is greater than the reciprocal of the downsampling rate, it indicates that the point cloud density in the voxel block is high, and the size of the voxel block needs to be reduced. Therefore, the optimal thick side length of the current voxel block is subtracted from the unit side length of the spatial distribution of the point cloud data. The difference obtained is used as the optimal side length of the voxel block. The step of calculating the average density in the above embodiment is repeated cyclically until the stopping condition is met. When the average density of the point cloud data of the real-time construction project is less than the reciprocal of the downsampling rate, it indicates that the point cloud density in the voxel block is low, and the size of the voxel block needs to be increased. Therefore, the sum of the optimal thick side length of the current voxel block and the unit side length of the spatial distribution of the point cloud data is used as the optimal side length of the voxel block. The step of calculating the average density in the above embodiment is repeated cyclically until the stopping condition is met.

[0049] Furthermore, the stopping conditions specifically include the following four cases: First, when the average density of the point cloud data of the real-time construction project is equal to the reciprocal of the downsampling rate, the side length of the current voxel block is taken as the optimal side length for voxel downsampling. Second, when the average density of the point cloud data of the current cycle of the real-time construction project is greater than the reciprocal of the downsampling rate, while the average density of the point cloud data of the previous cycle is less than the reciprocal of the downsampling rate, the average of the side lengths of the voxel blocks in the current and previous cycles is taken as the optimal side length for voxel downsampling. Third, when the average density of the point cloud data of the current cycle of the real-time construction project is less than the reciprocal of the downsampling rate, while the average density of the point cloud data of the previous cycle is greater than the reciprocal of the downsampling rate, the average of the side lengths of the voxel blocks in the current and previous cycles is taken as the optimal side length for voxel downsampling. Fourth, when the number of cycles exceeds the stopping threshold SUN, the process stops, and the side length of the current voxel block at the time of stopping is taken as the optimal side length for voxel downsampling. In this embodiment, the stopping threshold SUN is set to 10.

[0050] Furthermore, in this embodiment of the invention, the optimal side length of the voxel downsampling calculated above and the point cloud data of the real-time construction project are used as inputs to the voxel downsampling algorithm, and the algorithm output is the downsampled point cloud set of the real-time construction project.

[0051] Step S104: Construct a BIM model of the real-time construction project using the downsampled project point cloud, and use the BIM model to manage and control the real-time construction project.

[0052] Specifically, the downsampled project point cloud of the downsampled real-time construction project is used as the input to the Poisson Reconstruction algorithm, and the algorithm's output is the BIM model of the real-time construction project. Next, the BIM model of the real-time construction project and the 3D model of the project building under the standard state at this stage are voxelized. Then, the intersection-over-union (IoU) ratio of the two models is obtained. When the IoU is greater than 0.95, it indicates that the construction stage has been completed, and personnel from all relevant parties need to be notified to prepare for the next stage, thereby achieving management of multi-party collaborative construction.

[0053] Furthermore, this embodiment of the invention also provides a management and control system based on the above-described management and control method. The BIM model-based multi-party collaborative construction management and control system includes a standard model module, a data acquisition module, a data processing module, a 3D modeling module, a progress judgment module, and a personnel scheduling module. The standard model module obtains a 3D model of the BIM building in its standard state using engineering drawings and CAD software. The data acquisition module acquires point cloud data of the building under construction in real time using a 3D laser scanner. The data processing module preprocesses and downsamples the acquired point cloud data of the building under construction. The 3D modeling module performs 3D modeling using the downsampled point cloud data of the building under construction. The progress judgment module obtains the real-time construction progress by comparing the 3D model of the building under construction in real time with the standard state 3D model. The personnel scheduling module manages the construction work of multiple parties based on the real-time construction progress.

[0054] The technical solution provided by this invention calculates the optimal coarse side length of the voxel block to match the current construction stage by adjusting the downsampling rate and the unit side length of the point cloud spatial distribution. Then, it derives the average density by combining data such as the number of points in the voxel block and volume parameters, ultimately determining the optimal voxel side length. This achieves dynamic matching between the voxel side length and the characteristics of the construction stage. Specifically, through two-level optimization of the optimal coarse side length and the optimal side length, on the one hand, the accurate determination of the optimal coarse side length ensures that the voxel block accurately fits the point cloud distribution range of the current construction stage, avoiding the merging or loss of key edge points due to excessively large voxels; on the other hand, the optimal side length optimization based on average density further refines the point cloud filtering logic. While simplifying redundant data, it prioritizes retaining point cloud data from key areas such as structural edges and component connections, ensuring that the point cloud data can realistically and completely represent the structural form and detailed features of the building at the current stage, eliminating distortion problems caused by edge blunting. The accuracy of the BIM model directly depends on the quality of the input point cloud data. This invention significantly improves the construction accuracy of the BIM model for real-time construction projects by optimizing the point cloud processing flow. High-precision BIM models provide a reliable data foundation for construction management and control, enabling the model to accurately reflect core information such as on-site construction progress and component installation quality, thereby improving the accuracy and reliability of construction progress assessment.

[0055] Example 2: Corresponding to the BIM model-based multi-party collaborative construction management and control method provided in the above embodiments, based on the same technical concept, this invention also provides a BIM model-based multi-party collaborative construction management and control system. This BIM model-based multi-party collaborative construction management and control system is used to execute the above-described BIM model-based multi-party collaborative construction management and control method. Figure 2This is a schematic diagram of another BIM model-based multi-party collaborative construction management system provided in one embodiment of the present invention, as shown below. Figure 2 As shown. A multi-party collaborative construction management system based on a BIM model can vary significantly due to differences in configuration or performance. It may include one or more processors 201 and memory 202. The memory 202 stores computer programs that can run on the processor 201. The processor 201 executes the programs stored in the memory 202 to achieve the above... Figure 1 The various steps in the Chinese method embodiment. The memory 202 can be temporary or persistent storage. The application stored in the memory 202 may include one or more modules (not shown in the figure), each module may include a series of computer-executable instructions for the BIM model-based multi-party collaborative construction management system.

[0056] Furthermore, the processor 201 can be configured to communicate with the memory 202 and execute a series of computer-executable instructions stored in the memory 202 on the BIM model-based multi-party collaborative construction management system. The BIM model-based multi-party collaborative construction management system may also include one or more power supplies 203, one or more wired or wireless network interfaces 204, one or more input / output interfaces 205, and one or more keyboards 206.

[0057] Specifically, in this embodiment, the BIM model-based multi-party collaborative construction management system includes a processor, a communication interface, a memory, and a communication bus; wherein, the processor, communication interface, and memory communicate with each other via the bus; the memory stores computer programs; and the processor executes the programs stored in the memory to achieve the above... Figure 1 The various steps in the method embodiments are the same as those in the above method embodiments, and have the same beneficial effects. To avoid repetition, the embodiments of the present invention will not be described again here.

[0058] It should be noted that the BIM model-based multi-party collaborative construction management and control system provided in this embodiment of the invention and the BIM model-based multi-party collaborative construction management and control method provided in this embodiment of the invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned BIM model-based multi-party collaborative construction management and control method, and has the same or similar beneficial effects. Repeated parts will not be described again.

[0059] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0061] This invention also proposes a computer-readable storage medium storing one or more programs, which, when executed by a BIM model-based multi-party collaborative construction management system including multiple applications, cause the BIM model-based multi-party collaborative construction management system to perform... Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods in the preceding method embodiments, and will not be repeated here.

[0062] The computer-readable storage media include read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A multi-party collaborative construction management method based on a BIM model, characterized in that, include: Acquire point cloud data of the real-time construction project, and determine the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data. Based on the optimal coarse side length, the point cloud data is voxelized to obtain multiple voxel blocks. The average density of the point cloud data distribution in the voxel blocks is determined according to the first number of point cloud data in the voxel blocks, the point cloud volume parameter of the voxel blocks, the first volume of the voxel blocks, and the second number of each voxel block. The optimal side length of the voxel block is determined based on the average density, the downsampling rate, the optimal coarse side length, and the unit side length. The point cloud data is then downsampled based on the optimal side length to obtain the downsampled project point cloud set of the real-time construction project. The BIM model of the real-time construction project is constructed using the downsampled project point cloud, and the real-time construction project is managed and controlled using the BIM model.

2. The multi-party collaborative construction management method based on BIM model according to claim 1, characterized in that, The step of determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: The unit side length is determined based on the second volume of the point cloud data and the third quantity of the point cloud data; The second product between the reciprocal of the downsampling rate and the unit side length is determined as the optimal coarse side length.

3. The multi-party collaborative construction management method based on a BIM model according to claim 2, characterized in that, The second volume is the first product of the ranges of the three dimensions of the point cloud data.

4. The multi-party collaborative construction management method based on BIM model according to claim 1, characterized in that, The step of determining the optimal coarse side length of the voxel block based on the downsampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data includes: The unit side length is determined based on the proximity distance of each point cloud data; The second product between the reciprocal of the downsampling rate and the unit side length is determined as the optimal coarse side length.

5. The multi-party collaborative construction management method based on BIM model according to claim 1, characterized in that, The point cloud volume parameter includes the point cloud volume. Determining the average density of the point cloud distribution in the voxel block based on the first number of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second number of each voxel block includes: The local aggregation index of the voxel block is determined based on the point cloud volume and the first volume; The local aggregation index is normalized to obtain the normalized aggregation index; Calculate the third product between the normalized aggregation index of each voxel block and the first quantity, and sum the third products to obtain the summed value; The first ratio between the superposition value and the second quantity is determined to be the average density of the point cloud data in the voxel block.

6. The multi-party collaborative construction management method based on a BIM model according to claim 5, characterized in that, The step of determining the local aggregation index of the voxel block based on the point cloud volume and the first volume includes: Calculate the first sum between the point cloud volume and the first volume; The second ratio between the first quantity and the first sum is determined as the local aggregation index.

7. The multi-party collaborative construction management method based on BIM model according to claim 1, characterized in that, The point cloud volume parameter includes the convex point cloud volume. Determining the average density of the point cloud distribution in the voxel block based on the first quantity of point cloud data in the voxel block, the point cloud volume parameter of the voxel block, the first volume of the voxel block, and the second quantity of each voxel block includes: The local aggregation index of the voxel block is determined based on the convex point cloud volume and the first volume; The local aggregation index is normalized to obtain the normalized aggregation index; Calculate the third product between the normalized aggregation index of each voxel block and the first quantity, and sum the third products to obtain the summed value; The third ratio between the superposition value and the second quantity is determined to be the average density of the point cloud data in the voxel block.

8. The multi-party collaborative construction management method based on BIM model according to claim 7, characterized in that, The step of determining the local aggregation index of the voxel block based on the convex point cloud volume and the first volume includes: Calculate the second sum between the convex point cloud volume and the first volume; The fourth ratio between the first quantity and the second sum is determined as the local aggregation index.

9. The multi-party collaborative construction management method based on BIM model according to claim 1, characterized in that, The step of determining the optimal side length of the voxel block based on the average density, the downsampling rate, the optimal thick side length, and the unit side length includes: If the average density is greater than the reciprocal of the downsampling rate, the difference between the optimal thick side length and the unit side length is determined to be the optimal side length. If the average density is less than the reciprocal of the downsampling rate, the sum of the optimal thick side length and the unit side length is determined to be the optimal side length.

10. A multi-party collaborative construction management and control system based on a BIM model, characterized in that, include: Processor and memory; wherein the memory is used to store computer programs that can run on the processor; A processor is used to execute a program stored in memory to implement the steps of the multi-party collaborative construction management method based on a BIM model as described in any one of claims 1-9.

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