A digital engineering management system, device, and method based on BIM technology

Abstract

This invention relates to the field of point cloud data processing technology, specifically to a digital engineering management system, device, and method based on BIM technology. The method includes: acquiring point cloud data of cross-sections at different preset locations along the joint lines between different preset roads in an urban road space, thereby constructing a point cloud model; downsampling the point cloud data of each material layer at each preset location in the point cloud model using a voxel grid filter; determining the thickness uniformity evaluation index corresponding to each material layer at each preset location; selecting candidate material layers; performing slope change analysis on the boundary of each material layer within each candidate boundary area; correcting the selected geometric conflict boundary areas; and realizing engineering supervision based on the constructed target parametric model and BIM theoretical model, thereby achieving engineering management. This invention realizes engineering supervision through point cloud data processing and improves the effectiveness of engineering supervision.

Description

Technical Field

[0001] This invention relates to the field of point cloud data processing technology, and specifically to a digital engineering management system, device, and method based on BIM technology. Background Technology

[0002] With the continuous advancement of urbanization, the demand for urban road construction and renovation is increasing daily. As the infrastructure of the urban transportation system, urban roads play a vital role. However, with the increasing scale of engineering projects, the complexity of construction technologies, and the rising requirements for quality and safety management, traditional supervision methods often fall short of the needs of modern project management. In the process of urban road construction, combining BIM (Building Information Modeling) technology to digitally model the target project allows for real-time updates of project data, better expressing the spatial relationships of various components and forming a comprehensive and dynamic engineering information system. This enables supervisors to more intuitively see certain construction difficulties and provide construction guidance. Discrepancies between drawings and actual construction can also be identified and addressed more promptly, facilitating communication with design and construction teams, helping to identify and resolve problems in a timely manner, and assisting supervisors in controlling and managing the quality of building construction projects.

[0003] During BIM modeling of road construction, it is necessary to obtain different cross-sectional parameter information. When the thickness and slope of each layer of road material do not meet the design specifications, geometric conflicts are likely to occur when parametrically modeling the cross-section of urban roads. For example, the elevations of each material layer may overlap or not connect, which may cause the cross-sectional elements to break or overlap during the modeling process.

[0004] While traditional voxel-based collision detection can identify some geometric conflicts, the complexity of urban road cross-sections means that traditional methods often fail to promptly identify subtle parameter deviations during correction. This often leads to poor project supervision, which, as a form of project management, can negatively impact construction quality. Summary of the Invention

[0005] To address the technical problem of poor engineering supervision effectiveness, this invention proposes a digital engineering management system, device, and method based on BIM technology.

[0006] In a first aspect, the present invention provides a digital engineering management method based on BIM technology, the method comprising:

[0007] The point cloud data of cross sections at different preset positions on the joint line between different preset roads in the urban road space is acquired, and a point cloud model is constructed based on the acquired point cloud data.

[0008] The point cloud data of each material layer at each preset location in the point cloud model is downsampled using a voxel grid filter to obtain the target voxel of each material layer at each preset location after downsampling.

[0009] Based on the thickness distribution of the target voxels of each material layer at each preset location, determine the thickness uniformity evaluation index corresponding to each material layer at each preset location.

[0010] Candidate material layers are selected based on thickness uniformity evaluation index, and the boundary area between each candidate material layer and each of its adjacent material layers is determined as the candidate boundary area.

[0011] Slope variation analysis is performed on the boundary of each material layer in each candidate boundary area to obtain the slope variation factor corresponding to the boundary of each material layer in each candidate boundary area.

[0012] Based on the difference between the slope variation factors corresponding to the boundaries of two material layers within the candidate boundary area, geometrically conflicting boundary areas are selected from all candidate boundary areas.

[0013] The geometric conflict boundary area is corrected to construct a target parametric model, and engineering supervision is realized based on the target parametric model and the pre-constructed BIM theoretical model.

[0014] In conjunction with the first aspect above, in one possible implementation, determining the thickness uniformity evaluation index for each material layer at each preset location based on the thickness distribution of the target voxel at each preset location includes:

[0015] The average thickness value of all target voxels of each material layer at each preset location is determined as the representative thickness value of each material layer at each preset location.

[0016] The absolute value of the difference between the thickness value of each target voxel in each material layer at each preset position and the thickness representative value of its corresponding material layer is determined as the thickness deviation for each target voxel in each material layer at each preset position.

[0017] The minimum value among the thickness deviations of all target voxels of each material layer at each preset position is determined as the representative thickness deviation of each material layer at each preset position.

[0018] Based on the difference between the thickness deviation of each target voxel of each material layer at each preset location and the thickness representative deviation of its corresponding material layer, the thickness uniformity evaluation index of each material layer at each preset location is determined.

[0019] In conjunction with the first aspect above, in one possible implementation, the selection of candidate material layers based on the thickness uniformity evaluation index includes:

[0020] Any preset position is designated as a marker position, and any material layer at the marker position is designated as a marker material layer;

[0021] If the thickness uniformity evaluation index corresponding to the marked material layer is less than or equal to the preset uniformity threshold, then the marked material layer is determined as a candidate material layer.

[0022] In conjunction with the first aspect above, in one possible implementation, the step of performing slope variation analysis on the boundary of each material layer within each candidate boundary region to obtain the slope variation factor corresponding to the boundary of each material layer within each candidate boundary region includes:

[0023] Any candidate boundary region is designated as a marked boundary region, and any material layer boundary within the marked boundary region is designated as a marked layer boundary.

[0024] The edge voxels on the boundary of the marker layer are refined at different preset levels to obtain the refined voxels of the boundary of the marker layer at different preset levels.

[0025] Based on each refined voxel of the marker layer boundary at each preset level and its adjacent refined voxels, determine the local slope change corresponding to each refined voxel of the marker layer boundary at each preset level.

[0026] Based on the local slope changes corresponding to all refined voxels at each preset level, the boundary of the marker layer is segmented to obtain the edge segments of the marker layer boundary at each preset level.

[0027] The local slope change corresponding to the refined voxel on the edge segment is determined as the representative value of the slope change corresponding to the edge segment. The edge segments with the same representative value of slope change corresponding to the boundary of the marker layer at each preset level are formed into an edge segment sequence, thus obtaining multiple edge segment sequences of the boundary of the marker layer at each preset level.

[0028] Based on the distance between adjacent edge segments in each edge segment sequence of the marker layer boundary at each preset level, determine the slope change trend corresponding to each edge segment sequence of the marker layer boundary at each preset level;

[0029] Based on the differences in the slope change trends of different edge segment sequences at each preset level, the fluctuation change patterns of the marker layer boundary at each preset level are determined.

[0030] The average value of the differences in the fluctuation patterns of the boundary of the marker layer under all preset levels is determined as the slope change factor corresponding to the boundary of the marker layer.

[0031] In conjunction with the first aspect above, in one possible implementation, determining the local slope change corresponding to each refined voxel of the marker layer boundary at each preset level based on each refined voxel of the marker layer boundary at each preset level and its adjacent refined voxels includes:

[0032] Any preset level is determined as the marking level, and any refined voxel of the marking layer boundary under the marking level is determined as the reference voxel;

[0033] From the boundary of the marker layer and on both sides of the reference voxel, select one refined voxel that is closest to the reference voxel, and denot it as the first temporary voxel and the second temporary voxel, respectively.

[0034] Based on the vertical and horizontal distances between the reference voxel and the first temporary voxel, the slope change angle between the reference voxel and the first temporary voxel is determined and denoted as the first slope change angle.

[0035] Similarly, based on the vertical and horizontal distances between the reference voxel and the second temporary voxel, the slope change angle between the reference voxel and the second temporary voxel is determined and denoted as the second slope change angle.

[0036] The average of the first slope change angle and the second slope change angle is determined as the local slope change corresponding to the reference voxel.

[0037] In conjunction with the first aspect above, in one possible implementation, the step of segmenting the marker layer boundary based on the local slope changes corresponding to all refined voxels at each preset level to obtain the edge segments of the marker layer boundary at each preset level includes:

[0038] Any preset level is determined as the marking level, and the sub-boundary segment composed of refined voxels with the same local slope change corresponding to the marking level is determined as the edge segment.

[0039] In conjunction with the first aspect above, in one possible implementation, the step of selecting geometrically conflicting boundary regions from all candidate boundary regions based on the difference in slope variation factors between the boundaries of two material layers within the candidate boundary region includes:

[0040] Based on the absolute value of the difference between the slope variation factors corresponding to the two material layer boundaries within each candidate boundary area, the overlap evaluation index corresponding to each candidate boundary area is determined.

[0041] If the overlap evaluation index corresponding to the candidate boundary region is less than the preset overlap threshold, the candidate boundary region will be determined as a geometric conflict boundary region.

[0042] In conjunction with the first aspect above, in one possible implementation, the correction of the geometric conflict boundary region includes:

[0043] Based on the overlap evaluation index corresponding to the geometric conflict boundary region, determine the error correction factor corresponding to the geometric conflict boundary region;

[0044] Based on the error correction factor corresponding to the geometric conflict boundary region, the voxels within the geometric conflict boundary region are corrected, thereby achieving the correction of the geometric conflict boundary region.

[0045] Secondly, the present invention provides a digital engineering management system based on BIM technology, the system comprising:

[0046] The data acquisition and construction module is used to acquire point cloud data of cross-sections at different preset positions on the joint lines between different preset roads in the urban road space, and to construct point cloud models based on the acquired point cloud data.

[0047] The downsampling module is used to downsample the point cloud data of each material layer at each preset location in the point cloud model using a voxel grid filter, so as to obtain the target voxel of each material layer at each preset location after downsampling.

[0048] The thickness uniformity evaluation index determination module is used to determine the thickness uniformity evaluation index corresponding to each material layer at each preset position based on the thickness distribution of the target voxel of each material layer at each preset position.

[0049] The screening and determination module is used to screen candidate material layers based on the thickness uniformity evaluation index, and to determine the boundary area between each candidate material layer and each of its adjacent material layers as the candidate boundary area.

[0050] The slope change analysis and processing module is used to perform slope change analysis and processing on each material layer boundary in each candidate boundary area to obtain the slope change factor corresponding to each material layer boundary in each candidate boundary area.

[0051] The region filtering module is used to filter out geometrically conflicting boundary regions from all candidate boundary regions based on the difference between the slope variation factors corresponding to the boundaries of two material layers within the candidate boundary region.

[0052] The modified construction supervision module is used to correct geometric conflict boundary areas, thereby constructing a target parametric model, and realizing project supervision based on the target parametric model and the pre-constructed BIM theoretical model.

[0053] Thirdly, a digital engineering management device based on BIM technology is provided, including a memory and a processor. The processor is used to process instructions stored in the memory to implement the methods in the first aspect or any possible implementation thereof.

[0054] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0055] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0056] The present invention has the following beneficial effects:

[0057] This invention discloses a digital engineering management method based on BIM technology. Through point cloud data processing, it achieves engineering supervision, solving the technical problem of poor engineering supervision effectiveness and improving its efficiency. Specifically, this invention constructs a point cloud model based on point cloud data of cross-sections at different preset locations along the joint lines between different preset roads in urban road spaces. This quantifies the thickness uniformity evaluation index of each material layer at each preset location and performs slope change analysis on the boundary of each material layer within each candidate boundary area. This allows for relatively accurate screening of geometrically conflicting boundary areas, correction of these areas, and ultimately, engineering supervision, thus improving its effectiveness. Attached Figure Description

[0058] 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.

[0059] Figure 1 A flowchart of a digital engineering management method based on BIM technology according to the present invention;

[0060] Figure 2This is a schematic diagram of the composition structure of a digital engineering management system based on BIM technology according to the present invention;

[0061] Figure 3 This is a schematic diagram of the structure of a computer device according to the present invention. Detailed Implementation

[0062] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. 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.

[0063] 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.

[0064] refer to Figure 1 This document illustrates the flowchart of some embodiments of a BIM-based digital engineering management method according to the present invention. The BIM-based digital engineering management method includes the following steps:

[0065] Step S1: Obtain point cloud data of cross-sections at different preset positions on the joint lines between different preset roads in the urban road space, and construct a point cloud model based on the obtained point cloud data.

[0066] The pre-designated road can be any road within the urban road space that is to be subject to engineering supervision. Pre-designated roads can be, but are not limited to, motor vehicle lanes, non-motor vehicle lanes, sidewalks, green belts, and utility corridors. Different pre-designated roads within the urban road space are also referred to as different elements within the urban road space. The joint line between different pre-designated roads is the boundary line between them. Pre-designated locations can be different positions pre-set on the joint line. The cross-section at a pre-designated location, that is, the road cross-section at that location, can be perpendicular to the joint line. The distance between adjacent pre-designated locations can be 10 meters.

[0067] As an example, this step may include the following steps:

[0068] The first step is to select a portable ground laser scanner, ensure that the equipment is working properly, and then perform a laser scan on the cross-section of the urban road under construction at the preset location to obtain point cloud data of the road cross-section.

[0069] It should be noted that, depending on the construction progress, a scan can be performed after each layer of covering material is completed. The difference between two adjacent point cloud data sets indicates the thickness of the current road construction covering material layer.

[0070] The second step is to denoise the collected point cloud data, remove outliers caused by sensors or environmental factors, and then perform feature matching on the collected point cloud data based on the measurement marker positions. This aligns the point cloud data collected at different scanning positions and times to the same coordinate system and marks them as point cloud models.

[0071] It should be noted that the outlier identification method that needs to be removed when denoising point cloud data can be, but is not limited to, identifying outliers by calculating the local neighborhood statistics of each point.

[0072] Step S2: Use a voxel grid filter to downsample the point cloud data of each material layer at each preset location in the point cloud model to obtain the target voxel of each material layer at each preset location after downsampling.

[0073] It should be noted that comparing the differences between the point cloud model and the theoretical model to obtain the quality deviation in the construction process is problematic. Since point cloud data consists of several discrete points, directly comparing it with the theoretical model does not conform to the designers' operational habits regarding lines, surfaces, and volumes. Therefore, it is necessary to parametrically construct the collected road cross-section point cloud model data and use the model parameters for quality comparison. The theoretical model can be a BIM model constructed from project design drawings. During the parametric modeling process of point cloud data, deviations in reflected signals during different scanning times often lead to geometric conflicts in the matching process between different road layers. This often results in discrepancies between the real-time monitored road construction model and the actual construction situation, leading to errors in the quality judgment of the actual construction process and affecting the accuracy of construction supervision results. Therefore, subsequent analysis of the real-time collected point cloud data identifies potential geometric conflict areas, and modeling and correcting these areas yields an accurate parametric road cross-section model.

[0074] As an example, since different material layers reflect laser light differently, the collected point cloud data can be divided into multiple different material layers based on the different surface reflection changes obtained when the laser scans different material layers. A voxel grid filter is used to downsample the point cloud data for each material layer, transforming the collected dense point cloud data into sparse three-dimensional grid data. This effectively reduces the amount of data while preserving the main geometric features. The sparse three-dimensional grid data obtained at this time is the target voxel.

[0075] Step S3: Based on the thickness distribution of the target voxels of each material layer at each preset location, determine the thickness uniformity evaluation index corresponding to each material layer at each preset location.

[0076] It should be noted that point cloud data is three-dimensional spatial data acquired through laser scanning, representing the geometric shape of urban road cross-sections in the real environment. Urban road cross-sections are typically composed of multiple material layers, including asphalt layers, base layers, drainage layers, etc. Due to the uniformity of road paving, ideally, the paving state of each material layer during the road paving process should be uniform. Therefore, different locations within the same layer of the road cross-section should exhibit uniform thickness. By calculating the thickness variations of different material layers, abnormal deviation areas with significant geometric conflicts can be located.

[0077] As an example, this step may include the following steps:

[0078] The first step is to determine the average thickness value of all target voxels in each material layer at each preset location as the representative thickness value for each material layer at each preset location.

[0079] The thickness value corresponding to the target voxel can characterize the thickness information carried by the target voxel.

[0080] For example, the formula for determining the representative thickness value of different material layers at different preset locations can be:

[0081] ;

[0082] in, This represents the thickness of the j-th material layer at the i-th preset position. i is the index of the preset position. j is the index of the material layer in the cross-section at the preset position. is the number of target voxels in the j-th material layer at the i-th preset position. a is the index of the target voxels in the j-th material layer at the i-th preset position. It is the thickness value corresponding to the a-th target voxel of the j-th material layer at the i-th preset position.

[0083] The second step is to determine the thickness deviation of each target voxel in each material layer at each preset position by taking the absolute value of the difference between the thickness value of each target voxel in each material layer at each preset position and the thickness representative value of the corresponding material layer.

[0084] For example, the formula for determining the thickness deviation of target voxels at different preset locations and for different material layers can be:

[0085] ;

[0086] in, is the thickness deviation corresponding to the a-th target voxel of the j-th material layer at the i-th preset position. i is the index of the preset position. j is the index of the material layer in the cross-section at the preset position. a is the index of the target voxel of the j-th material layer at the i-th preset position. It is the representative thickness value corresponding to the j-th material layer at the i-th preset position. It is the thickness value corresponding to the a-th target voxel of the j-th material layer at the i-th preset position.

[0087] The third step is to determine the minimum thickness deviation among all target voxels of each material layer at each preset position as the representative thickness deviation for each material layer at each preset position.

[0088] The fourth step is to determine the thickness uniformity evaluation index for each material layer at each preset position based on the difference between the thickness deviation of each target voxel at each preset position and the thickness representative deviation of its corresponding material layer.

[0089] For example, the formula for determining the thickness uniformity evaluation index of different material layers at different preset locations can be:

[0090] ;

[0091] in, This is the thickness uniformity evaluation index corresponding to the j-th material layer at the i-th preset position. i is the index of the preset position. j is the index of the material layer in the cross-section at the preset position. It is a natural exponential function. is the number of target voxels in the j-th material layer at the i-th preset position. a is the index of the target voxels in the j-th material layer at the i-th preset position. It is the thickness deviation corresponding to the a-th target voxel of the j-th material layer at the i-th preset position. It represents the thickness deviation corresponding to the j-th material layer at the i-th preset position.

[0092] It should be noted that when A larger value usually indicates that the thickness variation at different positions within the j-th material layer at the i-th preset position is smaller, and that the thickness uniformity at different positions within the j-th material layer at the i-th preset position is greater.

[0093] Step S4: Based on the thickness uniformity evaluation index, candidate material layers are selected, and the boundary area between each candidate material layer and each of its adjacent material layers is determined as the candidate boundary area.

[0094] It should be noted that there are often two material layers adjacent to a material layer. If a material layer is located at the top or bottom, there is often only one material layer adjacent to it. Any material layer adjacent to a candidate material layer is designated as the reference material layer, and the region between the boundary of the candidate material layer and the boundary of the reference material layer is designated as the candidate boundary region.

[0095] As an example, this step may include the following steps:

[0096] The first step is to designate any preset location as the marker location, and to designate any material layer at the marked location as the marker material layer.

[0097] The second step is to determine the marked material layer as a candidate material layer if the thickness uniformity evaluation index corresponding to the marked material layer is less than or equal to the preset uniformity threshold.

[0098] The preset uniform threshold can be a pre-set threshold, which can be 0.75.

[0099] It should be noted that the greater the variation in voxel characteristic parameters within the same layer, the more inconsistent the thickness uniformity within that layer tends to be. This can lead to geometric conflicts with adjacent layers during the parameterization process, resulting in inaccurate parameterization. Therefore, layers with thickness uniformity evaluation indices less than or equal to a preset uniformity threshold are selected, and further analysis of several boundaries between the non-uniform thickness regions of each layer is required. In this embodiment of the invention, the layer refers to the material layer.

[0100] The third step is to determine the boundary region between each candidate material layer and each of its adjacent material layers as the candidate boundary region.

[0101] Step S5: Perform slope change analysis on the boundary of each material layer in each candidate boundary area to obtain the slope change factor corresponding to the boundary of each material layer in each candidate boundary area.

[0102] It should be noted that the more timely the discovery of abnormal construction conditions during construction supervision, the more construction and reconstruction costs can be saved. Therefore, the earlier minor defects in the construction process are identified, the better the supervision effect. To accelerate the efficiency of the engineering supervision comparison process, traditional point cloud collision detection methods usually use voxelized meshes of the same scale, which can only capture macroscopic abnormalities at the boundaries and cannot identify minor geometric conflicts. This embodiment of the invention refines the boundaries of the identified uneven thickness regions of the layers through multi-level mesh refinement. The refined mesh can provide more detailed information. By analyzing the consistency of slope feature changes at the layer boundaries during multi-level refinement, geometric conflicts in the modeling process that may be caused by abnormal point cloud data reflection can be identified. Based on the characteristic of road construction being laid layer by layer, the boundaries between two adjacent layers often show consistent slope information changes. However, deviations in collecting point cloud reflections often affect the consistency of slope changes, leading to scale differences in the parametric modeling of spatial layer features. This results in the same parametric feature corresponding to multiple candidate matching objects in space. This many-to-many matching often leads to geometric conflicts in the modeling process.

[0103] As an example, this step may include the following steps:

[0104] The first step is to identify any candidate boundary region as a marked boundary region, and then identify any material layer boundary within the marked boundary region as a marked layer boundary.

[0105] The second step is to refine the edge voxels on the boundary of the above-mentioned marker layer at different preset levels to obtain the refined voxels of the boundary of the above-mentioned marker layer at different preset levels.

[0106] Different preset levels of refinement can represent voxelized meshes of different sizes. Refined voxels at different preset levels can be voxels of voxelized meshes of different sizes.

[0107] It should be noted that for edge voxels on the boundary of the marker layer, each edge voxel can be divided into 8 sub-voxels to form a first-level refined mesh, achieving the first preset level of refinement. The sub-voxels obtained at this time are recorded as the refined voxels under the first preset level. The result after the first-level refinement is further refined, that is, each refined voxel under the first preset level can be divided into 8 sub-voxels to form a second-level refined mesh, achieving the second preset level of refinement. The latest sub-voxels obtained at this time are recorded as the refined voxels under the second preset level. This process is repeated multiple times to obtain multi-level refined meshes. Based on the characteristic of road construction being laid layer by layer, during the multi-level refinement process, the slope parameter factors at the boundaries of different mesh levels can be compared to determine whether there is consistency in slope variation.

[0108] The third step, determining the local slope change corresponding to each refined voxel of the marker layer boundary at each preset level based on the aforementioned refined voxel and its adjacent refined voxels, may include the following sub-steps:

[0109] The first sub-step involves determining any preset level as the marker level and determining any refined voxel at the marker level as the reference voxel, which is the boundary of the marker layer.

[0110] The second sub-step involves selecting one refined voxel closest to the reference voxel from the boundary of the aforementioned marker layer and located on both sides of the aforementioned reference voxel, and denoting them as the first temporary voxel and the second temporary voxel, respectively.

[0111] The third sub-step involves determining the slope change angle between the reference voxel and the first temporary voxel based on the vertical and horizontal distances between them, and denoting it as the first slope change angle.

[0112] The vertical distance between the reference voxel and the first temporary voxel can be the vertical distance between these two voxels on the cross-section of their respective roads, which can characterize the height difference. The horizontal distance between the reference voxel and the first temporary voxel can be the horizontal distance between these two voxels on the cross-section of their respective roads.

[0113] For example, the formula for determining the slope change angle between the reference voxel and the first temporary voxel can be:

[0114] ;

[0115] Where p is the slope change angle between the reference voxel and the first temporary voxel. It is the arctangent function. It is the vertical distance between the reference voxel and the first temporary voxel. It is the horizontal distance between the reference voxel and the first temporary voxel. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0116] It should be noted that determining the slope change angle between adjacent voxels based on the height difference between them reflects, to some extent, the slope change trend of voxels distributed along the boundary line.

[0117] The fourth sub-step, similarly, determines the slope change angle between the reference voxel and the second temporary voxel based on the vertical and horizontal distances between them, and denotes it as the second slope change angle.

[0118] It should be noted that the method for obtaining the second slope change angle is the same as the method for obtaining the first slope change angle, and will not be repeated here.

[0119] The fifth sub-step is to determine the average of the first slope change angle and the second slope change angle as the local slope change corresponding to the reference voxel.

[0120] The fourth step is to segment the marker layer boundary according to the local slope changes corresponding to all refined voxels at each preset level, thereby obtaining the edge segments of the marker layer boundary at each preset level.

[0121] For example, any preset level can be defined as the marking level, and the sub-boundary segment composed of refined voxels with the same local slope change corresponding to the marking layer boundary at the marking level can be defined as the edge segment.

[0122] It should be noted that there may be multiple sub-boundary segments formed by refined voxels with the same local slope change at the marker level for the marker layer boundary. Therefore, there may be multiple edge segments with the same local slope change at the marker level for the marker layer boundary.

[0123] The fifth step is to determine the local slope change corresponding to the refined voxel on the edge segment as the representative value of the slope change corresponding to the edge segment, and to form an edge segment sequence by forming the edge segments with the same representative value of slope change corresponding to each preset level of the above-mentioned marker layer boundary, thus obtaining multiple edge segment sequences of the above-mentioned marker layer boundary at each preset level.

[0124] It should be noted that edge segments within the same preset level at the boundary of the marker layer often have the same slope change representative value. The edge segments in the edge segment sequence can be ordered according to their position on the boundary of the marker layer, that is, they can be ordered from one side of the boundary to the other.

[0125] Step 6: Based on the distance between adjacent edge segments in each edge segment sequence of the above-mentioned marker layer boundary at each preset level, determine the slope change trend corresponding to each edge segment sequence of the above-mentioned marker layer boundary at each preset level.

[0126] The method for obtaining the distance between two edge segments can be as follows: the two edge segments are respectively designated as the first edge segment and the second edge segment, and each voxel on the first edge segment is designated as the first voxel, each voxel on the second edge segment is designated as the second voxel, the distance between each first voxel and each second voxel is designated as the reference distance, and the minimum value among all reference distances is designated as the distance between the first edge segment and the second edge segment.

[0127] For example, the formula for determining the slope change trend of different edge segment sequences at different preset levels of the marker layer boundary can be:

[0128] ;

[0129] in, This represents the slope change trend corresponding to the c-th edge segment sequence at the b-th preset level of the marker layer boundary. b is the preset level number. c is the edge segment sequence number at the b-th preset level. is the number of edge segments in the c-th edge segment sequence at the b-th preset level of the marker layer boundary. f is the index of the edge segment in the c-th edge segment sequence at the b-th preset level of the marker layer boundary. It is an absolute value function. It is the distance between the f-th edge segment and the (f+1)-th edge segment in the c-th edge segment sequence at the b-th preset level of the marker layer boundary. It is the distance between the (f+1)th edge segment and the (f+2)th edge segment in the (c)th edge segment sequence at the (b)th preset level of the marker layer boundary.

[0130] It should be noted that when The smaller the value, the smaller the distance between different edge segments in the c-th edge segment sequence under the b-th preset level of the marker layer boundary. This often indicates that the slope of the edge segments is relatively consistent and that the edges of the edge segments in the c-th edge segment sequence are relatively regular.

[0131] Step 7: Based on the differences in slope change trends between different edge segment sequences at each preset level, determine the differences in the fluctuation change patterns of the aforementioned marker layer boundary at each preset level.

[0132] For example, the formula for determining the differences in the fluctuation patterns of the marker layer boundary at different preset levels can be:

[0133] ;

[0134] in, It represents the difference in the fluctuation pattern of the marker layer boundary at the b-th preset level. b is the sequence number of the preset level. It represents the number of edge segment sequences at the b-th preset level. c and k are the sequence numbers of different edge segment sequences at the b-th preset level. It is an absolute value function. It represents the slope change trend corresponding to the c-th edge segment sequence of the marker layer boundary at the b-th preset level. It represents the slope change trend corresponding to the kth edge segment sequence of the marker layer boundary at the bth preset level. It is the average value of the slope change representative value corresponding to all edge segments of the marker layer boundary at the b-th preset level. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0135] It should be noted that by calculating the difference in slope variation between edge voxel segments at the boundary within the same refinement level, the undulation trend at the boundary can be reflected. Therefore, The smaller the value, the more consistent the slope variation pattern at the boundary of the same material layer in the refinement level under the b preset level.

[0136] The eighth step is to determine the average of the differences in the fluctuation patterns of the above-mentioned marker layer boundary under all preset levels as the slope change factor corresponding to the above-mentioned marker layer boundary.

[0137] For example, the formula for determining the slope variation factor corresponding to the boundary of the marked layer can be:

[0138] ;

[0139] Where q is the slope variation factor corresponding to the boundary of the marked layer. N is the number of preset levels. b is the sequence number of the preset level. It is the difference in the fluctuation pattern of the boundary of the marker layer at the b-th preset level.

[0140] It should be noted that when comparing the slope factors of edge segments located at the boundary positions within the same material layer across different refined meshes, data anomalies caused by point cloud data reflection anomalies can lead to minor geometric conflicts in the constructed parametric model. Due to the instability of the geometric conflict structure, the slope factor will exhibit significant abnormal fluctuations during multi-level refinement. Therefore, a slope variation factor can be constructed at the boundary between layers. A larger q generally indicates greater fluctuation in the slope variation pattern, more pronounced edge information changes at the boundary of the same material layer across several refinement levels, a higher probability of geometric conflicts at the boundary, and a greater likelihood of geometric conflicts within the boundary region to which the marked layer boundary belongs.

[0141] Step S6: Based on the difference between the slope variation factors corresponding to the boundaries of the two material layers within the candidate boundary area, select the geometrically conflicting boundary areas from all candidate boundary areas.

[0142] As an example, this step may include the following steps:

[0143] The first step is to determine the overlap evaluation index for each candidate boundary region based on the absolute value of the difference between the slope change factors corresponding to the boundaries of the two material layers within each candidate boundary region.

[0144] For example, the formula for determining the overlap evaluation index corresponding to the candidate boundary region can be:

[0145] ;

[0146] Where R is the overlap evaluation index corresponding to the candidate boundary region. It is a natural exponential function. It is an absolute value function. and These are the slope variation factors corresponding to the boundaries of the two material layers within the candidate boundary area.

[0147] It should be noted that, based on the characteristics of layered paving in actual installation, theoretically, the interface between a newly laid material layer and the original layer should completely overlap. That is, the slope variation on both sides of the same boundary should generally be consistent. However, due to reflection errors, if the collected point cloud data contains overlaps, sections, or gaps, the slope variation patterns on both sides of the boundary will often show significant differences. A smaller R value generally indicates a greater likelihood of significant differences in the slope variation patterns of the two material layers within the candidate boundary area, suggesting poorer data overlap and a higher probability of false point cloud data. Conversely, a larger R value generally indicates higher consistency in the geometric relationship between the two material layers, suggesting a higher degree of overlap between the interface surfaces.

[0148] The second step is to determine the candidate boundary region as a geometric conflict boundary region if the overlap evaluation index corresponding to the candidate boundary region is less than the preset overlap threshold.

[0149] The preset overlap threshold can be a pre-set threshold, which can be 0.85.

[0150] It should be noted that if the overlap evaluation index corresponding to the candidate boundary region is less than the preset overlap threshold, the uneven thickness of the material layer at the candidate boundary region is more likely to be caused by geometric conflicts in the modeling process.

[0151] Step S7: Correct the geometric conflict boundary area to construct the target parametric model, and realize the project supervision based on the target parametric model and the pre-constructed BIM theoretical model.

[0152] It should be noted that for the identified geometric conflict boundary areas, the overlap relationship between the boundaries between levels is verified according to the overlap evaluation index. When they do not overlap, it indicates that there are spurious voxels in the level that affect the boundary relationship, and the spurious voxels need to be readjusted.

[0153] As an example, this step may include the following steps:

[0154] The first step is to determine the error correction factor corresponding to the geometric conflict boundary region based on the overlap evaluation index corresponding to the geometric conflict boundary region.

[0155] For example, the formula for determining the error correction factor corresponding to the geometric conflict boundary region can be:

[0156] ;

[0157] Where w is the error correction factor corresponding to the geometric conflict boundary region. It is a normalized function. L is the overlap evaluation index corresponding to the geometric conflict boundary region. It is an absolute value function. and These are the slope variation factors corresponding to the boundaries of the two material layers within the geometric conflict boundary area. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0158] It should be noted that when w is larger, that is, when w is closer to 1, it often indicates that the overlap between the boundaries of the two material layers in the geometric conflict boundary region is higher, which often means that the original voxels in the geometric conflict boundary region should be preserved, and that there is often less need to adjust the geometric conflict boundary region.

[0159] The second step is to correct the voxels within the geometric conflict boundary region based on the error correction factor corresponding to the geometric conflict boundary region, thereby achieving the correction of the geometric conflict boundary region.

[0160] For example, the product of a voxel within the geometric conflict boundary region and its corresponding error correction factor can be used as the corrected voxel, thereby achieving the correction of the geometric conflict boundary region.

[0161] The third step is to construct a target parameterized model based on the corrected geometric conflict boundary region.

[0162] It should be noted that the corrected point cloud data can be imported into the OpenRoads Designer software, and the road centerline and other parameters can be input to complete the parametric processing of the point cloud data. The model constructed at this time is recorded as the target parametric model.

[0163] The fourth step, based on the target parametric model and the pre-built BIM theoretical model, to implement project supervision may include the following sub-steps:

[0164] The first sub-step is to construct a BIM theoretical model.

[0165] For example, obtain architectural design drawings, import the data from the drawings into Revit modeling software, create corresponding BIM model data according to the design drawings, and record the BIM model constructed at this time as the BIM theoretical model for comparison with point cloud model data.

[0166] The second sub-step involves determining the deviation coefficient for each voxel based on the target parameterized model and the pre-built BIM theoretical model.

[0167] It should be noted that during the comparison and matching process between the BIM theoretical model and the point cloud model, the experience accumulated during the analysis and correction process can be fed back into the subsequent modeling and construction process to gradually optimize the output of the corrected real-time point cloud model. By calculating the distance between the parametric model and the BIM theoretical model, the deviation coefficient of each parameter block can be determined.

[0168] It should be noted that in the embodiments of the present invention, different parameter blocks can represent different material layers of different road cross sections.

[0169] The third sub-step involves determining the deviation threat assessment for each preset road based on the deviation coefficients of all parameter blocks corresponding to each preset road.

[0170] For example, the formula for determining the deviation threat assessment corresponding to a preset road can be:

[0171] ;

[0172] Wherein, G is the deviation threat assessment corresponding to the preset road. It is a normalization function. M is the total number of parameter blocks corresponding to the preset road. m is the index of the parameter block corresponding to the preset road. It is the deviation coefficient corresponding to the m-th parameter block of the preset road.

[0173] The fourth sub-step involves identifying the road as a construction anomaly area if the deviation threat assessment corresponding to the preset road exceeds the preset threat threshold. This allows relevant personnel to record the deviation at the corresponding location within the construction anomaly area and to arrange for construction workers to adjust the parts that need to be repaired according to the theoretical model.

[0174] The preset threat threshold can be a pre-set threshold, which can be 0.68.

[0175] Based on the same inventive concept as the above-described method embodiments, this invention provides a digital engineering management device based on BIM technology, including a memory and a processor. The processor processes instructions stored in the memory to implement any of the above-described digital engineering management methods based on BIM technology.

[0176] refer to Figure 2 Based on the same inventive concept as the above-described method embodiments, this invention provides a digital engineering management system based on BIM technology. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a digital engineering management method based on BIM technology, specifically including:

[0177] The data acquisition and construction module 201 is used to acquire point cloud data of cross sections at different preset positions on the joint lines between different preset roads in the urban road space, and to construct a point cloud model based on the acquired point cloud data.

[0178] The downsampling module 202 is used to downsample the point cloud data of each material layer at each preset position in the point cloud model using a voxel grid filter to obtain the target voxel of each material layer at each preset position after downsampling.

[0179] The thickness uniformity evaluation index determination module 203 is used to determine the thickness uniformity evaluation index corresponding to each material layer at each preset position based on the thickness distribution of the target voxel of each material layer at each preset position.

[0180] The screening and determination module 204 is used to screen candidate material layers based on the thickness uniformity evaluation index, and to determine the boundary area between each candidate material layer and each of its adjacent material layers as the candidate boundary area.

[0181] The slope change analysis and processing module 205 is used to perform slope change analysis and processing on each material layer boundary in each candidate boundary area to obtain the slope change factor corresponding to each material layer boundary in each candidate boundary area.

[0182] The region filtering module 206 is used to filter out geometrically conflicting boundary regions from all candidate boundary regions based on the difference between the slope variation factors corresponding to the boundaries of two material layers within the candidate boundary region.

[0183] The modified construction supervision module 207 is used to correct the geometric conflict boundary area, thereby constructing the target parametric model, and realizing project supervision based on the target parametric model and the pre-constructed BIM theoretical model.

[0184] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 3As shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any of the aforementioned digital engineering management methods based on BIM technology.

[0185] Based on the same inventive concept as the above-described method embodiments, the present invention provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to execute any of the above-described digital engineering management methods based on BIM technology.

[0186] Based on the same inventive concept as the above-described method embodiments, the present invention provides a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform any of the above-described digital engineering management methods based on BIM technology.

[0187] In summary, this invention constructs a point cloud model based on point cloud data of cross-sections at different preset positions on the joint lines between different preset roads in urban road space. This model quantifies the thickness uniformity evaluation index of each material layer at each preset position and performs slope change analysis on the boundary of each material layer in each candidate boundary area. This allows for relatively accurate screening of geometric conflict boundary areas, correction of geometric conflict boundary areas, and ultimately, the realization of engineering supervision and the improvement of engineering supervision effectiveness.

[0188] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A digital engineering management method based on BIM technology, characterized in that, Includes the following steps: The point cloud data of cross sections at different preset positions on the joint line between different preset roads in the urban road space is acquired, and a point cloud model is constructed based on the acquired point cloud data. The point cloud data of each material layer at each preset location in the point cloud model is downsampled using a voxel grid filter to obtain the target voxel of each material layer at each preset location after downsampling. Based on the thickness distribution of the target voxels of each material layer at each preset location, determine the thickness uniformity evaluation index corresponding to each material layer at each preset location. Candidate material layers are selected based on thickness uniformity evaluation index, and the boundary area between each candidate material layer and each of its adjacent material layers is determined as the candidate boundary area. Slope variation analysis is performed on the boundary of each material layer in each candidate boundary area to obtain the slope variation factor corresponding to the boundary of each material layer in each candidate boundary area. Based on the difference between the slope variation factors corresponding to the boundaries of two material layers within the candidate boundary area, geometrically conflicting boundary areas are selected from all candidate boundary areas. The geometric conflict boundary area is corrected to construct the target parametric model, and the project supervision is realized based on the target parametric model and the pre-constructed BIM theoretical model; The step of determining the thickness uniformity evaluation index for each material layer at each preset location based on the thickness distribution of the target voxel at each preset location includes: The average thickness value of all target voxels of each material layer at each preset location is determined as the representative thickness value of each material layer at each preset location. The absolute value of the difference between the thickness value of each target voxel in each material layer at each preset position and the thickness representative value of its corresponding material layer is determined as the thickness deviation for each target voxel in each material layer at each preset position. The minimum value among the thickness deviations of all target voxels of each material layer at each preset position is determined as the representative thickness deviation of each material layer at each preset position. Based on the difference between the thickness deviation of each target voxel of each material layer at each preset location and the thickness representative deviation of its corresponding material layer, the thickness uniformity evaluation index of each material layer at each preset location is determined. The slope variation analysis of each material layer boundary within each candidate boundary region is performed to obtain the slope variation factor corresponding to each material layer boundary within each candidate boundary region, including: Any candidate boundary region is designated as a marked boundary region, and any material layer boundary within the marked boundary region is designated as a marked layer boundary. The edge voxels on the boundary of the marker layer are refined at different preset levels to obtain the refined voxels of the boundary of the marker layer at different preset levels. Based on each refined voxel of the marker layer boundary at each preset level and its adjacent refined voxels, determine the local slope change corresponding to each refined voxel of the marker layer boundary at each preset level. Based on the local slope changes corresponding to all refined voxels at each preset level, the boundary of the marker layer is segmented to obtain the edge segments of the marker layer boundary at each preset level. The local slope change corresponding to the refined voxel on the edge segment is determined as the representative value of the slope change corresponding to the edge segment. The edge segments with the same representative value of slope change corresponding to the boundary of the marker layer at each preset level are formed into an edge segment sequence, thus obtaining multiple edge segment sequences of the boundary of the marker layer at each preset level. Based on the distance between adjacent edge segments in each edge segment sequence of the marker layer boundary at each preset level, determine the slope change trend corresponding to each edge segment sequence of the marker layer boundary at each preset level; Based on the differences in the slope change trends of different edge segment sequences at each preset level, the fluctuation change patterns of the marker layer boundary at each preset level are determined. The average value of the differences in the fluctuation patterns of the boundary of the marker layer under all preset levels is determined as the slope change factor corresponding to the boundary of the marker layer.

2. The digital engineering management method based on BIM technology according to claim 1, characterized in that, The selection of candidate material layers based on thickness uniformity evaluation indicators includes: Any preset position is designated as a marker position, and any material layer at the marker position is designated as a marker material layer; If the thickness uniformity evaluation index corresponding to the marked material layer is less than or equal to the preset uniformity threshold, then the marked material layer is determined as a candidate material layer.

3. The digital engineering management method based on BIM technology according to claim 1, characterized in that, The step of determining the local slope change corresponding to each refined voxel of the marker layer boundary at each preset level based on each refined voxel of the marker layer boundary at each preset level and its adjacent refined voxels includes: Any preset level is determined as the marking level, and any refined voxel of the marking layer boundary under the marking level is determined as the reference voxel; From the boundary of the marker layer and on both sides of the reference voxel, select one refined voxel that is closest to the reference voxel, and denot it as the first temporary voxel and the second temporary voxel, respectively. Based on the vertical and horizontal distances between the reference voxel and the first temporary voxel, the slope change angle between the reference voxel and the first temporary voxel is determined and denoted as the first slope change angle. Similarly, based on the vertical and horizontal distances between the reference voxel and the second temporary voxel, the slope change angle between the reference voxel and the second temporary voxel is determined and denoted as the second slope change angle. The average of the first slope change angle and the second slope change angle is determined as the local slope change corresponding to the reference voxel.

4. The digital engineering management method based on BIM technology according to claim 1, characterized in that, The step of segmenting the marker layer boundary based on the local slope changes corresponding to all refined voxels at each preset level to obtain the edge segments of the marker layer boundary at each preset level includes: Any preset level is determined as the marking level, and the sub-boundary segment composed of refined voxels with the same local slope change corresponding to the marking level is determined as the edge segment.

5. The digital engineering management method based on BIM technology according to claim 1, characterized in that, The method involves filtering geometrically conflicting boundary regions from all candidate boundary regions based on the difference in slope variation factors between the boundaries of two material layers within the candidate boundary region. These regions include: Based on the absolute value of the difference between the slope variation factors corresponding to the two material layer boundaries within each candidate boundary area, the overlap evaluation index corresponding to each candidate boundary area is determined. If the overlap evaluation index corresponding to the candidate boundary region is less than the preset overlap threshold, the candidate boundary region will be determined as a geometric conflict boundary region.

6. The digital engineering management method based on BIM technology according to claim 1, characterized in that, The correction of the geometric conflict boundary region includes: Based on the overlap evaluation index corresponding to the geometric conflict boundary region, determine the error correction factor corresponding to the geometric conflict boundary region; Based on the error correction factor corresponding to the geometric conflict boundary region, the voxels within the geometric conflict boundary region are corrected, thereby achieving the correction of the geometric conflict boundary region.

7. A digital engineering management system based on BIM technology, characterized in that, The system is used to implement the digital engineering management method based on BIM technology according to any one of claims 1-6, and the system includes: The data acquisition and construction module is used to acquire point cloud data of cross-sections at different preset positions on the joint lines between different preset roads in the urban road space, and to construct point cloud models based on the acquired point cloud data. The downsampling module is used to downsample the point cloud data of each material layer at each preset location in the point cloud model using a voxel grid filter, so as to obtain the target voxel of each material layer at each preset location after downsampling. The thickness uniformity evaluation index determination module is used to determine the thickness uniformity evaluation index corresponding to each material layer at each preset position based on the thickness distribution of the target voxel of each material layer at each preset position. The screening and determination module is used to screen candidate material layers based on the thickness uniformity evaluation index, and to determine the boundary area between each candidate material layer and each of its adjacent material layers as the candidate boundary area; The slope change analysis and processing module is used to perform slope change analysis and processing on each material layer boundary in each candidate boundary area to obtain the slope change factor corresponding to each material layer boundary in each candidate boundary area. The region filtering module is used to filter out geometrically conflicting boundary regions from all candidate boundary regions based on the difference between the slope variation factors corresponding to the boundaries of two material layers within the candidate boundary region. The modified construction supervision module is used to correct geometric conflict boundary areas, thereby constructing a target parametric model, and realizing project supervision based on the target parametric model and the pre-constructed BIM theoretical model.

8. A digital engineering management device based on BIM technology, characterized in that, It includes a processor and a memory, the processor being used to process instructions stored in the memory to implement a digital engineering management method based on BIM technology according to any one of claims 1-6.

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