A cable-stayed bridge modeling method based on BIM technology

Abstract

The present invention relates to the field of cable-stayed bridge modeling, and in particular to a cable-stayed bridge modeling method based on BIM technology. The method first segments the three-dimensional model of the cable-stayed bridge to obtain a plurality of segmented areas, and obtains the structural abnormality of the segmented areas according to the position distribution of the projection data points of the segmented areas on the edge of the projection area of ​​each plane, and the position distribution of the projection data points in the projection area of ​​each plane, and screens out the abnormal segmented areas, analyzes the structural differences between the abnormal segmented areas and the surrounding adjacent segmented areas, obtains the structural independence of the abnormal segmented areas, obtains the repair fineness of the abnormal segmented areas according to the structural abnormality and structural independence of the abnormal segmented areas and the number of data points, and optimizes the abnormal segmented areas of the three-dimensional model based on the repair fineness to obtain an optimized three-dimensional model. The present invention can effectively optimize the three-dimensional model of the cable-stayed bridge and improve the quality of the three-dimensional model of the cable-stayed bridge.

Description

Technical Field

[0001] The invention relates to the field of cable-stayed bridge modeling, and in particular to a cable-stayed bridge modeling method based on BIM technology. Background Art

[0002] BIM technology is building information modeling technology. In bridge projects such as cable-stayed bridges, the use of BIM technology to construct a three-dimensional model of the cable-stayed bridge can more effectively understand the specific structure of the cable-stayed bridge, which is of great significance in the maintenance and management of the cable-stayed bridge.

[0003] When using BIM technology to construct a cable-stayed bridge model, the corresponding three-dimensional model is usually generated using the collected image information of the cable-stayed bridge. The model accuracy of the cable-stayed bridge area with less image information is low. Therefore, in related technologies, interpolation processing is usually performed on the areas with low model accuracy to supplement and optimize them to improve the quality of the three-dimensional model. However, due to the complex structure of the cable-stayed bridge and the large structural differences in different areas, different areas of the three-dimensional model require different degrees of optimization, which in turn makes it impossible to effectively optimize the three-dimensional model of the cable-stayed bridge through existing methods, thereby reducing the quality of the three-dimensional model of the cable-stayed bridge. Summary of the invention

[0004] In order to solve the technical problem that the existing methods cannot effectively optimize the three-dimensional model of the cable-stayed bridge and reduce the quality of the three-dimensional model of the cable-stayed bridge, the purpose of the present invention is to provide a cable-stayed bridge modeling method based on BIM technology, and the technical solution adopted is as follows:

[0005] The present invention proposes a cable-stayed bridge modeling method based on BIM technology, the method comprising:

[0006] Acquire a three-dimensional model of the cable-stayed bridge to be tested, wherein the three-dimensional model includes a plurality of data points;

[0007] The three-dimensional model is segmented to obtain a plurality of segmented regions, any one of the segmented regions is used as a target segmented region, and data points in the target segmented region are projected onto the plane of the three-dimensional coordinate system where the three-dimensional model is located, respectively, to obtain the projection region of the target segmented region on each plane and the projection data points in the projection region; according to the position distribution of the projection data points on the edge of the projection region of each plane and the position distribution of the projection data points in the projection region of each plane, the structural abnormality of the target segmented region is obtained; based on the structural abnormality, the abnormal segmented region is screened out from all the segmented regions;

[0008] The distance from each data point in each segmented area to each plane of the three-dimensional coordinate system is used as the distance parameter of each data point in each segmented area with respect to each plane; any abnormal segmented area is used as the target abnormal segmented area, and the segmented area adjacent to the target abnormal segmented area is used as the reference segmented area, and the structural independence of the target abnormal segmented area is obtained according to the difference in the distribution of the distance parameters between the data points in the target abnormal segmented area and the data points in each reference segmented area with respect to the same plane; the restoration fineness of the target abnormal segmented area is obtained according to the structural abnormality and the structural independence of the target abnormal segmented area, and the number of data points in the target abnormal segmented area;

[0009] Based on the restoration detail, each abnormal segmentation region of the three-dimensional model is optimized to obtain an optimized three-dimensional model.

[0010] Furthermore, obtaining the structural abnormality of the target segmented region includes:

[0011] Taking any plane of the three-dimensional coordinate system where the three-dimensional model is located as the target plane, taking the projection area of ​​the target segmentation area on the target plane as the target projection area, performing curve fitting on the projection data points on the edge of the target projection area, and obtaining the fitting projection data points of each projection data point on the edge of the target projection area;

[0012] According to the distribution of the distance between the projection data points on the edge of the target projection area and the fitting projection data points of the projection data points, the irregularity of the projection edge of the target segmentation area on the target plane is obtained;

[0013] In the target projection area, a preset neighborhood is constructed with each projection data point as the center, and the average value of the Euclidean distance between each projection data point in the target projection area and all other projection data points in the preset neighborhood is normalized to obtain the outlier degree of each projection data point, and the projection data points with outlier degrees greater than the preset outlier threshold are regarded as isolated projection data points;

[0014] Obtaining the projection area irregularity of the target segmentation area on the target plane according to the distribution of the outlier degree of the projection data points in the target projection area and the number of isolated projection data points in the target projection area;

[0015] The projection edge irregularity and the projection area irregularity are integrated to obtain the integrated irregularity of the target segmentation area in the target plane; the accumulated values ​​of the integrated irregularities of the target segmentation area in all planes are normalized to obtain the structural abnormality of the target segmentation area.

[0016] Furthermore, obtaining the projection edge irregularity of the target segmentation area on the target plane includes:

[0017] The Euclidean distance between each projection data point on the edge of the target projection area and the fitted projection data point of each projection data point is used as the fitting error distance of each projection data point on the edge of the target projection area;

[0018] The standard deviation of the fitting error distance of all projection data points on the edge of the target projection area is used as the first edge irregularity parameter of the target projection area;

[0019] Extracting a maximum projection data point from all projection data points on the edge of the target projection area, wherein the fitting error distance of the maximum projection data point is greater than the fitting error distances of a preset number of other projection data points on the edge of the target projection area that are closest to the maximum projection data point;

[0020] The standard deviation of the fitting error distance of all maximum projection data points is used as the second edge irregularity parameter of the target projection area;

[0021] On the edge of the target projection area, the number of projection data points between each maximum projection data point and the next adjacent maximum projection data point is used as the interval distance coefficient of each maximum projection data point;

[0022] The standard deviation of the interval distance coefficients of all maximum projection data points is used as the third edge irregularity parameter of the target projection area;

[0023] The first edge irregularity parameter, the second edge irregularity parameter and the third edge irregularity parameter are integrated to obtain the projection edge irregularity of the target segmentation area on the target plane.

[0024] Further, the step of obtaining the irregularity of the projection area of ​​the target segmentation area on the target plane includes:

[0025] The range of the outlier degree of all projection data points in the target projection area and the number of isolated projection data points in the target projection area are combined to obtain the projection area irregularity of the target segmentation area on the target plane.

[0026] Furthermore, the step of screening out abnormal segmented regions from all segmented regions based on the structural abnormality degree includes:

[0027] The segmented region whose structural abnormality is greater than a preset abnormality threshold is regarded as an abnormal segmented region.

[0028] Furthermore, obtaining the structural independence of the target abnormal segmentation region includes:

[0029] The target abnormal segmentation area or any reference segmentation area is used as the area to be analyzed, and the average value of the distance parameter of all data points in the area to be analyzed with respect to each plane is used as the overall distance parameter of the area to be analyzed with respect to each plane; the standard deviation of the distance parameter of all data points in the area to be analyzed with respect to each plane is used as the distance distribution dispersion of the area to be analyzed with respect to each plane;

[0030] Taking any reference segmentation region as the target reference segmentation region, taking the absolute value of the difference between the target abnormal segmentation region and the target reference segmentation region on each plane as the first distribution difference of the target reference segmentation region on each plane; taking the absolute value of the difference between the target abnormal segmentation region and the target reference segmentation region on each plane as the second distribution difference of the target reference segmentation region on each plane;

[0031] The first distribution difference and the second distribution difference of the target reference segmentation region with respect to each plane are integrated to obtain a comprehensive distribution difference of the target reference segmentation region with respect to each plane; and the accumulated value of the comprehensive distribution difference of the target reference segmentation region with respect to all planes is used as the splitting degree of the target reference segmentation region;

[0032] The average values ​​of the splitting degrees of all reference segmented regions are normalized to obtain the structural independence of the target abnormal segmented region.

[0033] Furthermore, obtaining the restoration detail of the target abnormal segmentation area includes:

[0034] Negative correlation mapping is performed on the number of data points in the target abnormal segmentation area to obtain the repair fineness factor of the target abnormal segmentation area;

[0035] The structural abnormality, structural independence and repair detail factor of the target abnormal segmentation region are integrated and normalized to obtain the repair detail of the target abnormal segmentation region.

[0036] Furthermore, obtaining the optimized three-dimensional model includes:

[0037] If the repair detail of the target abnormal segmentation area is greater than a preset detail threshold, then based on the repair detail, the reshoot distance and the number of reshoot positions of the target abnormal segmentation area are obtained, and the area of ​​the cable-stayed bridge to be tested represented by the target abnormal segmentation area is reshoot and modeled by evenly arranging cameras using close-range photography 3D modeling technology to obtain a high-precision 3D model of the target abnormal segmentation area, and the target abnormal segmentation area and the high-precision 3D model are fused to obtain an optimized segmentation area of ​​the target abnormal segmentation area, wherein the number of cameras is the number of reshoot positions, and the distance between each camera and the area of ​​the cable-stayed bridge to be tested represented by the target abnormal segmentation area is the reshoot distance;

[0038] If the restoration detail of the target abnormal segmentation region is not greater than a preset detail threshold, interpolation processing is performed on the target abnormal segmentation region to obtain an optimized segmentation region of the target abnormal segmentation region;

[0039] The combination of the optimized segmented regions of each abnormal segmented region of the three-dimensional model and each non-abnormal segmented region is used as the optimized three-dimensional model.

[0040] Furthermore, the obtaining of the reshoot distance and the number of reshoot positions of the target abnormal segmentation area based on the restoration detail includes:

[0041] The product value of the restoration detail of the target abnormal segmentation area and the preset standard reshoot distance is used as the reshoot distance adjustment amount of the target abnormal segmentation area, and the difference between the preset standard reshoot distance and the reshoot distance adjustment amount is used as the reshoot distance of the target abnormal segmentation area;

[0042] The product value of the repair detail of the target abnormal segmentation area and the preset standard number of re-shooting positions is rounded up to obtain the adjustment amount of the number of re-shooting positions in the target abnormal segmentation area, and the sum of the preset standard number of re-shooting positions and the adjustment amount of the number of re-shooting positions is used as the number of re-shooting positions in the target abnormal segmentation area.

[0043] Further, obtaining a plurality of segmented regions includes:

[0044] All data points in the three-dimensional model are clustered to obtain different clusters, and the area formed by each cluster is used as the segmentation area.

[0045] The present invention has the following beneficial effects:

[0046] The present invention takes into account that the existing methods cannot effectively optimize the three-dimensional model of the cable-stayed bridge, resulting in poor quality of the constructed three-dimensional model of the cable-stayed bridge. Therefore, the three-dimensional model of the cable-stayed bridge is first segmented to obtain multiple segmented areas. Among the many segmented areas, there are normal segmented areas and abnormal areas with low model accuracy. The features of the abnormal areas with low model accuracy under different viewing angles are quite different from those of the normal segmented areas. At the same time, the cable-stayed bridge is mostly a structure with relatively regular edges such as the bridge deck and the steel cable. For the segmented areas with low model accuracy, the regularity of the edges of the projection area of ​​each plane is low, and the uniformity of the distribution of data points in the segmented areas with low model accuracy is poor, resulting in its The distribution uniformity of the projection data points in the projection area of ​​each plane is poor. Therefore, the structural anomaly degree can be used to reflect the possibility of modeling anomalies in the target segmentation area of ​​the three-dimensional model, and accurately screen out abnormal segmentation areas with low model accuracy. Taking into account the different structural differences between different abnormal segmentation areas and the surrounding segmentation areas, the optimization accuracy required for different abnormal segmentation areas is different. Therefore, the obtained structural independence degree reflects the degree of independence of the structure of the target abnormal segmentation area relative to the surrounding adjacent segmentation areas, and then the repair detail is used to perform different degrees of optimization and repair on each abnormal segmentation area, thereby improving the effective optimization of the three-dimensional model of the cable-stayed bridge and improving the quality of the three-dimensional model of the cable-stayed bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0048] Figure 1 A flow chart of a cable-stayed bridge modeling method based on BIM technology provided by one embodiment of the present invention;

[0049] Figure 2 A flow chart of a method for obtaining the structural abnormality of a target segmented region provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a cable-stayed bridge modeling method based on BIM technology proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] 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.

[0052] A specific scheme of a cable-stayed bridge modeling method based on BIM technology provided by the present invention is described in detail below with reference to the accompanying drawings.

[0053] See also Figure 1 , which shows a flow chart of a cable-stayed bridge modeling method based on BIM technology provided by an embodiment of the present invention, the method comprising:

[0054] Step S1: obtaining a three-dimensional model of the cable-stayed bridge to be tested, wherein the three-dimensional model includes a plurality of data points.

[0055] In the process of modeling a cable-stayed bridge using BIM technology, the corresponding three-dimensional model is usually generated by using the collected image information of the cable-stayed bridge. Therefore, the embodiment of the present invention first uses the oblique photography modeling technology to construct a three-dimensional model of the cable-stayed bridge to be tested, wherein the operation process of the oblique photography modeling technology is: using a multi-angle camera system equipped with an unmanned aerial vehicle to capture image data of the cable-stayed bridge to be tested from different perspectives, and then using a multi-view image matching algorithm to process the collected multi-angle image data of the cable-stayed bridge to generate a large amount of point cloud data, wherein the point cloud data generally includes spatial position information and color information, and the point cloud data is imported into the three-dimensional modeling software to obtain the three-dimensional model of the cable-stayed bridge to be tested, wherein the three-dimensional model includes a large number of data points, and the data points are the point cloud data.

[0056] Among them, oblique photography modeling technology and multi-view image matching algorithm are technical means well known to those skilled in the art and will not be elaborated here.

[0057] Step S2: segment the three-dimensional model to obtain multiple segmentation areas, take any segmentation area as the target segmentation area, project the data points in the target segmentation area onto the plane of the three-dimensional coordinate system where the three-dimensional model is located, and obtain the projection area of ​​the target segmentation area on each plane and the projection data points in the projection area; obtain the structural abnormality of the target segmentation area according to the position distribution of the projection data points on the edge of the projection area of ​​each plane and the position distribution of the projection data points in the projection area of ​​each plane; and screen out abnormal segmentation areas from all segmentation areas based on the structural abnormality.

[0058] In the above modeling process, due to factors such as the jitter of the drone and the occlusion of the cable-stayed bridge, insufficient image information is collected for certain areas of the cable-stayed bridge, which in turn leads to low model accuracy in certain areas of the three-dimensional model of the cable-stayed bridge. Therefore, in subsequent steps, it is necessary to determine the areas with low model accuracy in the three-dimensional model, and optimize and repair these areas to improve the quality of the three-dimensional model of the cable-stayed bridge. Therefore, an embodiment of the present invention first segments the three-dimensional model of the cable-stayed bridge to obtain multiple segmented areas, and different segmented areas represent different structures of the cable-stayed bridge. Subsequently, the segmented areas can be analyzed and abnormal segmented areas with low model accuracy can be accurately selected.

[0059] Preferably, in one embodiment of the present invention, the method for acquiring multiple segmented areas of the three-dimensional model of the cable-stayed bridge specifically includes:

[0060] All data points in the three-dimensional model are clustered, and data points with similar color information and spatial position information are clustered into one category, so as to obtain different clusters, and the area formed by each cluster is used as the segmentation area. Among them, the clustering method can use the existing DBSCAN clustering algorithm, which is not limited or elaborated here.

[0061] Among the numerous segmented areas, there are normal segmented areas and abnormal areas with low model accuracy. The features of the abnormal areas with low model accuracy at different viewing angles are quite different from those of the normal segmented areas. Therefore, an embodiment of the present invention first takes any segmented area as a target segmented area, and projects the data points in the target segmented area to the planes of the three-dimensional coordinate system where the three-dimensional model is located, and obtains the projection area of ​​the target segmented area on each plane and the projection data points in the projection area, so as to provide a data basis for subsequent analysis of the target segmented area at different viewing angles, wherein the three-dimensional coordinate system where the three-dimensional model is located has three planes, namely, the xoy plane, the xoz plane and the yoz plane.

[0062] It should be noted that in the process of three-dimensional projection to two-dimensional projection, there may be overlapping positions of projection data points corresponding to multiple data points in the target segmentation area. Therefore, the present invention regards the overlapping projection data points as the same projection data point to facilitate subsequent calculation and analysis.

[0063] Since most cable-stayed bridges are structures with relatively regular edges such as bridge decks and cables, and there are data point missing phenomena in the segmentation areas with low model accuracy, it is impossible to accurately construct the structure of the cable-stayed bridge itself, which in turn leads to low regularity of the edges of the projection areas of each plane in the segmentation areas with low model accuracy. At the same time, the uniformity of the distribution of data points in the segmentation areas with low model accuracy is poor, resulting in poor distribution uniformity of the projection data points in the projection areas of each plane. Therefore, the position distribution of the projection data points on the edge of the projection area of ​​each plane and the position distribution of the projection data points in the projection area of ​​each plane can be analyzed. The possibility of modeling anomalies in the target segmentation area of ​​the three-dimensional model can be reflected by the obtained structural anomaly degree. The larger the structural anomaly degree, the more likely the target segmentation area is an abnormal segmentation area with low model accuracy. Subsequently, the abnormal segmentation areas of the three-dimensional model can be screened out based on the structural anomaly degree. At the same time, the degree of optimization of different abnormal segmentation areas can be accurately analyzed based on the structural anomaly degree to improve the final quality of the three-dimensional model.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining the structural abnormality of the target segmented region specifically includes:

[0065] See also Figure 2 , which shows a flow chart of a method for obtaining the structural abnormality of a target segmented region provided by an embodiment of the present invention.

[0066] Step S201: Take any plane of the three-dimensional coordinate system where the three-dimensional model is located as the target plane, take the projection area of ​​the target segmentation area on the target plane as the target projection area, perform curve fitting on the projection data points on the edge of the target projection area, and obtain the fitted projection data points of each projection data point on the edge of the target projection area.

[0067] First, any plane of the three-dimensional coordinate system where the three-dimensional model is located is analyzed, and any plane of the three-dimensional coordinate system where the three-dimensional model is located is used as the target plane. For example, the xoy plane can be used as the target plane, and the projection area of ​​the target segmentation area on the target plane is used as the target projection area. Then, curve fitting is performed on the projection data points on the edge of the target projection area to obtain the fitted projection data points of each projection data point on the edge of the target projection area, wherein the fitted projection data points are generally located on a relatively smooth and regular fitting curve. Subsequently, based on the distance between the projection data points on the edge of the target projection area and the corresponding fitting projection data points, the irregularity of the edge direction of the target projection area is analyzed.

[0068] In one embodiment of the present invention, the curve fitting method may be the existing least square method, which is not limited or elaborated herein.

[0069] Step S202: Obtain the projection edge irregularity of the target segmentation region on the target plane according to the distribution of the distance between the projection data points on the edge of the target projection region and the fitting projection data points of the projection data points.

[0070] The more inconsistent the distance between the projection data point on the edge of the target projection area and its corresponding fitting projection data point is, the more irregular the edge shape of the target projection area is. Therefore, the distribution of the distance between the projection data point on the edge of the target projection area and the fitting projection data point of the projection data point can be analyzed. The obtained projection edge irregularity reflects the irregularity of the edge of the projection area of ​​the target segmentation area on the target plane. The larger the projection edge irregularity is, the more irregular the shape of the edge of the projection area of ​​the target segmentation area on the target plane is, which further indicates that the target segmentation area is more likely to be an abnormal segmentation area with low model accuracy. Subsequently, the structural abnormality of the target segmentation area can be accurately calculated based on the projection edge irregularity.

[0071] Preferably, in one embodiment of the present invention, the method for obtaining the irregularity of the projection edge of the target segmentation area on the target plane specifically includes:

[0072] First, the Euclidean distance between each projection data point on the edge of the target projection area and the fitted projection data point of each projection data point is taken as the fitting error distance of each projection data point on the edge of the target projection area.

[0073] The standard deviation of the fitting error distances of all projection data points on the edge of the target projection area is taken as the first edge irregularity parameter of the target projection area. The larger the first edge irregularity parameter is, the more inconsistent the fitting error distances of all projection data points on the edge of the target projection area are, which further indicates that the shape of the edge of the target projection area is more irregular.

[0074] Then, a maximum projection data point is extracted from all the projection data points on the edge of the target projection area, wherein the fitting error distance of the maximum projection data point is greater than the fitting error distances of a preset number of other projection data points on the edge of the target projection area that are closest to the maximum projection data point. That is to say, on the edge of the target projection area, if the fitting error distance of a certain projection data point is greater than the fitting error distances of a preset number of other projection data points that are closest to the maximum projection data point, then the projection data point is the maximum projection data point, wherein the preset number is set to 8, and the specific value of the preset number can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0075] The standard deviation of the fitting error distances of all maximum projection data points is taken as the second edge irregularity parameter of the target projection area. The larger the second edge regularity parameter is, the more inconsistent the fitting error distances of the more prominent projection data points on the edge of the target projection area are, which further indicates that the shape of the edge of the target projection area is more irregular.

[0076] Furthermore, on the edge of the target projection area, the number of projection data points between each maximum projection data point and the adjacent next maximum projection data point is used as the interval distance coefficient of each maximum projection data point, and the standard deviation of the interval distance coefficients of all maximum projection data points is used as the third edge irregularity parameter of the target projection area. The larger the third edge irregularity, the more uneven the position distribution of the maximum projection data points on the edge of the target projection area, and thus the more irregular the shape of the edge of the target projection area.

[0077] It should be noted that the edge of the target projection area is closed, so each maximum projection data point has an adjacent next maximum projection data point.

[0078] Finally, the first edge irregularity parameter, the second edge irregularity parameter and the third edge irregularity parameter are combined to obtain the projection edge irregularity of the target segmentation area on the target plane.

[0079] In an embodiment of the present invention, the sum or product of the first edge irregularity parameter, the second edge irregularity parameter and the third edge irregularity parameter may be used as the projection edge irregularity of the target segmentation area on the target plane to achieve a combination of the three, which is not limited here.

[0080] As an example, in one embodiment of the present invention, the expression of the projection edge irregularity of the target segmentation area on the target plane can be specifically, for example, as follows:

[0081] A=σ1+σ2+σ3

[0082] Wherein, A represents the projection edge irregularity of the target segmentation area on the target plane; σ1 represents the standard deviation of the fitting error distance of all projection data points on the edge of the target projection area, that is, the first edge irregularity parameter of the target projection area; σ2 represents the standard deviation of the fitting error distance of all maximum projection data points on the edge of the target projection area, that is, the second edge irregularity parameter of the target projection area; σ3 represents the standard deviation of the interval distance coefficient of all maximum projection data points on the edge of the target projection area, that is, the third edge irregularity parameter of the target projection area.

[0083] Step S203: In the target projection area, a preset neighborhood is constructed with each projection data point as the center, and the average value of the Euclidean distance between each projection data point in the target projection area and other projection data points in the preset neighborhood is normalized to obtain the outlier degree of each projection data point, and the projection data points with outlier degrees greater than the preset outlier threshold are regarded as isolated projection data points.

[0084] From the above analysis, it can be seen that the position distribution of the projection data points in the projection area on the coordinate system plane of the abnormal segmentation area with low model accuracy is uneven. Therefore, in the target projection area, a preset neighborhood is first constructed with each projection data point as the center, wherein, for a certain projection data point, the preset neighborhood of the projection data point includes a set number of other projection data points closest to the projection data point, wherein the set number is set to 10, and the specific value of the set number can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0085] Then, the average value of the Euclidean distance between each projection data point in the target projection area and all other projection data points in the preset neighborhood is normalized to obtain the outlier degree of each projection data point. The greater the outlier degree, the more isolated the projection data point is relative to the surrounding projection data points. Then, the projection data points with outlier degrees greater than the preset outlier threshold can be regarded as isolated projection data points. Subsequently, based on the number of isolated projection data points and the outlier degree of each projection data point, the uniformity of the position distribution of the projection data points in the target projection area can be accurately analyzed. The preset outlier threshold is set to 0.6, and the specific value of the preset outlier threshold can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0086] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0087] As an example, in one embodiment of the present invention, the expression of the outlier degree of each projection data point in the target projection area may be specifically, for example, as follows:

[0088]

[0089] Among them, B k Indicates the degree of outlier of the kth projection data point in the target projection area; D (k,i)represents the Euclidean distance between the kth projection data point in the target projection area and the ith other projection data point in the preset neighborhood; I represents the number of other projection data points in the preset neighborhood of the kth projection data point, that is, the set number; norm() represents the normalization function.

[0090] Step S204: Obtain the projection area irregularity of the target segmentation area on the target plane according to the distribution of the outlier degree of the projection data points in the target projection area and the number of isolated projection data points in the target projection area.

[0091] The more inconsistent the degree of outliers of the projection data points in the target projection area and the greater the number of isolated projection data points in the target projection area, the more uneven the position distribution of the projection data points of the target segmentation area in the projection area of ​​the target plane is. Therefore, the distribution of the degree of outliers of the projection data points in the target projection area and the number of isolated projection data points in the target projection area can be analyzed. The obtained projection area irregularity reflects the irregularity of the position distribution of the projection data points of the target segmentation area in the projection area of ​​the target plane. Subsequently, the structural abnormality of the target segmentation area can be accurately calculated based on the projection area irregularity.

[0092] Preferably, in one embodiment of the present invention, the method for obtaining the irregularity of the projection area of ​​the target segmentation area on the target plane specifically includes:

[0093] The larger the range of the outlier degree of all projection data points in the target projection area, the larger the range of variation of the outlier degree of the projection data points in the target projection area, which further indicates that the more inconsistent the outlier degree of the projection data points in the target projection area is, the more uneven the position distribution of the projection data points in the target projection area is. Therefore, the range of the outlier degree of all projection data points in the target projection area and the number of isolated projection data points in the target projection area can be combined to obtain the irregularity of the projection area of ​​the target segmentation area on the target plane.

[0094] In the embodiment of the present invention, the sum or product of the extreme difference of the outlier degree and the number of isolated projection data points may be used as the irregularity of the projection area of ​​the target segmentation area on the target plane to achieve a combination of the two, which is not limited here.

[0095] As an example, in one embodiment of the present invention, the expression of the irregularity of the projection area of ​​the target segmentation area on the target plane can be specifically, for example, as follows:

[0096] C=(B max -B min )+Z

[0097] Among them, C represents the irregularity of the projection area of ​​the target segmentation area on the target plane; B max Indicates the maximum outlier degree of all projected data points in the target projection area; B min Indicates the minimum outlier degree of all projected data points in the target projection area; B max -B min It represents the range of outliers of all projected data points in the target projection area; Z represents the number of isolated projected data points in the target projection area.

[0098] Step S205: Integrate the projection edge irregularity and the projection area irregularity to obtain the comprehensive irregularity of the target segmentation area in the target plane; normalize the accumulated value of the comprehensive irregularity of the target segmentation area in all planes to obtain the structural abnormality of the target segmentation area.

[0099] The greater the projection edge irregularity and the projection area irregularity, the more irregular the characteristics of the projection area of ​​the target segmentation area on the target plane. Therefore, the projection edge irregularity and the projection area irregularity can be integrated to obtain the comprehensive irregularity of the target segmentation area on the target plane. The greater the comprehensive irregularity, the more the characteristics of the projection area of ​​the target segmentation area on the target plane conform to the characteristics of low model accuracy. The comprehensive irregularity of the target segmentation area in each plane can be obtained by the same method mentioned above, and then the accumulated value of the comprehensive irregularity of the target segmentation area in all planes can be normalized to obtain the structural abnormality of the target segmentation area.

[0100] In the embodiment of the present invention, the sum or product of the projection edge irregularity and the projection area irregularity may be used as the comprehensive irregularity of the target segmentation area on the target plane to achieve the integration of the two, which is not limited here.

[0101] As an example, in one embodiment of the present invention, the expression of the structural abnormality of the target segmented region may be specifically, for example, as follows:

[0102]

[0103] E=A+C

[0104] Among them, F represents the structural abnormality of the target segmentation area; E j It represents the comprehensive irregularity of the target segmentation area in the jth plane; E represents the comprehensive irregularity of the target segmentation area in the target plane; A represents the irregularity of the projection edge of the target segmentation area in the target plane; C represents the irregularity of the projection area of ​​the target segmentation area in the target plane; norm() represents the normalization function.

[0105] The same method as above can be used to obtain the structural abnormality degree of each segmented area in the 3D model of the cable-stayed bridge. The larger the structural abnormality degree, the more likely the segmented area is an abnormal segmented area with low model accuracy. Therefore, based on the structural abnormality degree, the abnormal segmented area can be screened out from all the segmented areas. Subsequently, the abnormal segmented area can be optimized and repaired to improve the final quality of the 3D model of the cable-stayed bridge.

[0106] Preferably, in one embodiment of the present invention, a segmented area whose structural abnormality is greater than a preset abnormality threshold is taken as an abnormal segmented area, wherein the preset abnormality threshold is set to 0.7, and the specific value of the preset abnormality threshold can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0107] Step S3: The distance from each data point in each segmented area to each plane of the three-dimensional coordinate system is used as the distance parameter of each data point in each segmented area with respect to each plane; any abnormal segmented area is used as the target abnormal segmented area, and the segmented area adjacent to the target abnormal segmented area is used as the reference segmented area, and the structural independence of the target abnormal segmented area is obtained according to the difference in the distribution of the distance parameters between the data points in the target abnormal segmented area and the data points in each reference segmented area with respect to the same plane; the repair fineness of the target abnormal segmented area is obtained according to the structural abnormality and structural independence of the target abnormal segmented area, and the number of data points in the target abnormal segmented area.

[0108] In traditional methods, the three-dimensional model is usually interpolated to achieve model optimization. However, due to the complex structure of the cable-stayed bridge, in the three-dimensional model of the cable-stayed bridge, the structural differences between different abnormal segmentation areas and the surrounding segmentation areas are different, and the optimization accuracy required for different abnormal segmentation areas is different. For a certain abnormal segmentation area, the greater the structural difference between the abnormal segmentation area and the surrounding segmentation areas, the more detailed the optimization of the abnormal segmentation area is required. Therefore, the embodiment of the present invention first uses the distance from each data point in each segmentation area to each plane of the three-dimensional coordinate system as the distance parameter of each data point in each segmentation area with respect to each plane, so as to provide a data basis for the subsequent analysis of the structural differences between the abnormal segmentation area and the surrounding segmentation areas.

[0109] It should be noted that the distance parameter of a data point about a plane can also be understood as the absolute value of the coordinate of the data point on the coordinate axis perpendicular to the plane. For example, the distance parameter of a data point about the xoy plane is the absolute value of the z coordinate of the data point.

[0110] Then, any abnormal segmentation area is analyzed, and any abnormal segmentation area is taken as the target abnormal segmentation area, and the segmentation area adjacent to the target abnormal segmentation area is taken as the reference segmentation area of ​​the target abnormal segmentation area. The greater the difference in distance parameters between the data points in the target abnormal segmentation area and the data points in the reference segmentation area about the same plane, the greater the difference in structural features between the target abnormal segmentation area and the surrounding segmentation areas. Therefore, the difference in distribution of distance parameters between the data points in the target abnormal segmentation area and the data points in each reference segmentation area about the same plane can be analyzed, and the difference in structural features between the target abnormal segmentation area and the surrounding segmentation areas can be reflected by the obtained structural independence. The greater the structural independence, the greater the difference in structural features between the target abnormal segmentation area and the surrounding segmentation areas, that is, the more independent the structure of the target abnormal segmentation area is relative to the surrounding segmentation areas. Subsequently, the structural independence and structural abnormality can be combined to accurately analyze the degree of optimization of the target abnormal segmentation area.

[0111] Preferably, in one embodiment of the present invention, the method for acquiring the structural independence of the target abnormal segmentation region specifically includes:

[0112] Firstly, the target abnormal segmentation area or any reference segmentation area is taken as the area to be analyzed, and the average value of the distance parameters of all data points in the area to be analyzed with respect to each plane is taken as the overall distance parameter of the area to be analyzed with respect to each plane; the standard deviation of the distance parameters of all data points in the area to be analyzed with respect to each plane is taken as the distance distribution discreteness of the area to be analyzed with respect to each plane.

[0113] The same method as above can be used to obtain the overall distance parameter and distance distribution dispersion of the target abnormal segmentation region with respect to each plane, as well as the overall distance parameter and distance distribution dispersion of each reference segmentation region with respect to each plane.

[0114] Then, any reference segmentation area is taken as the target reference segmentation area, and the absolute value of the difference in the overall distance parameters between the target abnormal segmentation area and the target reference segmentation area with respect to each plane is taken as the first distribution difference of the target reference segmentation area with respect to each plane; the absolute value of the difference in the distance distribution discreteness between the target abnormal segmentation area and the target reference segmentation area with respect to each plane is taken as the second distribution difference of the target reference segmentation area with respect to each plane. The larger the first distribution difference and the second distribution difference, the greater the structural difference between the target abnormal segmentation area and the target reference segmentation area at each plane perspective.

[0115] Furthermore, the first distribution difference and the second distribution difference of the target reference segmentation region with respect to each plane are integrated to obtain the comprehensive distribution difference of the target reference segmentation region with respect to each plane; the accumulated value of the comprehensive distribution difference of the target reference segmentation region with respect to all planes is taken as the splitting degree of the target reference segmentation region. The greater the splitting degree, the greater the structural difference between the target reference segmentation region and the target abnormal segmentation region.

[0116] In an embodiment of the present invention, the sum or product of the first distribution difference and the second distribution difference of the target reference segmentation area with respect to each plane may be used as the comprehensive distribution difference of the target reference segmentation area with respect to each plane to achieve the integration of the two, which is not limited here.

[0117] As an example, in one embodiment of the present invention, the expression of the splitting degree of the target reference segmentation region may be specifically, for example, as follows:

[0118]

[0119] G j =|L j -L ′ j |+|P j -P j ′ |

[0120] Among them, H represents the degree of segmentation of the target reference segmentation area; G j represents the comprehensive distribution difference of the target reference segmentation area with respect to the jth plane; L j represents the overall distance parameter of the target abnormal segmentation region with respect to the jth plane; L ′ j represents the overall distance parameter of the target reference segmentation area with respect to the jth plane; P j represents the distance distribution discreteness of the target abnormal segmentation area about the jth plane; P j ′ represents the distance distribution discreteness of the target reference segmentation area about the jth plane; |L j -L ′ j | represents the first distribution difference of the target reference segmentation area with respect to the jth plane; |P j -P j ′ | represents the second distribution difference of the target reference segmentation area with respect to the jth plane.

[0121] Finally, the splitting degree of each reference segmentation region can be obtained by the same method as above, and then the average value of the splitting degree of all reference segmentation regions can be normalized to obtain the structural independence of the target abnormal segmentation region.

[0122] As an example, in one embodiment of the present invention, the expression of the structural independence of the target abnormal segmentation region can be specifically, for example, as follows:

[0123]

[0124] Among them, Q represents the structural independence of the target abnormal segmentation area; H n It represents the degree of segmentation of the nth reference segmentation region of the target abnormal segmentation region; N represents the number of reference segmentation regions of the target abnormal segmentation region; norm() represents the normalization function.

[0125] The greater the structural abnormality and structural independence of the target abnormal segmentation region, the more abnormal the model structure of the target abnormal segmentation region is, and the more independent the structure of the target abnormal segmentation region is relative to the surrounding segmentation regions, which means that more detailed model optimization of the target abnormal segmentation region is needed. At the same time, if the volume of the target abnormal segmentation region is smaller, it means that the target abnormal segmentation region is more likely to be a structure with higher precision requirements, and more detailed model optimization is also needed. Therefore, the structural abnormality and structural independence of the target abnormal segmentation region, as well as the number of data points in the target abnormal segmentation region, can be analyzed. The accuracy of model optimization and repair of the target abnormal segmentation region can be reflected by the obtained repair detail. The greater the repair detail, the more detailed optimization and repair of the target abnormal segmentation region is needed.

[0126] Preferably, in one embodiment of the present invention, the method for obtaining the restoration detail of the target abnormal segmentation area specifically includes:

[0127] The fewer the number of data points in the target segmentation area, the smaller the volume of the target segmentation area. Therefore, the number of data points in the target abnormal segmentation area can be negatively correlated to obtain the repair detail factor of the target abnormal segmentation area. Then, the structural abnormality, structural independence and repair detail factor of the target abnormal segmentation area are integrated and normalized to obtain the repair detail of the target abnormal segmentation area.

[0128] In the embodiment of the present invention, the integration of the three can be achieved by calculating the sum or product of the structural abnormality, structural independence and repair refinement factor of the abnormal segmented region, which is not limited here.

[0129] As an example, in one embodiment of the present invention, the expression of the restoration detail of the target abnormal segmentation area can be specifically, for example, as follows:

[0130]

[0131] Among them, U represents the restoration detail of the target abnormal segmentation area; F represents the structural abnormality of the target segmentation area; Q represents the structural independence of the target abnormal segmentation area; M represents the number of data points in the target abnormal segmentation area, M≠0; It indicates the repair refinement factor of the target abnormal segmentation area; norm() indicates the normalization function.

[0132] It should be noted that in other embodiments of the present invention, negative correlation mapping may be achieved through other basic mathematical operations, which will not be elaborated herein.

[0133] The same method as above can be used to obtain the repair detail of each abnormal segmentation area in the three-dimensional model of the cable-stayed bridge.

[0134] Step S4: Based on the restoration detail, each abnormal segmentation area of ​​the three-dimensional model is optimized to obtain an optimized three-dimensional model.

[0135] In the three-dimensional model of the cable-stayed bridge, the repair details of different abnormal segmentation areas are different, and the accuracy of model optimization and repair of different abnormal segmentation areas is also different. Therefore, based on the repair detail, each abnormal segmentation area of ​​the three-dimensional model can be optimized to different degrees to obtain an optimized three-dimensional model. While improving the model optimization efficiency, it can also ensure that the quality of the optimized three-dimensional model is high.

[0136] Preferably, in one embodiment of the present invention, the method for obtaining the optimized three-dimensional model specifically includes:

[0137] If the restoration detail of the target abnormal segmentation area is greater than the preset detail threshold, it means that the target abnormal segmentation area needs more detailed optimization. Then, in the embodiment of the present invention, based on the restoration detail, the reshoot distance and the number of reshoot positions of the target abnormal segmentation area are first obtained. By using the close-up photography 3D modeling technology, the area of ​​the cable-stayed bridge to be measured represented by the target abnormal segmentation area is reshoot and modeled by evenly arranging cameras to obtain a high-precision 3D model of the target abnormal segmentation area. The target abnormal segmentation area and the high-precision 3D model are fused to obtain an optimized segmentation area of ​​the target abnormal segmentation area, thereby achieving more detailed model optimization of the target abnormal segmentation area. The number of cameras is the number of reshoot positions, and the distance between each camera and the area of ​​the cable-stayed bridge to be measured represented by the target abnormal segmentation area is the reshoot distance. The close-up photography 3D modeling technology and the fusion of 3D models are both technical means well known to those skilled in the art, and are not described in detail here. The preset detail threshold is set to 0.6. The specific value of the preset detail threshold can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0138] Preferably, in one embodiment of the present invention, the method for obtaining the reshoot distance and the number of reshoot positions of the target abnormal segmentation area specifically includes:

[0139] The product of the restoration detail of the target abnormal segmentation area and the preset standard reshooting distance is used as the reshooting distance adjustment amount of the target abnormal segmentation area. The greater the restoration detail, the more detailed the optimization is needed, and a smaller reshooting distance is needed to reshoot more detailed image information. Therefore, the difference between the preset standard reshooting distance and the reshooting distance adjustment amount can be used as the reshooting distance of the target abnormal segmentation area. The preset standard reshooting distance is set to 3 meters. The specific value of the preset standard reshooting distance can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0140] The product of the restoration detail of the target abnormal segmentation area and the preset standard number of reshoot positions is rounded up to obtain the adjustment amount of the number of reshoot positions for the target abnormal segmentation area. The greater the restoration detail, the more detailed the optimization is needed, and more cameras are needed to reshoot more detailed image information. Therefore, the sum of the preset standard number of reshoot positions and the adjustment amount of the number of reshoot positions can be used as the number of reshoot positions for the target abnormal segmentation area, where the preset standard number of reshoot positions is set to 20. The specific value of the preset standard number of reshoot positions can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0141] As an example, in one embodiment of the present invention, the expressions for the reshoot distance and the number of reshoot positions of the target abnormal segmentation area may be specifically, for example, as follows:

[0142] R ′ =RU×R

[0143]

[0144] Among them, R ′ represents the reshoot distance of the target abnormal segmentation area; R represents the preset standard reshoot distance; U represents the repair detail of the target abnormal segmentation area; U×R represents the reshoot distance adjustment amount of the target abnormal segmentation area; S ′ represents the number of retake positions in the target abnormal segmentation area; S represents the number of preset standard retake positions; Indicates the adjustment amount of the number of retake positions of the target abnormal segmentation area; Indicates the upward value symbol.

[0145] If the restoration detail of the target abnormal segmentation area is not greater than the preset detail threshold, it means that the degree of abnormality of the target abnormal segmentation area is low and the structure is similar to the surrounding segmentation area. Therefore, the target abnormal segmentation area can be directly interpolated to obtain the optimized segmentation area of ​​the target abnormal segmentation area. Among them, the interpolation method can select the existing nearest neighbor interpolation algorithm or bilinear interpolation algorithm, etc., which will not be elaborated or limited here.

[0146] The above method can be used to obtain the optimized segmentation region of each abnormal segmentation region, and then the combination of the optimized segmentation region of each abnormal segmentation region and each non-abnormal segmentation region of the three-dimensional model is used as the optimized three-dimensional model.

[0147] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A cable-stayed bridge modeling method based on BIM technology, characterized in that: The method comprises: Acquire a three-dimensional model of the cable-stayed bridge to be tested, wherein the three-dimensional model includes a plurality of data points; The three-dimensional model is segmented to obtain a plurality of segmented regions, any one of the segmented regions is used as a target segmented region, and data points in the target segmented region are projected onto the plane of the three-dimensional coordinate system where the three-dimensional model is located, respectively, to obtain the projection region of the target segmented region on each plane and the projection data points in the projection region; according to the position distribution of the projection data points on the edge of the projection region of each plane and the position distribution of the projection data points in the projection region of each plane, the structural abnormality of the target segmented region is obtained; based on the structural abnormality, the abnormal segmented region is screened out from all the segmented regions; The distance from each data point in each segmented area to each plane of the three-dimensional coordinate system is used as the distance parameter of each data point in each segmented area with respect to each plane; any abnormal segmented area is used as the target abnormal segmented area, and the segmented area adjacent to the target abnormal segmented area is used as the reference segmented area, and the structural independence of the target abnormal segmented area is obtained according to the difference in the distribution of the distance parameters between the data points in the target abnormal segmented area and the data points in each reference segmented area with respect to the same plane; the restoration fineness of the target abnormal segmented area is obtained according to the structural abnormality and the structural independence of the target abnormal segmented area, and the number of data points in the target abnormal segmented area; Based on the restoration detail, each abnormal segmentation region of the three-dimensional model is optimized to obtain an optimized three-dimensional model.

2. A cable-stayed bridge modeling method based on BIM technology according to claim 1, characterized in that: The obtaining of the structural abnormality of the target segmented region comprises: Taking any plane of the three-dimensional coordinate system where the three-dimensional model is located as the target plane, taking the projection area of ​​the target segmentation area on the target plane as the target projection area, performing curve fitting on the projection data points on the edge of the target projection area, and obtaining the fitting projection data points of each projection data point on the edge of the target projection area; According to the distribution of the distance between the projection data points on the edge of the target projection area and the fitting projection data points of the projection data points, the irregularity of the projection edge of the target segmentation area on the target plane is obtained; In the target projection area, a preset neighborhood is constructed with each projection data point as the center, and the average value of the Euclidean distance between each projection data point in the target projection area and all other projection data points in the preset neighborhood is normalized to obtain the outlier degree of each projection data point, and the projection data points with outlier degrees greater than the preset outlier threshold are regarded as isolated projection data points; Obtaining the projection area irregularity of the target segmentation area on the target plane according to the distribution of the outlier degree of the projection data points in the target projection area and the number of isolated projection data points in the target projection area; The projection edge irregularity and the projection area irregularity are integrated to obtain the integrated irregularity of the target segmentation area in the target plane; the accumulated values ​​of the integrated irregularities of the target segmentation area in all planes are normalized to obtain the structural abnormality of the target segmentation area.

3. The cable-stayed bridge modeling method based on BIM technology according to claim 2 is characterized in that: The step of obtaining the projection edge irregularity of the target segmentation region on the target plane includes: The Euclidean distance between each projection data point on the edge of the target projection area and the fitted projection data point of each projection data point is used as the fitting error distance of each projection data point on the edge of the target projection area; The standard deviation of the fitting error distance of all projection data points on the edge of the target projection area is used as the first edge irregularity parameter of the target projection area; Extracting a maximum projection data point from all projection data points on the edge of the target projection area, wherein the fitting error distance of the maximum projection data point is greater than the fitting error distances of a preset number of other projection data points on the edge of the target projection area that are closest to the maximum projection data point; The standard deviation of the fitting error distance of all maximum projection data points is used as the second edge irregularity parameter of the target projection area; On the edge of the target projection area, the number of projection data points between each maximum projection data point and the next adjacent maximum projection data point is used as the interval distance coefficient of each maximum projection data point; The standard deviation of the interval distance coefficients of all maximum projection data points is used as the third edge irregularity parameter of the target projection area; The first edge irregularity parameter, the second edge irregularity parameter and the third edge irregularity parameter are integrated to obtain the projection edge irregularity of the target segmentation area on the target plane.

4. The cable-stayed bridge modeling method based on BIM technology according to claim 2 is characterized in that: The step of obtaining the irregularity of the projection area of ​​the target segmentation area on the target plane includes: The range of the outlier degree of all projection data points in the target projection area and the number of isolated projection data points in the target projection area are combined to obtain the projection area irregularity of the target segmentation area on the target plane.

5. The cable-stayed bridge modeling method based on BIM technology according to claim 1 is characterized in that: The step of screening out abnormal segmentation regions from all segmentation regions based on the structural abnormality comprises: The segmented region whose structural abnormality is greater than a preset abnormality threshold is regarded as an abnormal segmented region.

6. The cable-stayed bridge modeling method based on BIM technology according to claim 1 is characterized in that: The method of obtaining the structural independence of the target abnormal segmentation region includes: The target abnormal segmentation area or any reference segmentation area is used as the area to be analyzed, and the average value of the distance parameter of all data points in the area to be analyzed with respect to each plane is used as the overall distance parameter of the area to be analyzed with respect to each plane; the standard deviation of the distance parameter of all data points in the area to be analyzed with respect to each plane is used as the distance distribution dispersion of the area to be analyzed with respect to each plane; Taking any reference segmentation region as the target reference segmentation region, taking the absolute value of the difference between the target abnormal segmentation region and the target reference segmentation region on each plane as the first distribution difference of the target reference segmentation region on each plane; taking the absolute value of the difference between the target abnormal segmentation region and the target reference segmentation region on each plane as the second distribution difference of the target reference segmentation region on each plane; The first distribution difference and the second distribution difference of the target reference segmentation region with respect to each plane are integrated to obtain a comprehensive distribution difference of the target reference segmentation region with respect to each plane; and the accumulated value of the comprehensive distribution difference of the target reference segmentation region with respect to all planes is used as the splitting degree of the target reference segmentation region; The average values ​​of the splitting degrees of all reference segmented regions are normalized to obtain the structural independence of the target abnormal segmented region.

7. The cable-stayed bridge modeling method based on BIM technology according to claim 1 is characterized in that: The method of obtaining the restoration detail of the target abnormal segmentation area includes: Negative correlation mapping is performed on the number of data points in the target abnormal segmentation area to obtain the repair fineness factor of the target abnormal segmentation area; The structural abnormality, structural independence and repair detail factor of the target abnormal segmentation region are integrated and normalized to obtain the repair detail of the target abnormal segmentation region.

8. The cable-stayed bridge modeling method based on BIM technology according to claim 1 is characterized in that: The obtaining of the optimized three-dimensional model comprises: If the repair detail of the target abnormal segmentation area is greater than a preset detail threshold, then based on the repair detail, the reshoot distance and the number of reshoot positions of the target abnormal segmentation area are obtained, and the area of ​​the cable-stayed bridge to be tested represented by the target abnormal segmentation area is reshoot and modeled by evenly arranging cameras using close-range photography 3D modeling technology to obtain a high-precision 3D model of the target abnormal segmentation area, and the target abnormal segmentation area and the high-precision 3D model are fused to obtain an optimized segmentation area of ​​the target abnormal segmentation area, wherein the number of cameras is the number of reshoot positions, and the distance between each camera and the area of ​​the cable-stayed bridge to be tested represented by the target abnormal segmentation area is the reshoot distance; If the restoration detail of the target abnormal segmentation region is not greater than a preset detail threshold, interpolation processing is performed on the target abnormal segmentation region to obtain an optimized segmentation region of the target abnormal segmentation region; The combination of the optimized segmented regions of each abnormal segmented region of the three-dimensional model and each non-abnormal segmented region is used as the optimized three-dimensional model.

9. The cable-stayed bridge modeling method based on BIM technology according to claim 8 is characterized in that: The obtaining of the reshoot distance and the number of reshoot positions of the target abnormal segmentation area based on the repair detail comprises: The product value of the restoration detail of the target abnormal segmentation area and the preset standard reshoot distance is used as the reshoot distance adjustment amount of the target abnormal segmentation area, and the difference between the preset standard reshoot distance and the reshoot distance adjustment amount is used as the reshoot distance of the target abnormal segmentation area; The product value of the repair detail of the target abnormal segmentation area and the preset standard number of re-shooting positions is rounded up to obtain the adjustment amount of the number of re-shooting positions in the target abnormal segmentation area, and the sum of the preset standard number of re-shooting positions and the adjustment amount of the number of re-shooting positions is used as the number of re-shooting positions in the target abnormal segmentation area.

10. The cable-stayed bridge modeling method based on BIM technology according to claim 1, characterized in that: The obtaining of a plurality of segmented regions comprises: All data points in the three-dimensional model are clustered to obtain different clusters, and the area formed by each cluster is used as the segmentation area.

Images