BIM (Building Information Modeling) data processing method for constructional engineering management

Abstract

The invention relates to the technical field of image data processing, in particular to a BIM (Building Information Modeling) data processing method for constructional engineering management, which comprises the following steps: constructing a point cloud model of a to-be-detected area in a building construction process, and obtaining a BIM model of the to-be-detected area; performing alignment processing on the point cloud model and the BIM model, and obtaining pairing information of each data point on the point cloud model and a theoretical point on the BIM model after alignment processing; dividing the to-be-detected area into a plurality of detection blocks, and determining a deviation threat evaluation value of the point cloud model in each detection block based on the pairing information; and determining a deviation block with a construction error in the plurality of detection blocks based on the deviation threat evaluation value, and outputting construction error prompt information about a construction position corresponding to the deviation block. According to the method, the technical problem that the detection result is inaccurate due to the fact that the point cloud data is interfered and sundries are easily misjudged as construction deviation when the point cloud data are matched with the BIM model can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and particularly to a BIM data processing method for construction project management. Background Art

[0002] Building Information Modeling (BIM) refers to integrating all aspects of a construction project (such as design, construction, operation, etc.) in a unified model in an informatized form through digital three-dimensional modeling technology, providing data support for the entire life cycle of the construction project, and playing a crucial role in construction project management.

[0003] During the construction process of a construction project, by scanning to obtain the three-dimensional point cloud data of the building at the construction site and comparing the actually measured three-dimensional point cloud data with the building design model in BIM, it can help construction workers promptly discover deviations during the construction process and avoid rework caused by errors. If it is caused by construction operation errors, the construction content with errors can be promptly corrected. During the construction process, by comparing and synchronizing the three-dimensional point cloud data with the BIM model data to check and detect possible construction errors, it can ensure the smooth progress of the construction project.

[0004] Point cloud data collection is usually carried out directly at the construction site. Since the site cannot be fully cleared in advance, tools and materials piled up on the site will generate noise interference, reducing the quality of the point cloud data. When these interfered point cloud data are matched with the BIM model, it is easy for the system to misjudge sundries such as materials and tools as deviations in construction content, resulting in inaccurate construction deviation detection results, affecting construction quality assessment and subsequent adjustment work. At the same time, it may also trigger unnecessary rectification operations due to incorrect judgments, further delaying the project schedule and increasing costs. Summary of the Invention

[0005] In order to solve the technical problem that when the interfered point cloud data is matched with the BIM model, it is easy for the system to misjudge sundries such as materials and tools as deviations in construction content, resulting in inaccurate construction deviation detection results, affecting construction quality assessment and subsequent adjustment work, and at the same time, it may also trigger unnecessary rectification operations due to incorrect judgments, further delaying the project schedule and increasing costs, the purpose of the present invention is to provide a BIM data processing method for construction project management, and the specific technical solution adopted is as follows: In the first aspect, the present invention provides a BIM data processing method for construction project management, and the method includes: Constructing a point cloud model of the area to be detected during the construction process of the building, and obtaining the BIM model of the area to be detected; Align the point cloud model and the BIM model, and after obtaining the alignment process, obtain the pairing information of each data point on the point cloud model and the theoretical point on the BIM model, where the pairing information is used to represent the corresponding relationship of each data point and the corresponding theoretical point in terms of spatial position; Divide the area to be detected into several detection blocks, and based on the pairing information, determine the deviation threat evaluation value of the point cloud model in each detection block, where the deviation threat evaluation value is used to measure the deviation degree of the point cloud model relative to the BIM model in this detection block; Based on the deviation threat evaluation value, determine the deviation blocks with construction errors among the several detection blocks, and output the construction error prompt information about the corresponding construction positions of the deviation blocks.

[0006] Optionally, the alignment process of the point cloud model and the BIM model includes: Obtain the first alignment feature points of the point cloud model and the second alignment feature points of the BIM model; Perform feature matching on the first alignment feature points and the second alignment feature points to obtain corresponding feature point pairs; Calculate the transformation matrix according to the feature point pairs; Based on the transformation matrix, perform alignment processing on the point cloud model and the BIM model.

[0007] Optionally, the obtaining of the first alignment feature points of the point cloud model and the second alignment feature points of the BIM model includes: Perform region extraction on the point cloud model to obtain several regions in the point cloud model; Based on the coordinate values of all data points in each region, extract the edge regions from the several regions; Determine the intersection points between any two of the edge regions as the first alignment feature points of the point cloud model; Based on the dimension data and attribute data of the BIM model, extract the wall edges of the BIM model, and determine the edge data points of the wall edges as the second alignment feature points of the BIM model.

[0008] Optionally, based on the pairing information, determining the deviation threat evaluation value of the point cloud model in each detection block includes: Based on the pairing information, determine the matching degree between the point cloud model and the BIM model in each detection block; Determine the region extraction information in each detection block, and based on the region extraction information, determine the density stability of the point cloud model in each detection block; Determine the deviation threat evaluation value of the point cloud model in each of the detection blocks according to the matching degree and the density stability.

[0009] Optionally, the pairing information between each data point on the point cloud model and the theoretical point on the BIM model includes: the target theoretical point corresponding to each data point, and the number of data points successfully paired with each theoretical point. Determine the matching degree between the point cloud model and the BIM model in each of the detection blocks based on the pairing information, including: Calculate the pairing coefficient of each theoretical point according to the number of data points successfully paired with each theoretical point. Calculate the corresponding distance between each data point and the target theoretical point in each of the detection blocks based on the pairing coefficient. Calculate the average corresponding distance of multiple data points in each of the detection blocks with respect to the multiple corresponding distances. Calculate the matching degree between the point cloud model and the BIM model in each of the detection blocks based on the average corresponding distance.

[0010] Optionally, determine the region extraction information in each of the detection blocks, and determine the density stability of the point cloud model in each of the detection blocks based on the region extraction information, including: Perform region extraction on each of the detection blocks to determine at least one region included in each of the detection blocks. Calculate the maximum region density and the minimum region density in the at least one region. Calculate the density stability of the point cloud model in each of the detection blocks based on the number of regions in the at least one region, the maximum region density, and the minimum region density.

[0011] Optionally, determine the deviation threat evaluation value of the point cloud model in each of the detection blocks according to the matching degree and the density stability, including: Calculate the first deviation evaluation value of the point cloud model in each of the detection blocks at the current detection stage based on the matching degree and the density stability. Obtain the second deviation evaluation value of the point cloud model in each of the detection blocks at the historical detection stage. Determine the deviation threat evaluation value of the point cloud model in each of the detection blocks based on the first deviation evaluation value and the second deviation evaluation value.

[0012] Optionally, determine the deviation threat evaluation value of the point cloud model in each of the detection blocks based on the first deviation evaluation value and the second deviation evaluation value, including: Based on the first deviation evaluation value and the second deviation evaluation value, calculate the deviation gain coefficient of the point cloud model in each of the detection blocks at the current detection stage; Adjust the first deviation evaluation value by using the deviation gain coefficient to obtain the deviation threat evaluation value of the point cloud model in each of the detection blocks.

[0013] Optionally, determining the deviation blocks with construction errors among the several detection blocks based on the deviation threat evaluation value includes: Determine the detection blocks corresponding to the deviation threat evaluation value greater than or equal to the threat warning threshold among the several detection blocks as the deviation blocks with construction errors.

[0014] Optionally, constructing the point cloud model of the area to be detected during the building construction process includes: During the building construction process, scan the area to be detected multiple times at different heights and different angles to obtain multiple groups of point cloud data; Perform data fusion on the multiple groups of point cloud data to obtain the point cloud model of the area to be detected.

[0015] The present invention has the following beneficial effects: The technical solution provided by the present invention, by constructing a point cloud model and obtaining a BIM model during the building construction process, without the need to comprehensively clean the construction site, can reduce the consumption of human and material resources and the impact on the construction period, and provide a reliable basis for subsequent analysis. By using a specific alignment algorithm to align the two types of models and obtain pairing information, it effectively overcomes the noise interference caused by sundries in the point cloud data and accurately establishes a spatial correspondence relationship. Divide the area to be detected into detection blocks, and determine the deviation threat evaluation value of each detection block based on the pairing information. This zoning processing method refines the analysis of the construction area, avoids overall misjudgment caused by sundries, and improves the detection accuracy. Finally, determine the deviation blocks according to the evaluation value and output accurate construction error prompt information, enabling construction personnel to accurately locate construction errors, avoid misjudgment, make targeted rectifications, improve the efficiency of construction quality control, and ensure the construction progress.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Other features and advantages of the present invention will be described in detail in the subsequent specific implementation section. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 A BIM data processing method provided for a building engineering management in an embodiment of the present invention; Figure 2 A BIM data processing method provided for a building engineering management in another embodiment of the present invention. Detailed implementation manners

[0019] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the drawings and preferred embodiments to detail a BIM data processing method provided for a building engineering management according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "an embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0021] The following specifically describes the specific solution of a BIM data processing method provided for a building engineering management according to the present invention with reference to the drawings.

[0022] Please refer to Figure 1 , which shows a method flowchart of a BIM data processing method provided for a building engineering management in an embodiment of the present invention. The method includes the following steps: Step 110: Construct a point cloud model of the area to be detected during the building construction process, and obtain the BIM model of the area to be detected.

[0023] Among them, the area to be detected is a specific building space range that needs to be carefully inspected and analyzed during the building construction process to ensure construction quality, control construction progress, or for other specific purposes, such as a certain area, a certain floor, or the entire project, etc.

[0024] In the quality control and progress monitoring of building construction, constructing a point cloud model and obtaining a Building Information Modeling (BIM) are key preliminary steps. When constructing a point cloud model, a portable laser scanner can be selected to complete this task. Its working principle is based on laser ranging technology. The scanner emits a laser beam. When the laser beam encounters the surface of an object, it will be reflected. The scanner receives the reflected signal and, based on the time difference between the emission and reception of the laser beam, combined with the constant speed of light, accurately calculates the distance between the scanner and each point on the object surface. At the same time, through the angle measurement device inside the instrument, the emission angle of the laser beam is determined, and then, using mathematical methods such as trigonometric functions, the coordinates of all scanned points on the object surface in three-dimensional space are calculated, that is, three-dimensional point cloud data. At the construction site, in order to comprehensively and accurately obtain the point cloud data of the area to be detected, a scientific and reasonable scanning strategy needs to be formulated. For example, according to the complexity of the building structure and the spatial layout of the area to be detected, in the vertical direction, it is preset to perform a horizontal scan at regular intervals; in the horizontal direction, the angle of the laser scanner is gradually adjusted at specific angular intervals for multiple scans. Scanning in this way from different heights and angles can ensure that the scanning range covers every corner of the area to be detected, obtaining comprehensive and complete point cloud data, which forms the basis of the point cloud model. Since the point cloud data obtained from each scan only represents the information at a specific height and angle, these data are scattered and independent in space. Therefore, the multi-group point cloud data can be further fused to integrate these multi-group discrete point cloud data into a complete, continuous, and point cloud model under a unified coordinate system, enabling it to accurately reflect the overall structure and shape of the area to be detected.

[0025] Correspondingly, for the embodiments of the present disclosure, when constructing the point cloud model of the area to be detected during the building construction process, the steps of the embodiments may include: during the building construction process, scanning the area to be detected multiple times at different heights and different angles to obtain multiple sets of point cloud data; performing data fusion on the multiple sets of point cloud data to obtain the point cloud model of the area to be detected. Among them, when performing data fusion on the multiple sets of point cloud data, an algorithm based on feature matching can be used to process the relationship between different scan data. This algorithm identifies feature points (such as points with unique geometric shapes or local features) in the overlapping areas of different scan data, and calculates the transformation relationship between these feature points, including rotation and translation parameters. For example, in two sets of overlapping point cloud data, pairs of feature points with similar local curvatures and neighborhood distributions are found, and by calculating the relative positions and angular changes between these point pairs, the transformation matrix required to transform one set of data to the same coordinate system as the other set of data is determined. Finally, according to the calculated transformation matrix, each part of the point cloud data is unified into the same coordinate system, and further processing and optimization are performed, such as weighted averaging of the data in the overlapping areas to eliminate the small differences caused by multiple scans, and finally a point cloud model that can truly reflect the actual situation of the area to be detected during the building construction process is constructed.

[0026] The BIM model is a digital integrated representation of the information related to a building engineering project. There are mainly two common ways to obtain the BIM model of the area to be detected. One is to directly export it from the professional design software (such as Revit, ArchiCAD, etc.) used in the building design stage. During the design stage, designers use these software to create BIM models containing detailed information such as building geometry, structural systems, building materials, and facilities and equipment. During the construction stage, the model part of the area to be detected can be extracted according to needs. The other is that if the construction unit has established its own BIM database during the project management process, the existing BIM model of the area to be detected can also be retrieved from this database. Such a database is usually updated in real time with the construction progress, which can ensure the consistency between the model and the actual construction situation. The BIM model not only contains the three-dimensional geometric shape information of the building, but also integrates a large amount of attribute information related to building components, such as the material, specification, model, and manufacturer of the components. During the building construction process, obtaining the BIM model of the area to be detected can provide various supports for construction management, quality inspection, etc. When used in combination with the point cloud model, the BIM model serves as a theoretical reference. By comparing the actual construction situation reflected by the point cloud model with the design information in the BIM model, it is possible to intuitively discover whether there are deviations during the construction process, providing a strong basis for taking timely rectification measures, helping to improve the construction quality, and ensuring the smooth progress of the project.

[0027] Step 120: Align the point cloud model and the BIM model, and obtain the pairing information of each data point on the point cloud model and the theoretical point on the BIM model after the alignment process. The pairing information is used to represent the corresponding relationship of each data point and the corresponding theoretical point in terms of spatial position.

[0028] The point cloud model is constructed by collecting actual data using a laser scanner at the construction site. It reflects the actual state of the building construction, including various construction deviations, interference from on-site debris, and other real situations. The BIM model, on the other hand, is a BIM model established based on design drawings and specifications, representing the ideal state that the building should achieve. Due to the different sources and natures of the two, there may be differences in their coordinate systems, scales, and model expression forms. Through the alignment process of the two, the actual situation presented by the point cloud model can be effectively compared with the design information in the BIM model, facilitating the accurate judgment of whether there are deviations during the construction process, as well as the specific location and degree of the deviations.

[0029] After the alignment of the point cloud model and the BIM model, the pairing information of each data point on the point cloud model and the theoretical point on the BIM model can be obtained. These pairing information details the corresponding relationship of each actual measurement point in the point cloud model with the theoretical design point in the BIM model in terms of spatial position. Through this corresponding relationship, the differences between the actual construction situation and the design requirements can be clearly understood.

[0030] Step 130: Divide the area to be detected into several detection blocks, and determine the deviation threat evaluation value of the point cloud model within each detection block based on the pairing information. The deviation threat evaluation value is used to measure the degree of deviation of the point cloud model relative to the BIM model within the detection block.

[0031] The area to be detected in a construction project is often large in scope and complex in structure. If the differences between the point cloud model and the BIM model are analyzed as a whole, it will result in a large amount of data and inaccurate analysis results. By dividing it into several detection blocks, the complex overall problem can be split into multiple relatively simple local problems for processing. This can not only reduce the complexity of the analysis but also more precisely focus on the construction situation of each local area, improving the accuracy and pertinence of the construction deviation detection.

[0032] In specific application scenarios, the basis for detecting block division can be determined by combining the characteristics of the building structure, functional zoning, and different construction techniques. For example, in the vertical direction, different detection blocks can be divided according to the natural floors of the building; in the horizontal direction, according to different functional areas, such as the business area, storage area, equipment room, etc. of a shopping mall, the detection blocks are divided; or according to the differences in construction techniques, the areas using different construction methods are divided. The division method can also utilize spatial division algorithms, such as grid-based division methods, to divide the area to be detected into cube or cuboid detection blocks with uniform sizes or different sizes set according to the actual situation in three-dimensional space. It can also be divided according to the boundaries of building components, and the range of the detection block is determined with the component as the unit. In this application, taking the example of dividing the area to be detected into cube detection blocks with uniform sizes (such as 1m×1m×1m) in three-dimensional space to illustrate the technical solution in this application, but it does not constitute a specific limitation.

[0033] To measure the deviation degree of the point cloud model in each detection block relative to the BIM model, it is necessary to calculate the deviation threat evaluation value. When calculating, various factors are comprehensively considered. First is the spatial distance deviation between the data points on the point cloud model and the corresponding points of the BIM model. The greater the distance deviation, the greater the possible deviation degree. For example, if the spatial distance between a point on the point cloud model and the corresponding point of the BIM model in a certain detection block is much larger than other points, this may mean that there are significant construction deviations in this area. Secondly, since normal building point clouds mainly come from elements such as building walls, internal structures, doors, and windows, the surface structures of these elements are regular and have clear geometric shapes, and the point cloud data collected by laser scanners is usually of high density and uniform distribution. For sundries at the construction site, which are usually building materials, tools, garbage, etc., their shapes are usually complex and their distributions may be scattered. Therefore, the density stability of each detection block can be obtained according to the distribution density of the data points in the detection block. Through a certain algorithm, these factors such as distance deviation and density stability are quantitatively and comprehensively calculated, and finally the deviation threat evaluation value of the point cloud model in each detection block is obtained. This deviation threat evaluation value can intuitively reflect the deviation degree between the actual construction situation and the design requirements in this detection block, providing a key basis for subsequent judgment of whether there are construction errors and the severity of the errors.

[0034] Step 140: Determine the deviation blocks with construction errors among several detection blocks based on the deviation threat evaluation value, and output the construction error prompt information regarding the corresponding construction positions of the deviation blocks.

[0035] To accurately determine which detection blocks have construction errors, it is necessary to preset a reasonable threshold value for the deviation threat evaluation value. The setting of this threshold is usually based on the design standards of construction projects, construction specifications, and empirical data from past similar projects. For example, for ordinary building structures, it may be stipulated that when the deviation threat evaluation value of a certain detection block exceeds 0.6, it is determined that the detection block has construction errors. This threshold is not fixed and will be adjusted according to different building types, functional requirements, and the complexity of construction techniques. After calculating the deviation threat evaluation value for each detection block, the evaluation values of each detection block are compared with the set threshold one by one. For those detection blocks with evaluation values greater than the threshold, they can be determined as deviation blocks with construction errors. For example, in a detection area containing 100 detection blocks, after calculation and comparison, it is found that the deviation threat evaluation values of 10 detection blocks exceed the threshold, then these 10 detection blocks are identified as deviation blocks. This screening method based on quantitative indicators can identify the problematic areas in construction more objectively and accurately compared to subjective judgment.

[0036] The output construction error prompt information contains multiple key elements. First is the specific location information of the deviation block in the building, which can be clarified through the building's coordinate system, floor number, room number, or specific area identifier. For example, the deviation block is located on the 3rd floor of the building, with a coordinate range from (X1, Y1, Z1) to (X2, Y2, Z2), belonging to a specific corner of the commercial business area. Secondly, it can also include the general direction of the construction error, such as whether the deviation of the wall is offset to the left or tilted upward. In addition, the degree of the error will also be mentioned, such as the verticality error of a certain column or the flatness error of a certain wall. By outputting the construction error prompt information, construction personnel can quickly locate the specific location with construction errors according to the prompt information, understand the direction and degree of the error, and thus formulate a targeted rectification plan.

[0037] In summary, according to the BIM data processing method for construction project management provided by the present invention, by constructing a point cloud model and obtaining a BIM model during the building construction process, it is not necessary to comprehensively clean the construction site, which can reduce the consumption of manpower and material resources and the impact on the construction period, providing a reliable basis for subsequent analysis. By using a specific alignment algorithm to align the two types of models and obtain pairing information, it effectively overcomes the noise interference caused by sundries in the point cloud data and accurately establishes a spatial correspondence relationship. The area to be detected is divided into detection blocks, and the deviation threat evaluation value of each detection block is determined based on the pairing information. This zoning processing method refines the analysis of the construction area, avoids overall misjudgment caused by sundries, and improves the detection accuracy. Finally, based on the evaluation value, the deviation blocks are determined and accurate construction error prompt information is output, enabling construction personnel to accurately locate the construction errors, avoid misjudgment, rectify them in a targeted manner, improve the efficiency of construction quality control, and ensure the construction progress.

[0038] Based on Figure 1 the embodiments shown, as a refinement and extension of the above embodiments, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides a specific method as shown in Figure 2 the following figure. Figure 2 Based on Figure 1 the embodiments shown. As shown in Figure 2 the following figure, the method includes the following steps: Step 210: Construct a point cloud model of the area to be detected during the building construction process, and obtain the BIM model of the area to be detected.

[0039] For the embodiments of the present disclosure, the specific implementation process can refer to the relevant descriptions in step 110 of the embodiments, which will not be elaborated here.

[0040] Step 220: Align the point cloud model and the BIM model, and obtain the pairing information of each data point on the point cloud model and the theoretical points on the BIM model after the alignment process.

[0041] For the embodiments of the present disclosure, the alignment process of the point cloud model and the BIM model in step 220 may include the following steps: Step 220-1: Obtain the first alignment feature points of the point cloud model and the second alignment feature points of the BIM model.

[0042] For the embodiments of the present disclosure, the point cloud model can be first subjected to region extraction. Region extraction is a classification in point cloud segmentation. Point cloud segmentation refers to dividing the point cloud according to the spatial, geometric, and texture features of the point cloud, so that the point cloud within the same division has similar features, and region extraction is based on curvature and normal vectors for segmentation. By region extraction, several regions in the point cloud model are obtained, and the average value of the coordinates of all points in each region is obtained, which is recorded as the region center of each region. The regions corresponding to the region centers that take the maximum and minimum values in the X, Y, and Z directions respectively are recorded as the edge regions, and the edge regions correspond to the walls corresponding to the boundaries of the area to be inspected. Furthermore, the intersection points between every two edge regions in the point cloud model can be obtained, which are recorded as the first alignment feature points of the point cloud model; the BIM model records the dimension data and attribute data of each part of the building project, etc., so the second alignment feature points corresponding to the edge walls of the area to be inspected can be directly obtained.

[0043] Correspondingly, for the embodiments of the present disclosure, the embodiment steps may include: performing region extraction on the point cloud model to obtain several regions in the point cloud model; extracting edge regions from the several regions based on the coordinate values of all data points in each region; determining the intersection points between any two edge regions as the first alignment feature points of the point cloud model; extracting the wall edges of the BIM model based on the dimension data and attribute data of the BIM model, and determining the edge data points of the wall edges as the second alignment feature points of the BIM model.

[0044] Step 220-2: Perform feature matching on the first alignment feature points and the second alignment feature points to obtain corresponding feature point pairs.

[0045] The core objective of feature matching is to find point pairs with similar geometric features and spatial position relationships between the first alignment feature points of the point cloud model and the second alignment feature points of the BIM model. The commonly used matching method is based on the matching of feature descriptors. For example, feature descriptors can be generated for each first alignment feature point and second alignment feature point first. This descriptor is a quantitative expression of the local geometric attributes of the feature points. For example, descriptors based on normal vectors and curvatures will encode information such as the normal vector distribution and curvature change within a certain neighborhood around the feature points. Then, by calculating the similarity (such as Euclidean distance, cosine similarity, etc.) between the descriptors of different feature points to determine whether they match. When the similarity of the descriptors of two feature points exceeds a pre-set threshold, it is considered that these two feature points form a corresponding feature point pair.

[0046] Step 220-3: Calculate the transformation matrix according to the feature point pairs.

[0047] The transformation matrix is used to describe the transformation relationships such as rotation, translation, and scaling of the point cloud model relative to the BIM model in space. Through the calculated transformation matrix, the points in the point cloud model can be accurately transformed to the coordinate system consistent with the BIM model, realizing the alignment of the two.

[0048] Step 220-4: Perform alignment processing on the point cloud model and the BIM model based on the transformation matrix.

[0049] After obtaining the transformation matrix, it can be applied to each point in the point cloud model. For any point in the point cloud model, its position and attitude can be transformed using the transformation matrix. In this way, all points in the point cloud model are transformed according to the rotation and translation relationships described by the transformation matrix, causing the position and attitude of the point cloud model in space to change and gradually approaching the coordinate system of the BIM model. The point cloud model and the BIM model are aligned in terms of spatial position and attitude. At this time, the differences between the two can be compared more intuitively and accurately, providing strong support for subsequent construction deviation detection, quality assessment, and other work.

[0050] Step 230: Divide the area to be detected into several detection blocks, and determine the matching degree between the point cloud model and the BIM model within each detection block based on the pairing information.

[0051] Among them, the pairing information between each data point on the point cloud model and the theoretical point on the BIM model includes: the target theoretical point corresponding to each data point, and the number of data points successfully paired with each theoretical point.

[0052] For the embodiments of the present disclosure, determining the matching degree between the point cloud model and the BIM model within each detection block based on the pairing information in step 230 may include the following steps: Step 230-1: Calculate the pairing coefficient of each theoretical point according to the number of data points successfully paired with each theoretical point.

[0053] For the part where there is no construction error or no noise interference, the point cloud model and the BIM model are basically coincident. When there are engineering errors or sundries and other noises in the collected or point cloud model, the Euclidean distance between the data points of the engineering errors or sundries noises and the corresponding points of the BIM model is relatively large. The parts of the model that may have engineering errors are obtained through the corresponding deviation between each data point and the theoretical point in the point cloud model and the Euclidean distance between the corresponding points.

[0054] Obtain the Euclidean distance between each data point and each theoretical point, and take the theoretical point with the smallest Euclidean distance from each data point as the corresponding point of each data point. Ideally, the theoretical points and data points correspond one by one. When there are deviations in the shape or size between the point cloud model and the BIM model, there may be a situation where one theoretical point corresponds to multiple or 0 data points. The corresponding situation between each theoretical point and the data points is reflected by the number of data points corresponding to each theoretical point, which is recorded as the pairing coefficient of each theoretical point. The pairing coefficient of the q-th theoretical point is calculated as follows: In the formula, is the pairing coefficient of the q-th theoretical point; represents the number of data points corresponding to the q-th theoretical point; represents the linear normalization function; represents taking the absolute value; represents the absolute value of the difference between the number of data points corresponding to the q-th theoretical point and 1. Ideally, the theoretical points and data points correspond one by one. Therefore, the larger this value is, the greater the deviation of the corresponding situation of the points.

[0055] Step 230-2: Calculate the corresponding distance between each data point and the target theoretical point within each detection block based on the pairing coefficient.

[0056] For the embodiments of the present disclosure, the corresponding distance of each data point can be obtained in combination with the pairing coefficient. The corresponding distance between the p-th data point and the target theoretical point is calculated as follows: In the formula, is the corresponding distance between the p-th data point and the target theoretical point; represents the linear normalization function; represents the pairing coefficient of the target theoretical point corresponding to the p-th data point; represents the Euclidean distance between the p-th data point and its corresponding target theoretical point.

[0057] Step 230-3: Calculate the average corresponding distance of multiple data points in each detection block with respect to multiple corresponding distances.

[0058] Step 230-4: Calculate the matching degree between the point cloud model and the BIM model in each detection block based on the average corresponding distance.

[0059] The matching degree of each detection block is obtained according to the average corresponding distance of all data points in each detection block. The matching degree of each block reflects the conformity between the point cloud model and the BIM model in this detection block. The more the point cloud model fits the BIM model, the higher the coincidence degree of all data points in the block with the corresponding theoretical points, the smaller the corresponding distance, and the greater the matching degree. The matching degree of the k-th detection block is calculated as follows: In the formula, is the matching degree of the k-th detection block; represents the total number of all data points in the k-th detection block; represents the corresponding distance of the m-th data point in the k-th detection block; represents the inverse proportional normalization with the exponential function with the natural constant as the base; is the average corresponding distance of multiple data points in the k-th detection block with respect to multiple corresponding distances.

[0060] Step 240: Determine the region extraction information in each detection block, and determine the density stability of the point cloud model in each detection block based on the region extraction information.

[0061] The matching degree is used to obtain the degree of conformity between the point cloud model and the BIM model within each block according to the Euclidean distance between the data points and the theoretical points and the corresponding situation. However, just by calculating the matching degree, it is still impossible to determine the existence of construction errors. If the matching degree of a detection block is small, it may be a deviation caused by operational errors during the construction process, or it may be a deviation caused by noise information generated by sundries piled up at the construction site, resulting in the presence of sundries in the point cloud model. Therefore, the density stability of each block can also be analyzed. The density stability refers to the uniformity of the point cloud data density and the stability of its change with the spatial position within the detection block. A stable point cloud density means that within the detection block, the distribution of the point cloud data is relatively uniform, without obvious sudden changes in density. This usually indicates that the data acquisition process is relatively stable, and the reflected building structure or construction situation is relatively regular. On the contrary, if the point cloud density is unstable, with uneven or even sudden changes in density, it may imply that the data acquisition is interfered (such as the presence of sundries at the construction site, such as building materials, tools, garbage, etc.).

[0062] For the embodiments of the present disclosure, determining the region extraction information within each detection block in step 240 and determining the density stability of the point cloud model within each detection block based on the region extraction information may include the following steps: Step 240-1: Perform region extraction on each detection block to determine at least one region contained within each detection block.

[0063] After the detection areas in building construction are divided into detection blocks, each detection block may contain various building components or areas in different construction states. Through region extraction, the complex point cloud data within the detection block can be segmented into relatively independent and meaningful sub-regions according to different geometric shapes, object boundaries, or other features. After the region extraction operation, different region division results will be presented within each detection block. These regions may be simple planar regions, such as building walls and floors; or complex three-dimensional structure regions, such as stairs and complex building decoration shapes.

[0064] Step 240-2: Calculate the maximum region density and the minimum region density in at least one region.

[0065] After calculating the point cloud density of each region within at least one region, find the regions with the maximum and minimum density values, and the corresponding density values are the maximum region density and the minimum region density.

[0066] Step 240-3: Calculate the density stability of the point cloud model within each detection block based on the number of regions, the maximum region density, and the minimum region density in at least one region.

[0067] For the embodiments of the present disclosure, the number of regions, the maximum region density, and the minimum region density of at least one region can be substituted into the density stability calculation formula, and the density stability of the point cloud model within each detection block can be calculated. The formula features of the density stability calculation formula are described as follows: In the formula, is the density stability of the point cloud model within the k-th detection block; represents the number of regions in the k-th detection block, represents the maximum region density in the k-th detection block; represents the minimum region density in the k-th detection block; reflects the range of the region density within the k-th detection block. The larger this value is, the more likely it is that there is point cloud corresponding to sundries within the k-th detection block. Dividing by is to make the overall value of this between 0 and 1, which is used as the weight of.

[0068] Step 250: Determine the deviation threat evaluation value of the point cloud model within each detection block according to the matching degree and the density stability.

[0069] In this step, by comparing the differences between the point cloud model and the BIM model and the density distribution of the point cloud model, the situations of possible construction deviations and sundry noises are distinguished, and the deviation evaluation of each detection block is obtained. The deviation evaluation reflects the probability of possible construction deviations in each detection block.

[0070] For the embodiments of the present disclosure, determining the deviation threat evaluation value of the point cloud model within each detection block according to the matching degree and the density stability in step 250 may include the following steps: Step 250-1: Calculate the first deviation evaluation value of the point cloud model within each detection block at the current detection stage based on the matching degree and the density stability.

[0071] For the embodiments of the present disclosure, the matching degree and the density stability of the point cloud model within each detection block can be substituted into the deviation evaluation value calculation formula, and the first deviation evaluation value of the point cloud model within each detection block at the current detection stage can be obtained. The formula features of the deviation evaluation value calculation formula are described as follows: In the formula, is the first deviation evaluation value of the point cloud model within the k-th detection block at the current detection stage; represents the linear normalization function; is the matching degree of the k-th detection block; is the density stability of the point cloud model within the k-th detection block.

[0072] Step 250-2: Obtain the second deviation evaluation value of the point cloud model within each detection block during the historical detection stage.

[0073] During the entire construction cycle of a construction project, it is necessary to conduct inspections for construction errors at multiple stages to ensure the correct progress of construction at multiple stages. The construction errors screened and obtained at different stages may vary. In addition to the inspection in the current detection stage, when conducting inspections for construction errors, the deviation evaluation (i.e., the second deviation evaluation value) of the positions that have been inspected in the previous stage is also used as a reference for this inspection to avoid the situation where the accumulation of construction errors in multiple stages is not discovered in time, that is, the deviation evaluation obtained during the inspection in the previous stage is not large, but as the construction progresses, the deviation shows a gradually increasing trend.

[0074] For the embodiments of the present disclosure, according to the calculation method of the deviation evaluation in the foregoing steps, the second deviation evaluation value of each detection block in all detection stages before the current detection stage can be obtained.

[0075] Step 250-3: Based on the first deviation evaluation value and the second deviation evaluation value, determine the deviation threat evaluation value of the point cloud model within each detection block.

[0076] For the embodiments of the present disclosure, the embodiment steps may include: calculating the deviation gain coefficient of the point cloud model within each detection block in the current detection stage based on the first deviation evaluation value and the second deviation evaluation value; using the deviation gain coefficient to adjust the first deviation evaluation value to obtain the deviation threat evaluation value of the point cloud model within each detection block.

[0077] During different detection stages, the deviation of the same detection block may change. For example, the block with a large deviation evaluation in the previous detection stage may have been corrected, and the deviation evaluation decreases; or it may not have been corrected and the deviation still exists, and the deviation evaluation remains unchanged; or even due to the unstable building structure at the position with a large deviation evaluation in the previous stage, the deviation situation deteriorates during the construction process.

[0078] Taking the inspection in the T-th detection stage as an example, the deviation gain coefficient of the k-th detection block in the T-th detection stage is calculated as follows: In the formula, is the deviation gain coefficient of the k-th detection block in the T-th detection stage. The larger the deviation gain coefficient, the more likely it is that the k-th detection block has a situation of deviation deterioration; ReLU represents the ReLU function, and the ReLU function is expressed as , that is, when the input is less than or equal to 0, the function value is 0. When the input is greater than 0, the function value is equal to the input value. Here, the ReLU function is used to avoid the negative value of the corrected deviation affecting the subsequent calculation results; represents the first deviation evaluation value of the point cloud model in the k-th detection block of the T-th detection stage in the current detection stage; represents the second deviation evaluation value of the point cloud model in the k-th detection block of the T-th detection stage in the historical detection stage.

[0079] With the accumulation of deviation evaluations, it may lead to greater difficulty in correcting construction errors or pose potential hazards to project safety. Therefore, the deviation evaluation value is adjusted in combination with the deviation changes of each detection block. Through all the previous detection stages of the T-th detection stage, the deviation gain coefficient of each detection block is combined with the first deviation evaluation value of the current detection stage to obtain the deviation threat evaluation of each detection block. The deviation threat evaluation represents the result of amplifying the deviation evaluation by combining the deviation gain coefficient, reflecting the threat degree of deviation deterioration of each detection block.

[0080] The calculation method of the deviation threat evaluation of the k-th detection block in the T-th detection stage is: In the formula, represents the deviation threat evaluation of the k-th detection block in the T-th detection stage; represents the first deviation evaluation value of the k-th detection block in the T-th detection stage; is the product symbol, indicating that the subsequent expression is to be multiplied continuously; represents the deviation gain coefficient of the k-th detection block in the t-th detection stage. When T = 1, take . (There is a normalization process in the deviation evaluation calculation steps. Here, it is to amplify abnormal situations and keep non-abnormal situations unchanged, which does not affect the subsequent threshold judgment and can be without normalization).

[0081] Compared with the deviation evaluation, the deviation threat evaluation further adjusts the first deviation evaluation value of the current detection stage according to the deviation threat change trend of each detection block, and can more timely detect potential deviation hazards that may deteriorate.

[0082] Step 260: Determine the detection blocks corresponding to the deviation threat evaluation values greater than or equal to the threat warning threshold among several detection blocks as the deviation blocks with construction errors, and output the construction error prompt information about the corresponding construction positions of the deviation blocks.

[0083] Among them, the threat warning threshold is a preset standard value, which is determined based on the design standards of construction projects, construction specifications, and empirical data of past similar projects. For different types of construction projects, due to different design precision requirements and construction process complexities, the threat warning thresholds will also vary. For example, for a precision laboratory building with extremely high spatial precision requirements, the threat warning threshold will be set relatively low to strictly control the construction quality; while for some ordinary civil buildings, the threshold may be relatively loose, but it is also necessary to ensure that the basic construction quality standards are met.

[0084] For the embodiments of the present disclosure, after calculating the deviation threat evaluation values of several detection blocks respectively, the evaluation value of each detection block is compared with the threat warning threshold one by one. For those detection blocks whose deviation threat evaluation values are greater than or equal to the threat warning threshold, they are determined as deviation blocks with construction errors. For example, in a detection area containing 100 detection blocks, after calculation and comparison, it is found that the deviation threat evaluation values of 15 detection blocks reach or exceed the threshold, then these 15 detection blocks are identified as deviation blocks. This screening method based on quantitative indicators can more objectively and accurately identify the areas with problems in construction compared with subjective judgment.

[0085] In summary, the technical solution in the present application, by constructing a point cloud model and obtaining a BIM model during the construction process, does not require a full clean-up of the construction site, can reduce the consumption of manpower and material resources and the impact on the construction period, and provides a reliable basis for subsequent analysis. By using a specific alignment algorithm to align the two types of models and obtain pairing information, it effectively overcomes the noise interference caused by sundries in the point cloud data and accurately establishes the spatial correspondence relationship. The detection area to be detected is divided into detection blocks, and the deviation threat evaluation value of each detection block is determined based on the pairing information. This zoning processing method refines the analysis of the construction area, avoids overall misjudgment caused by sundries, and improves the detection accuracy. Finally, deviation blocks are determined based on the evaluation value and accurate construction error prompt information is output, enabling construction personnel to accurately locate construction errors, avoid misjudgment, make targeted rectifications, improve the efficiency of construction quality control, and ensure the construction progress.

[0086] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0088] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A BIM data processing method for construction project management, characterized in that: The method comprises: Constructing a point cloud model of the area to be inspected during the construction process, and obtaining a BIM model of the area to be inspected; Performing an alignment process on the point cloud model and the BIM model, and obtaining pairing information of each data point on the point cloud model and a theoretical point on the BIM model after the alignment process, wherein the pairing information is used to indicate a corresponding relationship between each data point and a corresponding theoretical point in spatial position; Divide the area to be detected into a number of detection blocks, and determine a deviation threat evaluation value of the point cloud model in each detection block based on the pairing information, wherein the deviation threat evaluation value is used to measure the degree of deviation of the point cloud model in the detection block relative to the BIM model; A deviation block with a construction error is determined among the plurality of detection blocks based on the deviation threat evaluation value, and construction error prompt information about a construction position corresponding to the deviation block is output.

2. The BIM data processing method for construction project management according to claim 1, characterized in that: Performing an alignment process on the point cloud model and the BIM model includes: Acquire a first alignment feature point of the point cloud model and a second alignment feature point of the BIM model; Performing feature matching on the first alignment feature point and the second alignment feature point to obtain a corresponding feature point pair; Calculate a transformation matrix based on the feature point pairs; The point cloud model and the BIM model are aligned based on the transformation matrix.

3. The BIM data processing method for construction project management according to claim 2, characterized in that: The obtaining of the first alignment feature point of the point cloud model and the second alignment feature point of the BIM model includes: Performing region extraction on the point cloud model to obtain a plurality of regions in the point cloud model; Extracting edge regions from the plurality of regions based on coordinate values ​​of all data points in each of the regions; Determine an intersection point between any two edge regions as a first alignment feature point of the point cloud model; Based on the size data and attribute data of the BIM model, the wall edge of the BIM model is extracted, and the edge data points of the wall edge are determined as the second alignment feature points of the BIM model.

4. The BIM data processing method for construction project management according to claim 1, characterized in that: Determining the deviation threat evaluation value of the point cloud model in each detection block based on the pairing information includes: Determine the matching degree between the point cloud model and the BIM model in each detection block based on the pairing information; Determine region extraction information within each of the detection blocks, and determine density stability of the point cloud model within each of the detection blocks based on the region extraction information; A deviation threat evaluation value of the point cloud model in each detection block is determined according to the matching degree and the density stability.

5. The BIM data processing method for construction project management according to claim 4, characterized in that: The pairing information of each data point on the point cloud model and the theoretical point on the BIM model includes: the target theoretical point that each data point corresponds to, and the number of successfully paired data points corresponding to each theoretical point; Determining the degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information includes: Calculate the pairing coefficient of each theoretical point according to the number of data points that are successfully paired with each theoretical point; Calculate the corresponding distance between each data point in each detection block and the target theoretical point based on the pairing coefficient; Calculate the average corresponding distance of a plurality of data points in each of the detection blocks with respect to a plurality of the corresponding distances; Based on the average corresponding distance, the matching degree between the point cloud model and the BIM model in each detection block is calculated.

6. The BIM data processing method for construction project management according to claim 4, characterized in that: Determining region extraction information within each of the detection blocks, and determining density stability of the point cloud model within each of the detection blocks based on the region extraction information, including: Performing region extraction on each of the detection blocks to determine at least one region contained in each of the detection blocks; Calculate the maximum value and the minimum value of regional density in the at least one region; Based on the number of regions of the at least one region, the maximum region density and the minimum region density, the density stability of the point cloud model in each detection block is calculated.

7. The BIM data processing method for construction project management according to claim 4, characterized in that: Determining the deviation threat evaluation value of the point cloud model in each detection block according to the matching degree and the density stability includes: Based on the matching degree and the density stability, calculating a first deviation evaluation value of the point cloud model in each of the detection blocks at a current detection stage; Obtaining a second deviation evaluation value of the point cloud model in each detection block in a historical detection phase; Based on the first deviation evaluation value and the second deviation evaluation value, a deviation threat evaluation value of the point cloud model in each detection block is determined.

8. The BIM data processing method for construction project management according to claim 7, characterized in that: Determining a deviation threat evaluation value of the point cloud model in each of the detection blocks based on the first deviation evaluation value and the second deviation evaluation value includes: Calculating a deviation gain coefficient of the point cloud model in each detection block at the current detection stage based on the first deviation evaluation value and the second deviation evaluation value; The first deviation evaluation value is adjusted by using the deviation gain coefficient to obtain a deviation threat evaluation value of the point cloud model in each detection block.

9. The BIM data processing method for construction project management according to claim 1, characterized in that: Determining a deviation block having a construction error among the plurality of detection blocks based on the deviation threat evaluation value includes: The detection blocks among the plurality of detection blocks, whose corresponding deviation threat evaluation values ​​are greater than or equal to the threat warning threshold, are determined as deviation blocks with construction errors.

10. The BIM data processing method for construction project management according to claim 1, characterized in that: The step of constructing a point cloud model of the area to be inspected during the construction process includes: During the construction process, the inspection area is scanned multiple times at different heights and angles to obtain multiple sets of point cloud data; The multiple groups of point cloud data are fused to obtain a point cloud model of the area to be detected.

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