A BIM data processing method for construction project management

Abstract

The present invention relates to the field of image data processing technology, and specifically to a BIM data processing method for construction project management, comprising: constructing a point cloud model of an area to be inspected during construction, and obtaining a BIM model of the area to be inspected; aligning 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; dividing the area to be inspected into a number of inspection blocks, and determining a deviation threat evaluation value of the point cloud model in each inspection block based on the pairing information; determining a deviation block with construction errors in the number of inspection blocks based on the deviation threat evaluation value, and outputting construction error prompt information about the construction position corresponding to the deviation block. This application can solve the technical problem that point cloud data is interfered with and is easily misjudged as construction deviation when matching with the BIM model, resulting in inaccurate inspection results.

Description

Technical Field

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

[0002] Building Information Modeling (BIM) refers to the use of digital three-dimensional modeling technology to integrate various aspects of a construction project (such as design, construction, and operation) into a unified model in an information-based manner. It provides data support for the entire life cycle of a construction project and plays a vital role in construction project management.

[0003] During the construction process, scanning and acquiring 3D point cloud data of buildings on the construction site and comparing the measured 3D point cloud data with the architectural design model in BIM can help construction personnel promptly identify deviations during the construction process and avoid rework due to errors. If the cause is a construction operation error, the construction content in question can be corrected promptly. By comparing and synchronizing 3D point cloud data with BIM model data during the construction process, possible construction errors can be identified and verified, ensuring the smooth progress of the construction project.

[0004] Point cloud data collection is typically conducted directly at the construction site. Because the site cannot be fully cleaned beforehand, tools and materials piled onsite can generate noise interference, reducing point cloud data quality. When matching this disturbed point cloud data with the BIM model, the system can easily misinterpret these debris as construction deviations. This leads to inaccurate deviation detection results, impacting construction quality assessments and subsequent adjustments. Misjudgments can also lead to unnecessary rectification operations, further delaying construction schedules and increasing costs. Summary of the Invention

[0005] In order to solve the technical problem that when the disturbed point cloud data is matched with the BIM model, the system may easily misjudge materials and tools as deviations in the construction content, resulting in inaccurate construction deviation detection results, affecting the construction quality assessment and subsequent adjustment work, and may also cause unnecessary rectification operations due to erroneous judgment, further delaying the construction period and increasing costs, the purpose of the present invention is to provide a BIM data processing method for construction project management. The technical solutions adopted are as follows:

[0006] In a first aspect, the present invention provides a BIM data processing method for construction project management, the method comprising:

[0007] 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;

[0008] 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 the alignment processing, wherein the pairing information is used to represent a corresponding relationship between each data point and a corresponding theoretical point in spatial position;

[0009] Divide the area to be inspected into a plurality of inspection blocks, and determine a deviation threat evaluation value of the point cloud model in each inspection 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 relative to the BIM model in the inspection block;

[0010] 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.

[0011] Optionally, aligning the point cloud model with the BIM model includes:

[0012] Acquire a first alignment feature point of the point cloud model and a second alignment feature point of the BIM model;

[0013] Performing feature matching on the first alignment feature points and the second alignment feature points to obtain corresponding feature point pairs;

[0014] Calculating a transformation matrix based on the feature point pairs;

[0015] The point cloud model and the BIM model are aligned based on the transformation matrix.

[0016] Optionally, obtaining the first alignment feature point of the point cloud model and the second alignment feature point of the BIM model includes:

[0017] Performing region extraction on the point cloud model to obtain a plurality of regions in the point cloud model;

[0018] extracting edge regions from the plurality of regions based on coordinate values ​​of all data points in each of the regions;

[0019] Determine an intersection point between any two edge regions as a first alignment feature point of the point cloud model;

[0020] Based on the size data and attribute data of the BIM model, the wall edges of the BIM model are extracted, and the edge data points of the wall edges are determined as second alignment feature points of the BIM model.

[0021] Optionally, determining a deviation threat assessment value of the point cloud model in each detection block based on the pairing information includes:

[0022] Determining a degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information;

[0023] 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;

[0024] Determine a deviation threat evaluation value of the point cloud model in each detection block according to the matching degree and the density stability.

[0025] Optionally, 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 data points that are successfully paired with each theoretical point;

[0026] Determining a degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information includes:

[0027] Calculating the pairing coefficient of each theoretical point according to the number of successfully paired data points corresponding to each theoretical point;

[0028] Calculating the corresponding distance between each data point in each detection block and the target theoretical point based on the pairing coefficient;

[0029] Calculating an average corresponding distance of a plurality of data points in each detection block with respect to a plurality of corresponding distances;

[0030] Based on the average corresponding distance, the matching degree between the point cloud model and the BIM model in each detection block is calculated.

[0031] Optionally, determining region extraction information within each detection block, and determining density stability of the point cloud model within each detection block based on the region extraction information includes:

[0032] Performing region extraction on each of the detection blocks to determine at least one region contained in each of the detection blocks;

[0033] Calculate the maximum regional density and the minimum regional density in the at least one region;

[0034] Based on the number of regions of the at least one region, the maximum region density, and the minimum region density, density stability of the point cloud model in each detection block is calculated.

[0035] Optionally, determining a deviation threat evaluation value of the point cloud model in each detection block according to the matching degree and the density stability includes:

[0036] Calculating a first deviation evaluation value of the point cloud model in each detection block at a current detection stage based on the matching degree and the density stability;

[0037] Obtaining a second deviation evaluation value of the point cloud model in each detection block during a historical detection phase;

[0038] A deviation threat evaluation value of the point cloud model in each detection block is determined based on the first deviation evaluation value and the second deviation evaluation value.

[0039] Optionally, determining a deviation threat evaluation value of the point cloud model in each detection block based on the first deviation evaluation value and the second deviation evaluation value includes:

[0040] 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;

[0041] The first deviation evaluation value is adjusted using the deviation gain coefficient to obtain a deviation threat evaluation value of the point cloud model in each detection block.

[0042] Optionally, determining a deviation block having a construction error among the plurality of detection blocks based on the deviation threat evaluation value includes:

[0043] 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.

[0044] Optionally, constructing a point cloud model of the area to be inspected during the building construction process includes:

[0045] During the construction process, the inspection area is scanned multiple times at different heights and angles to obtain multiple sets of point cloud data;

[0046] The multiple sets of point cloud data are fused to obtain a point cloud model of the area to be detected.

[0047] The present invention has the following beneficial effects: The technical solution provided by the present invention can reduce the consumption of manpower and material resources and the impact on the construction period by constructing a point cloud model and obtaining a BIM model during the construction process, without the need to comprehensively clean the construction site, and provide a reliable basis for subsequent analysis. The two types of models are aligned and pairing information is obtained through a specific alignment algorithm, which effectively overcomes the noise interference caused by debris in the point cloud data and accurately establishes a spatial correspondence. 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 partitioning processing method refines the analysis of the construction area, avoids overall misjudgment due to debris, and improves detection accuracy. Finally, the deviation block is determined based on the evaluation value and accurate construction error prompt information is output, so that construction personnel can accurately locate construction errors, avoid misjudgment, make targeted rectifications, improve construction quality control efficiency, and ensure construction progress.

[0048] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory and cannot limit the present invention. Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A BIM data processing method for construction project management provided by one embodiment of the present invention;

[0051] Figure 2 Another embodiment of the present invention provides a BIM data processing method for construction project management. DETAILED DESCRIPTION

[0052] To further illustrate the technical means and effectiveness of the present invention to achieve its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a BIM data processing method for construction project management, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0053] Unless defined otherwise, 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 belongs.

[0054] The following describes in detail a specific solution of a BIM data processing method for construction project management provided by the present invention in conjunction with the accompanying drawings.

[0055] See also Figure 1 , which shows a method flow chart of a BIM data processing method for construction project management provided by an embodiment of the present invention, the method comprising the following steps:

[0056] Step 110: construct a point cloud model of the area to be inspected during the construction process, and obtain a BIM model of the area to be inspected.

[0057] Among them, the area to be inspected is a specific building space range that needs to be inspected and analyzed in detail during the 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.

[0058] Constructing a point cloud model and obtaining a Building Information Model (BIM) are critical preliminary steps in quality control and progress monitoring for construction projects. A portable laser scanner can be used to construct this point cloud model. Its operating principle is based on laser ranging technology. The scanner emits a laser beam, which is reflected when it hits an object's surface. The scanner receives the reflected signal and, based on the time difference between the laser beam's emission and reception and the constant speed of light, accurately calculates the distance between the scanner and each point on the object's surface. Simultaneously, an internal angle measurement device determines the laser beam's emission angle. Using mathematical methods such as trigonometric functions, the coordinates of all scanned points on the object's surface in three dimensions are calculated, creating the three-dimensional point cloud data. At the construction site, a scientific and rational scanning strategy is necessary to comprehensively and accurately acquire point cloud data for the area to be inspected. For example, based on the complexity and spatial layout of the building structure in the area to be inspected, horizontal scans can be pre-set at predetermined intervals in the vertical direction. Horizontally, the laser scanner's angle can be gradually adjusted at specific angles to perform multiple scans. This all-around scanning from different heights and angles ensures that every corner of the inspection area is covered, acquiring comprehensive and complete point cloud data, which forms the basis of the point cloud model. Because the point cloud data acquired from each scan only represents information at a specific height and angle, this data is spatially dispersed and independent. Therefore, multiple sets of point cloud data can be further fused, integrating these discrete sets of point cloud data into a complete, continuous point cloud model in a unified coordinate system, which accurately reflects the overall structure and shape of the inspection area.

[0059] Accordingly, for the embodiments of the present disclosure, when constructing a point cloud model of the area to be inspected during the construction process, the embodiment steps may include: during the construction process, scanning the area to be inspected multiple times at different heights and angles to obtain multiple sets of point cloud data; fusing the multiple sets of point cloud data to obtain a point cloud model of the area to be inspected. When fusing the multiple sets of point cloud data, an algorithm based on feature matching can be used to process the relationship between different scan data. The algorithm calculates the transformation relationship between these feature points, including rotation and translation parameters, by identifying feature points (such as points with unique geometric shapes or local features) in the overlapping areas of different scan data. For example, in two sets of overlapping point cloud data, feature point pairs with similar local curvature and neighborhood distribution are found, and by calculating the relative position and angle changes between these point pairs, the transformation matrix required to convert one set of data to the same coordinate system as the other set of data is determined. Finally, based on the calculated transformation matrix, the point cloud data of each part is unified into the same coordinate system and further processed and optimized, such as weighted averaging of the data in overlapping areas to eliminate slight differences caused by multiple scans. Ultimately, a point cloud model is constructed that can truly reflect the actual conditions of the area to be inspected during the construction process.

[0060] A BIM model is a digital, integrated representation of all information related to a construction project. There are two common ways to obtain BIM models of the area to be inspected. First, directly export them from specialized design software (such as Revit and ArchiCAD) used during the architectural design phase. During the design phase, designers use these software to create a BIM model containing detailed information such as the building's geometry, structural system, building materials, and equipment and facilities. During the construction phase, the model of the area to be inspected can be extracted from this model as needed. Second, if the construction company has established its own BIM database during project management, it can also retrieve existing BIM models of the area to be inspected from this database. This database is typically updated in real time as construction progresses, ensuring the model's consistency with actual construction conditions. BIM models not only contain the building's 3D geometry but also integrate a wealth of attribute information related to building components, such as material, specifications, model, and manufacturer. During construction, obtaining BIM models of the area to be inspected can provide comprehensive support for construction management, quality inspection, and other tasks. When used in conjunction with the point cloud model, the BIM model serves as a theoretical reference. By comparing the actual construction conditions reflected by the point cloud model with the design information in the BIM model, it is possible to intuitively discover whether there are deviations in the construction process, providing a strong basis for timely corrective measures, helping to improve construction quality and ensure the smooth progress of the project.

[0061] Step 120 : Align the point cloud model and the BIM model, and obtain 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 between each data point and the corresponding theoretical point in spatial position.

[0062] Point cloud models are constructed using actual data collected at the construction site using laser scanners. They reflect the actual state of construction, including various construction deviations, on-site debris interference, and other real-world conditions. BIM models, on the other hand, are based on design drawings and specifications and represent the ideal state that a building should achieve. Due to their different sources and natures, their coordinate systems, scales, and model expressions may differ. By aligning the two, the actual situation presented by the point cloud model can be directly compared with the design information in the BIM model, facilitating an accurate assessment of any deviations during construction, as well as their specific location and extent.

[0063] After the point cloud model and BIM model are aligned, the matching information between each data point on the point cloud model and the theoretical point on the BIM model can be obtained. This matching information records in detail the spatial correspondence between each actual measurement point in the point cloud model and the theoretical design point in the BIM model. This correspondence can clearly understand the difference between the actual construction situation and the design requirements.

[0064] Step 130: Divide the area to be inspected into several inspection blocks, and determine the deviation threat evaluation value of the point cloud model in each inspection block based on the pairing information. The deviation threat evaluation value is used to measure the degree of deviation of the point cloud model in the inspection block relative to the BIM model.

[0065] The inspection areas of construction projects are often large and complex. Analyzing the discrepancies between the point cloud model and the BIM model as a whole results in a massive amount of data and inaccurate analysis. By dividing the inspection area into several inspection blocks, the complex overall problem can be broken down into multiple, relatively simple, local issues for analysis. This not only reduces analysis complexity but also allows for more detailed focus on the construction status of each local area, improving the accuracy and specificity of construction deviation detection.

[0066] In specific application scenarios, the basis for detecting block division can be determined based on the building's structural characteristics, functional zoning, and construction techniques. For example, vertically, detection blocks can be divided according to the building's natural floors; horizontally, detection blocks can be divided according to different functional areas, such as the business area, storage area, and equipment room of a shopping mall; or, based on differences in construction techniques, areas using different construction methods can be divided. The division method can also utilize spatial division algorithms, such as grid-based division methods, to divide the detection area in three-dimensional space into cubes or rectangular blocks of uniform size or varying sizes depending on the actual situation. Alternatively, division can be performed based on the boundaries of building components, with the detection block range determined by component. In this application, the technical solution herein is described using the example of dividing the detection area in three-dimensional space into cubes of uniform size (e.g., 1m×1m×1m), but this does not constitute a specific limitation.

[0067] To measure the degree of deviation of the point cloud model from the BIM model within each inspection block, a deviation threat assessment value must be calculated. This calculation takes into account multiple factors. First, the spatial distance deviation between the data points on the point cloud model and the corresponding points in the BIM model. A larger distance deviation indicates a potentially greater degree of deviation. For example, if the spatial distance between a point cloud model point and its corresponding BIM model point within a particular inspection block is significantly greater than that of other points, this may indicate significant construction deviation in that area. Secondly, since normal building point clouds primarily consist of elements such as walls, internal structures, doors, and windows, which have regular surface structures and well-defined geometric shapes, point cloud data collected by laser scanners is typically dense and evenly distributed. However, construction site debris, typically consisting of building materials, tools, and garbage, is often complex and scattered, resulting in lower and more unevenly distributed point clouds. Therefore, the density stability of each inspection block can be determined based on the distribution density of the data points within the block. Using a specific algorithm, these factors, such as distance deviation and density stability, are quantitatively and comprehensively calculated to ultimately determine the deviation threat assessment value of the point cloud model within each inspection block. The deviation threat evaluation value can intuitively reflect the degree of deviation between the actual construction situation and the design requirements within the detection block, providing a key basis for subsequent judgment on whether there are errors in the construction and the severity of the errors.

[0068] Step 140 : Determine a deviation block with construction errors among the plurality of detection blocks based on the deviation threat evaluation value, and output construction error prompt information about the construction position corresponding to the deviation block.

[0069] To accurately determine which inspection blocks have construction errors, a reasonable threshold for the deviation threat assessment value must be pre-set. This threshold is typically determined based on the design standards and construction specifications of the building project, as well as empirical data from similar projects. For example, for common building structures, a block's deviation threat assessment value exceeding 0.6 may be considered to have a construction error. This threshold is not fixed and may be adjusted based on building type, functional requirements, and the complexity of the construction process. After calculating the deviation threat assessment value for each inspection block, each block's assessment value is compared against the threshold. Blocks with a value greater than the threshold are identified as having construction errors. For example, if, within an inspection area containing 100 inspection blocks, the deviation threat assessment values ​​of 10 blocks exceed the threshold, these 10 blocks are identified as deviation blocks. This quantitative screening approach can more objectively and accurately identify areas with construction problems than subjective judgment.

[0070] The output construction error prompt information contains several key elements. The 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 identification. For example, the deviation block is located on the 3rd floor of the building, with a coordinate range of (X1, Y1, Z1) to (X2, Y2, Z2), and belongs to a specific corner of the shopping mall's 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 error will also be mentioned, such as the verticality error of a column or the flatness error of a wall. By outputting the construction error prompt information, construction personnel can quickly locate the specific location of the construction error based on the prompt information, understand the direction and degree of the error, and thus formulate targeted rectification plans.

[0071] 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 construction process, there is no need to comprehensively clean the construction site, which can reduce the consumption of manpower and material resources and the impact on the construction period, and provide a reliable basis for subsequent analysis. The two types of models are aligned and pairing information is obtained through a specific alignment algorithm, which effectively overcomes the noise interference caused by debris in the point cloud data and accurately establishes a spatial correspondence. 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 partitioning processing method refines the analysis of the construction area, avoids overall misjudgment due to debris, and improves detection accuracy. Finally, the deviation block is determined based on the evaluation value and accurate construction error prompt information is output, so that construction personnel can accurately locate construction errors, avoid misjudgment, make targeted rectifications, improve construction quality control efficiency, and ensure construction progress.

[0072] based on Figure 1 The embodiment shown is a refinement and expansion of the above embodiment. In order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 2 The specific method shown. Figure 2 based on Figure 1 The embodiment shown. Figure 2 As shown, the method includes the following steps:

[0073] Step 210: construct a point cloud model of the area to be inspected during the construction process, and obtain a BIM model of the area to be inspected.

[0074] For the embodiment of the present disclosure, the specific implementation process can be found in the relevant description of step 110 of the embodiment, which will not be repeated here.

[0075] Step 220 : Align the point cloud model and the BIM model, and obtain pairing information between each data point on the point cloud model and a theoretical point on the BIM model after the alignment process.

[0076] In the embodiment of the present disclosure, the alignment process of the point cloud model and the BIM model in step 220 may include the following steps:

[0077] Step 220 - 1 : Acquire a first alignment feature point of the point cloud model and a second alignment feature point of the BIM model.

[0078] For the embodiment 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 clouds within the same division have similar features, while region extraction is based on curvature and normal vector segmentation. Several regions in the point cloud model are obtained through region extraction, 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 region corresponding to the center of the region that takes the maximum and minimum values ​​in the X, Y and Z directions is obtained, and recorded as the edge region. The edge region is the wall corresponding to the boundary of the area to be inspected. Then, the intersection between each two edge regions in the point cloud model can be obtained, which is recorded as the first alignment feature point of the point cloud model; the BIM model has records of the size data and attribute data of each part of the construction project, so the second alignment feature point corresponding to the edge wall of the area to be inspected can be directly obtained.

[0079] Accordingly, 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 several regions based on the coordinate values ​​of all data points in each region; determining the intersection between any two edge regions as the first alignment feature point of the point cloud model; extracting the wall edge of the BIM model based on the size data and attribute data of the BIM model, and determining the edge data points of the wall edge as the second alignment feature points of the BIM model.

[0080] Step 220 - 2 : Perform feature matching on the first alignment feature point and the second alignment feature point to obtain a corresponding feature point pair.

[0081] The core goal of feature matching is to find point pairs with similar geometric features and spatial positional relationships between the first aligned feature points of the point cloud model and the second aligned feature points of the BIM model. A common matching method is based on feature descriptors. For example, a feature descriptor can be generated for each first aligned feature point and second aligned feature point. This descriptor is a quantitative expression of the local geometric properties of the feature point. For example, a descriptor based on normals and curvature encodes information such as the distribution of normal directions and curvature changes within a certain neighborhood around the feature point. A match is then determined by calculating the similarity between the descriptors of different feature points (such as Euclidean distance and cosine similarity). When the descriptor similarity of two feature points exceeds a preset threshold, the two feature points are considered to constitute a corresponding feature point pair.

[0082] Step 220-3: Calculate the transformation matrix based on the feature point pairs.

[0083] The transformation matrix is ​​used to describe the transformation relationship of the point cloud model in space, such as rotation, translation, and scaling, relative to the BIM model. The calculated transformation matrix can accurately transform the points in the point cloud model to a coordinate system consistent with the BIM model, thereby achieving alignment between the two.

[0084] Step 220 - 4 : Align the point cloud model and the BIM model based on the transformation matrix.

[0085] After obtaining the transformation matrix, it can be applied to every point in the point cloud model. For any point in the point cloud model, the transformation matrix can be used to transform its position and attitude. 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 point cloud model's position and attitude in space to change, gradually approaching the coordinate system of the BIM model. Once the point cloud model and the BIM model are aligned in spatial position and attitude, the differences between the two can be more intuitively and accurately compared, providing strong support for subsequent construction deviation detection, quality assessment, and other tasks.

[0086] Step 230: Divide the area to be inspected into several inspection blocks, and determine the matching degree between the point cloud model and the BIM model in each inspection block based on the pairing information.

[0087] 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.

[0088] In the embodiment of the present disclosure, determining the degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information in step 230 may include the following steps:

[0089] Step 230 - 1 : Calculate the pairing coefficient of each theoretical point based on the number of data points that are successfully paired with each theoretical point.

[0090] For parts without construction errors or noise, the point cloud model and the BIM model are basically consistent. However, when there are engineering errors or noise in the point cloud model, the Euclidean distance between the data points with engineering errors or noise and the corresponding points in the BIM model is large. The deviation between each data point in the point cloud model and the theoretical point and the Euclidean distance between the corresponding points are used to obtain the parts of the model that may have engineering errors.

[0091] Get the Euclidean distance between each data point and each theoretical point, and take the theoretical point with the smallest Euclidean distance to each data point, and record it as the corresponding point of each data point. Ideally, theoretical points correspond to data points one by one, but when the shape or size of the point cloud model and the BIM model deviate, it may happen that one theoretical point corresponds to multiple or zero data points. The number of data points corresponding to each theoretical point reflects the correspondence between each theoretical point and the data point, and is recorded as the pairing coefficient of each theoretical point. The pairing coefficient of the qth theoretical point is The calculation method is:

[0092]

[0093] Where, is the pairing coefficient of the qth theoretical point; Indicates the number of data points corresponding to the qth theoretical point; represents the linear normalization function; Indicates taking the absolute value; It represents the absolute value of the difference between the number of data points corresponding to the qth theoretical point and 1. Ideally, theoretical points correspond to data points one-to-one, so the larger the value, the greater the deviation in the corresponding situation of the points.

[0094] Step 230 - 2 : Calculate the corresponding distance between each data point in each detection block and the target theoretical point based on the pairing coefficient.

[0095] For the embodiment of the present disclosure, the corresponding distance of each data point can be obtained by combining the pairing coefficient. The corresponding distance between the pth data point and the target theoretical point The calculation method is:

[0096]

[0097] Where, is the corresponding distance between the pth data point and the target theoretical point; represents the linear normalization function; Indicates the pairing coefficient of the target theoretical point corresponding to the p-th data point; Represents the Euclidean distance between the pth data point and its corresponding target theoretical point.

[0098] Step 230 - 3 : Calculate the average corresponding distance of multiple data points in each detection block with respect to multiple corresponding distances.

[0099] Step 230 - 4 : Based on the average corresponding distance, calculate the matching degree between the point cloud model and the BIM model in each detection block.

[0100] The matching degree of each detection block is obtained based on 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 in the detection block and the BIM model. The closer the point cloud model fits the BIM model, the higher the overlap between all data points in the block and the corresponding theoretical points, and the smaller the corresponding distance, the greater the matching degree. The matching degree of the kth detection block The calculation method is:

[0101]

[0102] Where, is the matching degree of the kth detection block; Represents the total number of all data points in the k-th detection block; represents the corresponding distance of the mth data point of the kth detection block; It represents the inverse normalization of the exponential function with the natural constant as the base; is the average corresponding distance of multiple data points in the kth detection block with respect to multiple corresponding distances.

[0103] Step 240: Determine region extraction information within each detection block, and determine the density stability of the point cloud model within each detection block based on the region extraction information.

[0104] The degree of match is calculated based on the Euclidean distance between data points and theoretical points, as well as the correspondence between them. However, calculating the degree of match alone cannot guarantee the presence of construction errors. A low degree of match for a test block could indicate deviations due to operational errors during construction, or noise generated by debris at the construction site within the point cloud model. Therefore, the density stability of each block can also be analyzed. Density stability refers to the uniformity of point cloud data density within the test block and its stability over spatial position. Stable point cloud density means that the point cloud data is relatively evenly distributed within the test block, without significant sudden changes in density. This generally indicates a stable data collection process and a relatively regular representation of the building structure or construction conditions. Conversely, unstable point cloud density, with uneven or even sudden changes in density, may indicate interference with data collection (e.g., the presence of debris at the construction site, such as building materials, tools, or trash).

[0105] In the embodiment 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:

[0106] Step 240 - 1 : Perform region extraction on each detection block to determine at least one region contained in each detection block.

[0107] After the inspection area in a building construction project is divided into inspection blocks, each inspection block may contain multiple building components or areas in different construction states. Region extraction can segment the complex point cloud data within the inspection block into relatively independent and clearly defined sub-regions based on different geometric shapes, object boundaries, or other features. After region extraction, each inspection block will display different region division results. These regions may be simple planar areas, such as building walls and floors, or complex three-dimensional structures, such as staircases and intricate architectural decorative shapes.

[0108] Step 240 - 2 : Calculate the maximum and minimum regional density values ​​in at least one region.

[0109] After calculating the point cloud density of each area in at least one region, the areas with the largest and smallest density values ​​are found out, and the corresponding density values ​​are the maximum and minimum regional density values.

[0110] Step 240 - 3 : Calculate the density stability of the point cloud model in each detection block based on the number of regions, the maximum region density, and the minimum region density of at least one region.

[0111] In the embodiment 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 to calculate the density stability of the point cloud model in each detection block. The formula characteristics of the density stability calculation formula are described as follows:

[0112]

[0113] Where, 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, Indicates the maximum regional density in the k-th detection block; Indicates the minimum regional density in the k-th detection block; It reflects the extreme difference in regional density within the kth detection block. The larger the value, the more likely it is that there is a point cloud corresponding to debris in the kth detection block. I want to make This overall value is between 0 and 1 and is used as The weight of .

[0114] Step 250: Determine the deviation threat evaluation value of the point cloud model in each detection block based on the matching degree and density stability.

[0115] This step distinguishes between possible construction deviations and debris noise by comparing the point cloud model with the BIM model and the density distribution of the point cloud model. This generates a deviation rating for each inspection block. The deviation rating reflects the probability of construction deviation in each inspection block.

[0116] In the embodiment of the present disclosure, determining the deviation threat evaluation value of the point cloud model in each detection block based on the matching degree and density stability in step 250 may include the following steps:

[0117] Step 250 - 1 : Calculate a first deviation evaluation value of the point cloud model in each detection block in the current detection stage based on the matching degree and density stability.

[0118] In the embodiment of the present disclosure, the matching degree and density stability of the point cloud model in each detection block can be substituted into the deviation evaluation value calculation formula to obtain the first deviation evaluation value of the point cloud model in each detection block in the current detection stage. The formula characteristic description of the deviation evaluation value calculation formula is:

[0119]

[0120] Where, is the first deviation evaluation value of the point cloud model in the kth detection block in the current detection stage; represents the linear normalization function; is the matching degree of the kth detection block; is the density stability of the point cloud model within the k-th detection block.

[0121] Step 250 - 2 : Obtain a second deviation evaluation value of the point cloud model in each detection block during the historical detection phase.

[0122] Throughout the construction cycle of a building project, it is necessary to conduct inspections and tests for construction errors at multiple stages to ensure the correct progress of construction at all stages. The construction errors screened out at different stages may vary. In addition to the inspection at the current detection stage, when conducting inspections for construction errors, the deviation evaluation of locations inspected in the previous stage (i.e., the second deviation evaluation value) is also used as a reference for this inspection. This avoids the situation where construction errors accumulate over multiple stages and go undetected in a timely manner. In other words, the deviation evaluation obtained in the previous stage of inspection was not large, but as construction progresses, the deviation gradually increases.

[0123] According to the embodiment of the present disclosure, the second deviation evaluation value of each detection block in all detection stages before the current detection stage may be obtained according to the calculation method of the deviation evaluation in the aforementioned steps.

[0124] Step 250 - 3 : Determine the deviation threat evaluation value of the point cloud model in each detection block based on the first deviation evaluation value and the second deviation evaluation value.

[0125] For the embodiments of the present disclosure, the embodiment steps may include: calculating the deviation gain coefficient of the point cloud model in 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 in each detection block.

[0126] The deviation of the same inspection block may change in different inspection stages. For example, a block with a larger deviation evaluation in the previous inspection stage may have been corrected, and the deviation evaluation may have decreased; or it may not have been corrected, and the deviation still exists, and the deviation evaluation remains unchanged; or even because the building structure at the location with a larger deviation evaluation in the previous stage is unstable, the deviation situation may worsen during the construction process.

[0127] Taking the inspection of the Tth detection stage as an example, the deviation gain coefficient of the kth detection block in the Tth detection stage is The calculation method is:

[0128]

[0129] Where, is the deviation gain coefficient of the kth detection block in the Tth detection stage. The larger the deviation gain coefficient, the more likely the kth detection block is to have a deteriorated deviation. ReLU represents the ReLU function, which is expressed as , that is, when the input is less than or equal to 0, the function value is 0, and when the input is greater than 0, the function value is equal to the input value. The ReLU function is used here to avoid the negative value of the corrected deviation affecting the subsequent calculation results; Indicates the first deviation evaluation value of the point cloud model in the k-th detection block in the T-th detection stage in the current detection stage; It represents the second deviation evaluation value of the point cloud model in the k-th detection block in the T-th detection stage in the historical detection stage.

[0130] As deviation evaluations accumulate, construction errors may become more difficult to correct or pose a safety hazard to the project. Therefore, the deviation evaluation value is adjusted based on the deviation changes of each detection block. The deviation gain coefficient of each detection block from all detection stages prior to the Tth detection stage is combined with the first deviation evaluation value of the current detection stage to obtain the deviation threat evaluation for each detection block. The deviation threat evaluation represents the result of amplifying the deviation evaluation with the deviation gain coefficient, reflecting the threat level of deviation deterioration for each detection block.

[0131] The calculation method of the deviation threat evaluation of the k-th detection block in the T-th detection stage is:

[0132]

[0133] Where, 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; It is a multiplication symbol, which means to perform multiplication operation on the following expressions; It represents the deviation gain coefficient of the kth detection block in the tth detection stage. When T=1, take (There is a normalization step in the deviation evaluation calculation step. Here, abnormal situations are amplified, and non-abnormal situations remain unchanged. This does not affect the subsequent threshold judgment and does not require normalization).

[0134] Compared with deviation evaluation, 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, which can detect potential deviation risks that may worsen more promptly.

[0135] Step 260: Determine the detection blocks whose corresponding deviation threat evaluation values ​​are greater than or equal to the threat warning threshold among the detection blocks as deviation blocks with construction errors, and output construction error prompt information about the construction positions corresponding to the deviation blocks.

[0136] The threat warning threshold is a pre-set standard value determined based on the building's design standards, construction specifications, and empirical data from similar projects. Different types of construction projects will have different threat warning thresholds due to varying design precision requirements and construction complexity. For example, for precision laboratory buildings requiring extremely high spatial precision, the threat warning threshold may be set relatively low to ensure strict construction quality control. For more general residential buildings, the threshold may be more relaxed, but still ensure that basic construction quality standards are met.

[0137] For the embodiment of the present disclosure, after calculating the deviation threat evaluation values ​​of several detection blocks, 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 to be deviation blocks with construction errors. For example, in an area to be detected 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 areas with problems in construction compared to subjective judgment.

[0138] In summary, the technical solution in this application, by constructing a point cloud model and obtaining a BIM model during the construction process, does not require a comprehensive cleaning of the construction site, can reduce the consumption of manpower and material resources and the impact on the construction period, and provide a reliable basis for subsequent analysis. The two types of models are aligned and pairing information is obtained through a specific alignment algorithm, which effectively overcomes the noise interference caused by debris in the point cloud data and accurately establishes a spatial correspondence. 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 partitioning processing method refines the analysis of the construction area, avoids overall misjudgment due to debris, and improves detection accuracy. Finally, the deviation block is determined based on the evaluation value and accurate construction error prompt information is output, so that construction personnel can accurately locate construction errors, avoid misjudgment, make targeted rectifications, improve construction quality control efficiency, and ensure construction progress.

[0139] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0141] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection 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 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 the alignment processing, wherein the pairing information is used to represent a corresponding relationship between each data point and a corresponding theoretical point in spatial position; Divide the area to be inspected into a plurality of inspection blocks, and determine a deviation threat evaluation value of the point cloud model in each inspection 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 relative to the BIM model in the inspection block; Determining a deviation block having a construction error among 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; Determining a deviation threat evaluation value of the point cloud model in each detection block based on the pairing information includes: Determining a degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information; Determining region extraction information within each detection block, and determining density stability of the point cloud model within each detection block based on the region extraction information; wherein the density stability is used to characterize the uniformity of the density of the point cloud data within each detection block and its stability with changes in spatial position; Determining a deviation threat evaluation value of the point cloud model in each detection block according to the matching degree and the density stability includes: Calculating a first deviation evaluation value of the point cloud model in each detection block at a current detection stage based on the matching degree and the density stability; Obtaining a second deviation evaluation value of the point cloud model in each detection block during a historical detection phase; A deviation threat evaluation value of the point cloud model in each detection block is determined based on the first deviation evaluation value and the second deviation evaluation value.

2. The BIM data processing method for construction project management according to claim 1, characterized in that: Performing alignment processing on the point cloud model and the BIM model, including: 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 points and the second alignment feature points to obtain corresponding feature point pairs; Calculating 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 edges of the BIM model are extracted, and the edge data points of the wall edges are determined as 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: 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 data points that are successfully matched with each theoretical point; Determining a degree of matching between the point cloud model and the BIM model in each detection block based on the pairing information includes: Calculating the pairing coefficient of each theoretical point according to the number of successfully paired data points corresponding to each theoretical point; Calculating the corresponding distance between each data point in each detection block and the target theoretical point based on the pairing coefficient; Calculating an average corresponding distance of a plurality of data points in each detection block with respect to a plurality of 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.

5. The BIM data processing method for construction project management according to claim 1, 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 regional density and the minimum 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, density stability of the point cloud model in each detection block is calculated.

6. The BIM data processing method for construction project management according to claim 1, characterized in that: Determining a deviation threat evaluation value of the point cloud model in each detection block 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 using the deviation gain coefficient to obtain a deviation threat evaluation value of the point cloud model in each detection block.

7. 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.

8. 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 sets of point cloud data are fused to obtain a point cloud model of the area to be detected.

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