Building model construction method and system based on BIM technology

Abstract

The application relates to the technical field of electric digital data processing, in particular to a building model construction method and system based on BIM technology, which specifically comprises the following steps: determining a spatial distribution surface by projecting structure data in each solid unit of a building in different directions, calculating an angle distortion degree of each projection point based on the regularity of the distribution of the projection points on the surface; calculating a spatial abnormal distribution degree by the distance between the three-dimensional space fitting surfaces of each structure data and its neighborhood data; identifying abnormal data by the angle distortion degree and the spatial abnormal distribution degree, combining a clustering cluster algorithm, determining a correction proportion coefficient of the abnormal structure data, correcting the abnormal structure data in combination with its neighborhood structure data, and then constructing a BIM building model; the accuracy of the BIM building model construction is improved, and the problem that a fixed correction coefficient fails to fully consider the influence of the building geometric space structure on data correction, thereby leading to poor BIM building model construction effect is avoided.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing technology, specifically to a method and system for building model construction based on BIM technology. Background Technology

[0002] Building Information Modeling (BIM) is a digital model that integrates information from the entire lifecycle of a building project. It includes not only the building's three-dimensional geometry but also multi-dimensional data such as component attributes, spatial relationships, and geographic information. BIM is crucial for the 3D reconstruction of buildings. First, it provides accurate geometric data and component information, offering precise foundational data for 3D reconstruction. Second, the parametric nature of BIM models allows for rapid adjustments and modifications during the reconstruction process, improving efficiency. Furthermore, the interconnectedness of information within a BIM model helps in better understanding the building's structural and functional relationships, ensuring that the reconstructed model not only meets aesthetic requirements but also maintains consistency with the actual building in terms of function and performance.

[0003] In the process of building a BIM architectural model, the collected building data needs to be preprocessed. Traditional methods generally use fixed correction coefficients to correct abnormal data. However, due to the complex structure of BIM architectural models and the geometric spatial structure on their surfaces, fixed correction coefficients fail to fully consider the impact of the building's geometric spatial structure on data correction. This results in poor accuracy in data preprocessing, thus affecting the effectiveness of subsequent BIM architectural model construction. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for building model construction based on BIM technology, and the specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for building a building model based on BIM technology, the method comprising the following steps:

[0006] Laser scanning is performed on the surface of the building to obtain structural data at each scanning point; all structural data are divided into three-dimensional units, and the set of all structural data in each cubic unit is denoted as each structural data subset;

[0007] The spatial distribution surface of each structural data subset is determined based on the dispersion of the projection points on different projection surfaces; on the spatial distribution surface, the neighboring projection points of each projection point are obtained; the connection angle sequence of each projection point is constructed based on the angle between each projection point and its neighboring projection points; the angle distortion of each projection point is constructed based on the difference between the sum of continuous elements in the connection angle sequence and the preset optimal distribution angle.

[0008] Based on the neighboring projection points of each projection point, determine the neighborhood structural data of each structural data, obtain the fitting surface constructed by each structural data and its neighborhood structural data, and analyze the spatial anomaly distribution degree of each structural data based on the distance between each structural data and the corresponding fitting surface.

[0009] Based on the included angle distortion degree and the spatial anomaly distribution degree, all structural data in the structural data subset are clustered to obtain each cluster, and the anomaly distribution degree of each cluster is constructed by calculating the uniformity of element distribution in each cluster.

[0010] Abnormal structural data are determined based on the abnormal distribution degree and corrected by combining it with neighboring structural data; abnormal structural data correction is performed on all structural data subsets and they are combined, and BIM building model is constructed using the combined structural data set.

[0011] In one embodiment, the process of obtaining the spatial distribution surface is as follows:

[0012] For any projection surface of the current subset of structured data, calculate the distance between any structured data point on the projection surface and every other structured data point, and denot it as the first distance; obtain the minimum value among all the first distances of any structured data, and take it as the shortest Euclidean distance of any structured data; take the sum of the shortest Euclidean distances of all structured data on the projection surface as the spatial feature distance of the projection surface;

[0013] The projection surface corresponding to the maximum spatial feature distance among all projection surfaces of the current structural data subset is taken as the spatial distribution surface of the structural data subset.

[0014] In one embodiment, the process of obtaining the connection angle sequence is as follows:

[0015] Taking any projection point in the spatial distribution plane as the origin, obtain the line connecting each of its neighboring projection points to the arbitrary projection point. Among all the lines connecting the arbitrary projection point, obtain the included angle between two adjacent lines, and record it as the included angle of the origin line. Record the sequence of included angles of all the origin lines connecting the arbitrary projection point as the included angle sequence of the lines connecting the arbitrary projection point.

[0016] In one embodiment, the process of obtaining the included angle distortion is as follows:

[0017] The sum of each element in the angle sequence and the three elements following it is taken as the adjacent angle sum of each element; the sequence of adjacent angle sums of all elements in the angle sequence is denoted as the adjacent angle sum sequence.

[0018] The angular distortion of each projection point is calculated using the following expression:

[0019]

[0020] In the formula, This represents the angular distortion degree of the i-th projection point in the spatial distribution plane; This represents the maximum value of the elements in the sequence of adjacent angle sums of the i-th projection point on the spatial distribution surface; This represents the number of elements in the sequence of adjacent angle sums of the i-th projection point in the spatial distribution surface; R represents the j-th element in the sequence of adjacent angle sums of the i-th projection point on the spatial distribution surface; R represents the preset optimal distribution angle.

[0021] In one embodiment, the process of obtaining the spatial anomaly distribution degree is as follows:

[0022] The structural data corresponding to each neighboring projection point of any structural data is taken as the neighboring structural data of the any structural data. The distance from the fitted surface of the any structural data and all its neighboring structural data to the any structural data is calculated and taken as the anomaly distance of the any structural data.

[0023] The mean of the anomaly distances of all neighboring structural data of any given structural data is used as the segmentation threshold, and the set of anomaly distances less than or equal to the segmentation threshold is used as the calibration distance set of any given structural data.

[0024] The spatial anomaly distribution degree of each structured data is calculated using the following expression:

[0025] In the formula, This represents the spatial anomaly distribution degree of the i-th structural data in the current structural data subset; This represents the anomaly distance of the i-th structural data in the current subset of structural data; This represents the mean of all elements in the calibration distance set of the i-th structural data in the current structural data subset.

[0026] In one embodiment, the process of obtaining each cluster is as follows:

[0027] The normalized values ​​of the included angle distortion degree and the normalized values ​​of the spatial anomaly distribution degree of each structural data are combined to form a two-dimensional data, which is denoted as the anomaly identification array of each structural data. The anomaly identification arrays of all structural data in the structural data subset are used as the input of the clustering algorithm, and the output is each cluster.

[0028] In one embodiment, the process of obtaining the anomaly distribution degree is as follows:

[0029] Calculate the minimum distance between each element in a cluster and all other elements, and use the sum of the minimum distances of all elements in the cluster as the normal distribution of the cluster; record the distance from the cluster center to the cluster origin as the center distance of each cluster.

[0030] The outlier distribution of each cluster is calculated using the following expression:

[0031] In the formula, This represents the outlier distribution degree of the k-th cluster; This represents the center distance of the k-th cluster; This represents the normal distribution of the k-th cluster; This indicates the preset denominator adjustment parameter.

[0032] In one embodiment, the step of determining abnormal structure data based on the abnormal distribution degree and correcting it by combining it with neighborhood structure data specifically involves:

[0033] Clusters with anomaly distribution greater than a preset anomaly threshold are selected, and the structural data in the selected clusters are taken as anomalous structural data.

[0034] For each selected cluster, the normalized value of the maximum abnormal distribution degree in the cluster is used as the first correction scaling factor for the structural data in the cluster.

[0035] Based on each abnormal structural data and its neighboring structural data, and combined with the first correction ratio coefficient, each abnormal structural data is corrected to obtain the structural data correction value of the abnormal structural data.

[0036] In one embodiment, the expression for the structural data correction value is:

[0037] In the formula, This represents the structure data correction value for the x-th abnormal structure data. This represents the x-th abnormal structure data; This represents the mean of all neighboring structural data of the x-th abnormal structural data; This represents the first correction scaling factor for the x-th anomalous structural data; Let x be the second correction scaling factor for the x-th anomalous structural data, where... .

[0038] Secondly, embodiments of this application also provide a building model building system based on BIM technology, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0039] The embodiments of this application have at least the following beneficial effects:

[0040] This application determines the spatial distribution surface by projecting structural data from each three-dimensional unit of a building in different directions. Based on the distribution regularity of projection points in the neighborhood of each projection point on the spatial distribution surface, the angular distortion of each projection point is calculated. Anomalies are initially screened and abnormal data are preliminarily located based on the angular distribution of projection points. The spatial anomaly distribution degree is calculated by the distance between each structural data and the three-dimensional spatial fitting surface of its neighborhood data. This allows for more detailed identification of structural data with abnormal distribution in three-dimensional space, further refining the identification of abnormal data. The calculation process closely matches the actual building structure characteristics, which helps to improve the accuracy of data anomaly identification. By combining the angular distortion degree and the spatial anomaly distribution degree with a clustering algorithm, anomaly data is detected in multiple dimensions, gradually identifying anomalies and determining the correction ratio coefficient for abnormal structural data. Combined with its neighborhood structural data, the abnormal structural data is corrected from the overall distribution regularity, and then a BIM building model is constructed. This improves the accuracy of BIM building model construction and avoids the problem that fixed correction coefficients fail to fully consider the influence of the building's geometric spatial structure on data correction, resulting in poor BIM building model construction. Attached Figure Description

[0041] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating the steps of a BIM-based building model construction method provided in one embodiment of this application;

[0043] Figure 2 This is a schematic diagram illustrating the process of obtaining the spatial distribution surface. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the BIM-based building model construction method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0046] The following, in conjunction with the accompanying drawings, details the specific scheme of the building model construction method and system based on BIM technology provided in this application.

[0047] Please see Figure 1 The diagram illustrates a flowchart of a BIM-based building model construction method according to an embodiment of this application. The method includes the following steps:

[0048] Step S1: Perform laser scanning on the building surface to obtain structural data at each scanning point; divide all structural data into three-dimensional units, and denote the set of all structural data in each cubic unit as each structural data subset.

[0049] First, the building's data is collected. This application uses a 3D laser scanner to scan the building and obtain its structural data, which includes spatial dimensional information at each laser scanning point on the building's surface. After scanning the building's structural data, the 3D laser scanner can transmit the collected structural data to a BIM building model database via a wireless network to construct its BIM building model. The accuracy range of the building's structural data acquisition is generally 2nm to 10nm; in this embodiment, the accuracy is 5nm. The set of all collected structural data is denoted as the structural data set.

[0050] Furthermore, for the structural data in the structural data set, the value range of all structural data in each spatial dimension is statistically analyzed. The spatial dimensions described in this application include the x, y, and z axes in a Cartesian coordinate system. The value range interval in each dimension is divided into N equal parts, thereby dividing the spatial range formed by all structural data into N×N×N cubic units. In this embodiment, the value of N is 100. As other embodiments of this application, implementers can set the value of N according to actual conditions.

[0051] Next, all cube cells that do not contain structure data are removed, and the set of all structure data in each remaining cube cell is denoted as each structure data subset.

[0052] Step S2: Determine the spatial distribution surface of each structural data subset based on the dispersion of projection points on different projection surfaces; on the spatial distribution surface, obtain the neighboring projection points of each projection point; construct the connection angle sequence of each projection point based on the angle between each projection point and its neighboring projection points; construct the angle distortion degree of each projection point based on the difference between the sum of continuous elements in the connection angle sequence and the preset optimal distribution angle.

[0053] The structural data in each subset of structural data represents data information for a specific part of the building. When constructing a BIM building model using BIM technology, the structural data on the same surface of the building is collected by scanning points on the building surface with lasers in a regular manner. This results in the structural data collected on the building surface exhibiting the same regular distribution on a certain projection plane in space.

[0054] Since the distribution pattern is usually that each row and column is equally spaced, for each structural data in each structural data subset, taking structural data i as an example, structural data i has 8 neighborhoods, and the angle between the lines connecting the two neighborhood data points on the diagonal of the 8 neighborhoods and the center point i should be 180°.

[0055] Based on the above analysis, the abnormal data in the structured data has been preliminarily identified as follows:

[0056] Because the scanning process involves scanning the building from different directions, and because the 3D laser scanner collects structural data through spatial intervals on the building surface, the collected data points are not fixed on a single projection plane and distributed in the aforementioned pattern. Therefore, there will be differences in the angles between the structural data points in the spatial distribution.

[0057] Therefore, taking any subset of structured data as an example, all structured data in the subset of structured data are projected onto multiple planes in different directions. In this embodiment, the projection planes are the XOY, XOZ and YOZ planes of the spatial coordinate system.

[0058] Taking the structural data points on the XOY projection plane as an example, calculate the distance between structural data i and every other structural data point on the projection plane, and record it as the first distance; obtain the minimum value among all the first distances of structural data i, and take it as the shortest Euclidean distance of structural data i; sum the shortest Euclidean distances of all structural data on the projection plane, and take the sum as the spatial feature distance of the projection plane;

[0059] The projection surface corresponding to the maximum spatial feature distance among all projection surfaces of the structured data subset is taken as the spatial distribution surface of the structured data subset, which is used to characterize the dimension of the distribution of the main features of the data in the structured data subset.

[0060] Furthermore, in the spatial distribution plane, taking the i-th projection point as an example, the m other projection points closest to the i-th projection point are obtained as the neighboring projection points of the i-th projection point, where m is 8. Taking any axis in the spatial distribution plane as the 0-degree axis and the i-th projection point as the origin, starting from the 0-degree axis, the angle between the line connecting each neighboring projection point and the i-th projection point and the 0-degree axis is obtained in a clockwise direction, and recorded as the direction angle of each neighboring projection point. The sequence obtained by arranging the direction angles of all neighboring projection points in the order of acquisition is recorded as the direction angle sequence. The direction angle can be an obtuse angle.

[0061] Furthermore, a first-order difference sequence of the direction angle sequence is obtained using the first-order difference method. Each element in this first-order difference sequence is the angle between the lines connecting two adjacent neighboring projection points and the i-th projection point, denoted as the angle between the lines connecting the origins of the two adjacent neighboring projection points. It should be noted that the last neighboring projection point in the direction angle sequence is adjacent to the first neighboring projection point. The angle between the origins of these two neighboring projection points is obtained by adding the direction angle of the first neighboring projection point to 360° and then subtracting the direction angle of the last neighboring projection point. The first-order difference method is a well-known technique, and the specific calculation process will not be elaborated further.

[0062] The sequence of angles formed by the lines connecting the origins of all adjacent neighboring projection points of the i-th projection point is denoted as the line angle sequence of the i-th projection point.

[0063] When the i-th projection point is a noise point, the sum of any four connected elements in the angle sequence will deviate significantly from 180°. However, when its neighboring projection points are noise points, it will only cause the angle formed by the neighboring projection points to become distorted. Therefore, the sum of each element in the angle sequence and the three elements following it is calculated and recorded as the adjacent angle sum of that element. When the tail element in the angle sequence is insufficient, the first element is used to complete it. For example, the three elements after the last element are the first, second, and third elements in the angle sequence. The sequence obtained by arranging the adjacent angle sums of all elements in the angle sequence in a random order is recorded as the adjacent angle sum sequence.

[0064] Furthermore, for each projection point on the spatial distribution surface corresponding to the current subset of structural data, the angular distortion degree of each projection point is calculated, expressed as:

[0065]

[0066] In the formula, This represents the angular distortion degree of the i-th projection point in the spatial distribution plane; This represents the maximum value of the elements in the sequence of adjacent angle sums of the i-th projection point on the spatial distribution surface; This represents the number of elements in the sequence of adjacent angle sums of the i-th projection point in the spatial distribution surface; R represents the j-th element in the sequence of adjacent angle sums of the i-th projection point in the spatial distribution plane; R represents the preset optimal distribution angle, which is 180° in this embodiment.

[0067] When an anomaly occurs at the central projection point, the distribution of the projection point and its adjacent projection points becomes distorted. This causes the sum of four consecutive angles formed by the lines connecting the projection point to the adjacent spatial distribution plane to deviate from the optimal distribution angle. Simultaneously, the difference between consecutive adjacent angles increases, resulting in a greater degree of angular distortion at the projection point. A greater degree of angular distortion at a projection point indicates that the structural data corresponding to that projection point is more likely to be anomalous.

[0068] Step S3: Determine the neighborhood structural data of each structural data based on the neighboring projection points of each projection point, obtain the fitting surface constructed by each structural data and its neighborhood structural data, and analyze the spatial anomaly distribution degree of each structural data based on the distance between each structural data and the corresponding fitting surface.

[0069] For structural data that may not exhibit distortion on a spatial distribution surface but shows anomalies in three-dimensional space, taking the i-th structural data in a subset of structural data as an example, the structural data corresponding to the m neighboring projection points of the i-th structural data are used as the neighboring structural data of the i-th structural data. The i-th structural data and all its neighboring structural data are used as inputs to the least squares method for surface fitting, outputting the fitted surface of the i-th structural data. The calculation of the least squares fitted surface is a well-known technique, and the specific process will not be elaborated further.

[0070] The greater the distance between a structural data point and its corresponding fitted surface, the more likely it is to be an outlier. This is because a building is typically a planar or curved structure, without a single prominent point. Therefore, the distance from the i-th structural data point to its fitted surface is calculated as the outlier distance for that i-th structural data point. The calculation process for the distance from a point to the surface is a well-known technique, and the specific calculation process will not be elaborated here.

[0071] Because the scanning environment for building structural data is complex, there are slight deviations between the structural data and the distribution of surrounding structural data, resulting in the abnormal distance of the structural data not being zero. However, under normal circumstances, the abnormal distance value of the structural data is small and not significantly different from the abnormal distance of the surrounding structural data. Therefore, the mean of the abnormal distances of the m neighboring structural data of the i-th structural data is calculated as a segmentation threshold. The abnormal distances of all neighboring structural data of the i-th structural data are then segmented. The abnormal distances less than or equal to the segmentation threshold are grouped into a set as the calibration distance set of the i-th structural data, which is used to characterize the abnormal distance of normal structural data in the neighboring structural data of the i-th structural data.

[0072] Furthermore, the spatial anomaly distribution degree of each structural data in the current structural data subset is calculated, expressed as:

[0073]

[0074] In the formula, This represents the spatial anomaly distribution degree of the i-th structural data in the current structural data subset; This represents the anomaly distance of the i-th structural data in the current subset of structural data; This represents the mean of all elements in the calibration distance set of the i-th structural data in the current structural data subset.

[0075] When calculating the spatial anomaly distribution degree of structural data using anomaly distances, the anomaly distance will be large if the structural data is anomalous. Furthermore, to avoid anomaly data appearing within the neighborhood of the existing structural data and affecting the calculation, a segmentation algorithm is used to select the anomaly distances of normal structural data. This results in a larger difference between the anomaly distances of the anomalous structural data and their normal neighboring structural data, thus yielding a larger spatial anomaly distribution degree. Preprocessing is required for structural data with a large spatial anomaly distribution degree to ensure the accuracy of the structural data.

[0076] Step S4: Based on the included angle distortion degree and the spatial anomaly distribution degree, cluster all structural data in the structural data subset to obtain each cluster, and calculate the uniformity of element distribution in each cluster to construct the anomaly distribution degree of each cluster.

[0077] Since each projection point in the spatial distribution plane is a projection of the structural data subset, the angular distortion of the projection point is also the angular distortion of the corresponding structural data. Therefore, the angular distortion and spatial anomaly distribution of all structural data in the structural data subset are normalized using normalization functions to eliminate the dimensions of the data. In this embodiment, a maximum value normalization function is used to normalize both the angular distortion and spatial anomaly distribution. As another embodiment of this application, implementers may also use other normalization algorithms. The normalized angular distortion value and the normalized spatial anomaly distribution value of each structural data are combined to form a two-dimensional data set, denoted as the anomaly identification array for each structural data.

[0078] When there is abnormal data in the structured data subset, the distribution of the abnormal identification array of the structured data in the structured data subset in the two-dimensional space is polarized. The abnormal identification array of normal structured data is distributed around (0,0), and the abnormal identification array of abnormal structured data is distributed around (1,1). However, when there is no abnormal state in the structured data subset, the abnormal identification array of the structured data in the structured data subset is a uniformly distributed state.

[0079] Therefore, the anomaly identification array of all structural data in the structural data subset is used as the input to the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. For the necessary parameters of the algorithm, the neighborhood radius is set to 0.1, and the minimum number of clusters is set to 10. The algorithm outputs multiple clusters. The DBSCAN algorithm is a well-known technique, and its specific process will not be elaborated further.

[0080] Since the structured data is normally uniformly distributed, the minimum Euclidean distance between each element in a cluster and other elements is calculated, and the sum of these minimum Euclidean distances for all elements in a cluster is taken as the normal distribution value of the cluster. Simultaneously, the Euclidean distance from the cluster center of each cluster to the origin (0,0) during clustering is calculated and denoted as the center distance of each cluster.

[0081] Furthermore, the outlier distribution degree of each cluster is calculated, expressed as:

[0082]

[0083] In the formula, This represents the outlier distribution degree of the k-th cluster; This represents the center distance of the k-th cluster; This represents the normal distribution of the k-th cluster; This represents the preset denominator adjustment parameter, designed to prevent the denominator from being too small. Since the anomaly detection array is distributed in [0,1], the normal distribution value of the clusters must be less than 1, which would result in an excessively large result. Therefore... In this embodiment, the value is 1.

[0084] When anomalous structural data appears in a subset of structural data, the normalized values ​​of the angular distortion and spatial anomaly distribution of the anomalous structural data are close to 1. Therefore, the normal distribution value for clustering the anomalous structural data is relatively small. Furthermore, since the anomalous structural data is distributed near the (1,1) position on the two-dimensional coordinate axis, the greater the distance from the center of the cluster to the origin, the greater the anomaly distribution of the cluster. In this case, preprocessing of the structural data is necessary to ensure the accuracy of the BIM building structural data.

[0085] Step S5: Determine abnormal structural data based on the abnormal distribution degree, and correct it by combining it with neighboring structural data; perform abnormal structural data correction on all structural data subsets and combine them, and construct a BIM building model using the combined structural data set.

[0086] The outlier distribution degree of each cluster in the subset of structured data is calculated. Clusters with an outlier distribution degree greater than a preset outlier threshold are selected, and the structured data in these clusters are considered outlier structured data. The outlier distribution degree of the selected clusters is normalized to its maximum value, which is used as the first correction ratio coefficient for the structured data corresponding to the elements in the cluster. In this implementation, the outlier threshold is set to 0.95. Therefore, the structured data correction value for each outlier structured data is calculated, as expressed by:

[0087]

[0088] In the formula, This represents the structure data correction value for the x-th abnormal structure data. This represents the x-th abnormal structure data; This represents the mean of the m neighboring structural data of the xth abnormal structural data; This represents the first correction scaling factor for the x-th anomalous structural data; Let x be the second correction scaling factor for the x-th anomalous structural data, where... .

[0089] The above steps correct for abnormal structural data in each subset of structural data. All corrected subsets of structural data are then combined to obtain the structural data set of the BIM building model. Next, this structural data set of the BIM building model is used as input to the Delaunay triangulation algorithm, and the output is the BIM building model of the building. The calculation of the Delaunay triangulation algorithm is a well-known technique, and the specific calculation steps will not be elaborated here.

[0090] A schematic diagram of the process of obtaining the spatial distribution surface is shown below. Figure 2 As shown.

[0091] Based on the same inventive concept as the above methods, this application also provides a building model construction system based on BIM technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described building model construction methods based on BIM technology.

[0092] In summary, this application provides a BIM-based building model construction method. It determines the spatial distribution surface by projecting structural data from each three-dimensional unit of the building in different directions. Based on the distribution regularity of projection points in the neighborhood of each projection point on the spatial distribution surface, it calculates the angular distortion of each projection point, initially screening for anomalies and locating abnormal data from the angular distribution of projection points. By calculating the spatial anomaly distribution degree through the distance between each structural data point and the three-dimensional spatial fitting surface of its neighborhood data, it can more meticulously identify structural data with abnormal distributions in three-dimensional space, further refining the identification of anomalies. The calculation process closely matches the actual building structure characteristics, helping to improve the accuracy of data anomaly identification. Through angular distortion and spatial anomaly distribution degree, combined with a clustering algorithm, it performs multi-dimensional detection of anomaly data, gradually identifying anomalies and determining the correction ratio coefficient for abnormal structural data. Combined with its neighborhood structural data, it corrects the abnormal structural data from the overall distribution regularity, thereby constructing a BIM building model. This improves the accuracy of BIM building model construction and avoids the problem of poor BIM building model construction results caused by fixed correction coefficients failing to fully consider the influence of building geometry on data correction.

[0093] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0094] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0095] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for constructing building models based on BIM technology, characterized in that, The method includes the following steps: Laser scanning is performed on the surface of the building to obtain structural data at each scanning point; all structural data are divided into three-dimensional units, and the set of all structural data in each cubic unit is denoted as each structural data subset; The spatial distribution surface of each structural data subset is determined based on the dispersion of the projection points on different projection surfaces; on the spatial distribution surface, the neighboring projection points of each projection point are obtained; the connection angle sequence of each projection point is constructed based on the angle between each projection point and its neighboring projection points; the angle distortion of each projection point is constructed based on the difference between the sum of continuous elements in the connection angle sequence and the preset optimal distribution angle. Based on the neighboring projection points of each projection point, determine the neighborhood structural data of each structural data, obtain the fitting surface constructed by each structural data and its neighborhood structural data, and analyze the spatial anomaly distribution degree of each structural data based on the distance between each structural data and the corresponding fitting surface. Based on the included angle distortion degree and the spatial anomaly distribution degree, all structural data in the structural data subset are clustered to obtain each cluster, and the anomaly distribution degree of each cluster is constructed by calculating the uniformity of element distribution in each cluster. Based on the aforementioned abnormal distribution degree, abnormal structural data is determined and corrected by combining it with neighboring structural data; abnormal structural data correction is performed on all structural data subsets and then combined; and BIM building model is constructed using the combined structural data set. The process of obtaining the included angle distortion is as follows: The sum of each element in the angle sequence and the three elements following it is taken as the adjacent angle sum of each element; the sequence of adjacent angle sums of all elements in the angle sequence is denoted as the adjacent angle sum sequence. The angular distortion of each projection point is calculated using the following expression: In the formula, This represents the angular distortion degree of the i-th projection point in the spatial distribution plane; This represents the maximum value of the elements in the sequence of adjacent angle sums of the i-th projection point on the spatial distribution surface; This represents the number of elements in the sequence of adjacent angle sums of the i-th projection point in the spatial distribution surface; R represents the j-th element in the sequence of adjacent angle sums of the i-th projection point on the spatial distribution surface; R represents the preset optimal distribution angle. The process of obtaining the spatial anomaly distribution degree is as follows: The structural data corresponding to each neighboring projection point of any structural data is taken as the neighboring structural data of the any structural data. The distance from the fitted surface of the any structural data and all its neighboring structural data to the any structural data is calculated and taken as the anomaly distance of the any structural data. The mean of the anomaly distances of all neighboring structural data of any given structural data is used as the segmentation threshold, and the set of anomaly distances less than or equal to the segmentation threshold is used as the calibration distance set of any given structural data. The spatial anomaly distribution degree of each structured data is calculated using the following expression: In the formula, This represents the spatial anomaly distribution degree of the i-th structural data in the current structural data subset; This represents the anomaly distance of the i-th structural data in the current subset of structural data; This represents the mean of all elements in the calibration distance set of the i-th structural data in the current structural data subset.

2. The building model construction method based on BIM technology as described in claim 1, characterized in that, The process of obtaining the spatial distribution surface is as follows: For any projection surface of the current subset of structured data, calculate the distance between any structured data point on the projection surface and every other structured data point, and denot it as the first distance; obtain the minimum value among all the first distances of any structured data, and take it as the shortest Euclidean distance of any structured data; take the sum of the shortest Euclidean distances of all structured data on the projection surface as the spatial feature distance of the projection surface; The projection surface corresponding to the maximum spatial feature distance among all projection surfaces of the current structural data subset is taken as the spatial distribution surface of the structural data subset.

3. The building model construction method based on BIM technology as described in claim 1, characterized in that, The process of obtaining the connection angle sequence is as follows: Taking any projection point in the spatial distribution plane as the origin, obtain the line connecting each of its neighboring projection points to the arbitrary projection point. Among all the lines connecting the arbitrary projection point, obtain the included angle between two adjacent lines, and record it as the included angle of the origin line. Record the sequence of included angles of all the origin lines connecting the arbitrary projection point as the included angle sequence of the lines connecting the arbitrary projection point.

4. The building model construction method based on BIM technology as described in claim 1, characterized in that, The process of obtaining each cluster is as follows: The normalized values ​​of the included angle distortion degree and the normalized values ​​of the spatial anomaly distribution degree of each structural data are combined to form a two-dimensional data, which is denoted as the anomaly identification array of each structural data. The anomaly identification arrays of all structural data in the structural data subset are used as the input of the clustering algorithm, and the output is each cluster.

5. The building model construction method based on BIM technology as described in claim 1, characterized in that, The process of obtaining the anomaly distribution degree is as follows: Calculate the minimum distance between each element in a cluster and all other elements, and use the sum of the minimum distances of all elements in the cluster as the normal distribution of the cluster; record the distance from the cluster center to the cluster origin as the center distance of each cluster. The outlier distribution of each cluster is calculated using the following expression: In the formula, This represents the outlier distribution degree of the k-th cluster; This represents the center distance of the k-th cluster; This represents the normal distribution of the k-th cluster; This indicates the preset denominator adjustment parameter.

6. The building model construction method based on BIM technology as described in claim 1, characterized in that, The process of determining abnormal structural data based on the abnormal distribution degree and correcting it by combining it with neighborhood structural data specifically involves: Clusters with anomaly distribution greater than a preset anomaly threshold are selected, and the structural data in the selected clusters are taken as anomalous structural data. For each selected cluster, the normalized value of the maximum abnormal distribution degree in the cluster is used as the first correction scaling factor for the structural data in the cluster. Based on each abnormal structural data and its neighboring structural data, and combined with the first correction ratio coefficient, each abnormal structural data is corrected to obtain the structural data correction value of the abnormal structural data.

7. The building model construction method based on BIM technology as described in claim 6, characterized in that, The expression for the structural data correction value is: In the formula, This represents the structure data correction value for the x-th abnormal structure data. This represents the x-th abnormal structure data; This represents the mean of all neighboring structural data of the x-th abnormal structural data; This represents the first correction scaling factor for the x-th anomalous structural data; Let x be the second correction scaling factor for the x-th anomalous structural data, where... .

8. A building model building system based on BIM technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

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