Intelligent management method for high-speed railway expansion construction based on BIM

Abstract

The present invention relates to the technical field of point cloud data processing, and in particular to a BIM-based intelligent management method for high-speed rail expansion construction, including: obtaining point cloud data of several directions at several locations in several construction stages during the expansion of the high-speed rail; obtaining the stress degree of each data point in each point cloud data; obtaining the contribution of each data point in each voxel block through the change of stress degree between adjacent data points, the angle between adjacent stress planes, the distance between data points, and the normal vector of the data point; obtaining the first contribution of the voxel block, comparing the first contribution with a preset threshold, obtaining a key voxel block, matching the key voxel blocks in all point cloud data under the same construction stage, and obtaining a BIM model of each construction stage. The present invention improves the speed of constructing BIM models in different construction stages, and can better analyze the stress distribution in the high-speed rail expansion process through the BIM model.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data processing, and in particular to a BIM-based intelligent management method for high-speed railway expansion construction. Background Art

[0002] Building Information Modeling (BIM) is a tool for architecture, engineering and civil engineering. The core of BIM is to establish a virtual three-dimensional model of the building project and use digital technology to provide this model with a complete and actual building project information database. The BIM-based intelligent management method for high-speed rail expansion construction combines digital modeling and information technology to improve the efficiency of high-speed rail expansion construction.

[0003] When obtaining the BIM model of the high-speed railway expansion construction phase, the existing method only obtains the BIM model of the construction phase by matching the point cloud data of multiple positions and multiple directions in the same construction phase. Since the stress levels of data points in different point cloud data are different, the contribution of different data points to the construction of the BIM model is also different. If the stress levels and contributions of the data points are not considered and the point cloud data of all positions and directions in the same construction phase are directly matched, the huge amount of point cloud data will lead to a slow construction speed of the BIM model, and it is not convenient to analyze the stress distribution during the high-speed railway expansion process through the BIM model. Summary of the invention

[0004] In order to solve the problem that the BIM model is constructed slowly and it is inconvenient to analyze the stress distribution problem in the high-speed railway expansion process through the BIM model, the present invention provides a BIM-based intelligent management method for high-speed railway expansion construction.

[0005] The BIM-based high-speed railway expansion construction intelligent management method of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a BIM-based intelligent management method for high-speed railway expansion construction, the method comprising the following steps:

[0007] At each construction stage during the high-speed rail expansion process, point cloud data in different directions of each location is obtained, wherein the point cloud data includes a plurality of data points;

[0008] According to the coordinate distribution of data points in the point cloud data at the same position and the same direction at different construction stages, the two-dimensional fluctuation curve of each data point in each point cloud data is obtained; according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves, the stress degree of each data point in each point cloud data is obtained;

[0009] The point cloud data is segmented to obtain a number of voxel blocks, and the segmentation line of each data point in each voxel block is obtained by the force fluctuation of all data points on the straight line passing through the data point in the voxel block;

[0010] According to the straight line parallel to the dividing line in the voxel block and the force degree of the data points on the straight line, the translation position of the dividing line is obtained, and the dividing line is translated by the translation position to obtain several force planes of the data points to which the dividing line belongs;

[0011] According to the change of force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data points, the contribution of each data point in each voxel block is obtained;

[0012] The first contribution of the voxel block is obtained by the contribution of all data points in the voxel block. The first contribution is compared with the preset threshold to obtain the key voxel block. The key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage.

[0013] Furthermore, the method of obtaining a two-dimensional fluctuation curve of each data point in each point cloud data according to the coordinate distribution of the data points in the point cloud data at the same position and the same direction at different construction stages includes the following specific steps:

[0014] The point cloud data of any position and any direction at all construction stages are recorded as the first point cloud data;

[0015] Record any first point cloud data as target first point cloud data;

[0016] Any data point in the target first point cloud data is recorded as data points;

[0017] The first point cloud data of the target The coordinates of the data point are recorded as the first coordinates. In the other first point cloud data except the target first point cloud data, the data point closest to the first coordinates is recorded as the first point cloud data. The matching data points of the data points are The coordinates of the data points and their matching data points are fitted in the order of the construction stages to obtain a four-dimensional fluctuation curve. The four-dimensional fluctuation curve is subjected to PCA dimensionality reduction to obtain a two-dimensional fluctuation curve, which is recorded as A two-dimensional fluctuation curve of 1 data point.

[0018] Furthermore, the stress degree of each data point in each point cloud data is obtained according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves, and the specific steps include the following:

[0019] The cumulative value of the Pearson correlation coefficient between the data point and the two-dimensional fluctuation curve of the data points in its neighborhood and the fluctuation degree of the two-dimensional fluctuation curve of the data point are fused to obtain the stress degree of the data point in the point cloud data; the cumulative value of the Pearson correlation coefficient and the fluctuation degree of the two-dimensional fluctuation curve of the data point are directly proportional to the stress degree.

[0020] Furthermore, the point cloud data is segmented to obtain a plurality of voxel blocks, and a segmentation line for each data point in each voxel block is obtained by the force fluctuation of all data points on a straight line passing through the data point in the voxel block, and the specific steps include the following:

[0021] Perform an octree segmentation on the target first point cloud data to obtain eight voxel blocks of the target first point cloud data. Any voxel block is recorded as the target voxel block, and any data point in the target voxel block is recorded as the first voxel block. data points, obtained in the target voxel block All the straight lines with data points are obtained, and the variance of the stress degree of all data points in each straight line is obtained. The straight line corresponding to the minimum variance of the stress degree of all data points in the straight line is recorded as The dividing line of the data points.

[0022] Furthermore, the step of obtaining the translation position of the segmentation line according to the straight line in the voxel block parallel to the segmentation line and the force degree of the data points on the straight line includes the following specific steps:

[0023] In the target voxel block, The data points in the neighborhood set of the data points are all recorded as the first data points. The neighborhood set is a set of data points within a preset neighborhood range. The straight line parallel to the dividing line of the data points is recorded as The neighborhood segmentation line of the data point is obtained The absolute value of the difference between the average force degree of all data points on the dividing line of the first data point and the average force degree of all data points on each neighborhood dividing line is taken, and the position of the neighborhood dividing line corresponding to the minimum absolute value of the difference is taken as the first The translation position of the dividing line of the data points.

[0024] Furthermore, the step of translating the segmentation line by translating the position to obtain a plurality of force planes of the data points to which the segmentation line belongs includes the following specific steps:

[0025] The first The dividing line of the data point is translated to the The translation position of the dividing line of the data point is obtained A force plane of data points is obtained by The same method as the translation position of the segmentation line of the data point is used to obtain the new translation position, and the segmentation line after translation is further translated until the data point corresponding to the newly obtained translation position is at the boundary of the target voxel block, and the first Several force planes for each data point.

[0026] Furthermore, the contribution of each data point in each voxel block is obtained according to the change in the force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data point, and the specific steps include the following:

[0027] In the voxel block, the cumulative value of the angle cosine value between adjacent force planes of the data point, the change in the force degree between the data point and the adjacent data points, and the distance between the data point and the adjacent data points are obtained; the line connecting the data point and the adjacent data points and the acute angle cosine value between the normal vector of the data point are obtained; the force degree change, the distance, and the acute angle cosine value are fused to obtain the structural distribution factor of the data point and the adjacent data points, and the distance is inversely proportional to the structural distribution factor; the cumulative value of the structural distribution factor of the data point and all adjacent data points within a preset range is recorded as a first cumulative value; the cumulative value of the angle cosine value between adjacent force planes of the data point is recorded as a second cumulative value; the first cumulative value and the second cumulative value are fused to obtain the contribution of the data point, and the first cumulative value and the second cumulative value are directly proportional to the contribution.

[0028] Furthermore, the step of obtaining the first contribution of the voxel block by the contribution of all data points in the voxel block includes the following specific steps:

[0029] The contribution of all data points in the target voxel block is accumulated to obtain the first contribution of the target voxel block.

[0030] Furthermore, the step of comparing the first contribution with a preset threshold to obtain a key voxel block includes the following specific steps:

[0031] If the normalized value of the first contribution of the target voxel block is less than the preset first threshold, the target voxel block is taken as a key voxel block. If the normalized value of the first contribution of the target voxel block is greater than or equal to the preset first threshold, the target voxel block is further divided into an octree to obtain eight voxel blocks of the target voxel block, all of which are recorded as first voxel blocks. The first contribution of each first voxel block is obtained. If the normalized value of the first contribution of the first voxel block is less than the preset first threshold, the first voxel block is taken as a key voxel block. If the normalized value of the first contribution of the first voxel block is greater than or equal to the preset first threshold, the first voxel block is further divided into an octree to obtain eight voxel blocks of the first voxel block, and the first contribution of each of the eight voxel blocks of the first voxel block is obtained and compared with the preset first threshold until it is less than the preset first threshold.

[0032] Furthermore, the key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage, including the following specific steps:

[0033] All key voxel blocks in the first point cloud data of the target are obtained, the construction stage where the first point cloud data of the target is located is recorded as the target construction stage, and the key voxel blocks in all point cloud data of the target construction stage are matched by the ICP algorithm to obtain the BIM model of the target construction stage.

[0034] The beneficial effect of the technical solution of the present invention is: after obtaining the point cloud data of several directions at several positions in several construction stages during the expansion of the railway, the present invention obtains the two-dimensional fluctuation curve of each data point in the point cloud data through the coordinates of the data points in the point cloud data at the same position and the same direction, which reflects the displacement change of the data points at the same position with the change of the construction stage, and then obtains the stress degree of each data point in the point cloud data. The stress degree can more accurately reflect the stress degree of the data point by analyzing the fluctuation of the two-dimensional fluctuation curve and the correlation between the two-dimensional fluctuation curves of adjacent data points, so that the BIM model constructed subsequently can better analyze the stress distribution in the process of high-speed railway expansion, and the stress degree changes between adjacent data points and the angles between adjacent stress planes can be used to analyze the stress distribution of the data point. , the distance between data points, and the normal vector of the data point, and the contribution of each data point in each voxel block is obtained, and then the first contribution of the voxel block is obtained. The first contribution can better reflect the contribution of the segmented voxel block in constructing the BIM model, and then the key voxel block is obtained according to the size of the first contribution. Finally, the key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage. By obtaining the key voxel blocks for matching, the problem of slow BIM model construction speed due to the huge amount of point cloud data during matching is reduced. At the same time, by analyzing the stress degree of the data points, the constructed BIM model can better analyze the stress distribution during the high-speed rail expansion process, and can better carry out subsequent construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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.

[0036] Figure 1 A flowchart of a method for intelligent management of high-speed railway expansion construction based on BIM provided by one embodiment of the present invention;

[0037] Figure 2 A flowchart for obtaining a BIM model in the construction phase provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of the intelligent management method for high-speed railway expansion construction based on BIM proposed by the present invention are described in detail below 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.

[0039] 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.

[0040] The specific scheme of the intelligent management method for high-speed railway expansion construction based on BIM provided by the present invention is described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 and Figure 2 , which shows a flowchart of the steps of a BIM-based high-speed railway expansion construction intelligent management method provided by an embodiment of the present invention and a flowchart of obtaining a BIM model in the construction stage, the method comprising the following steps:

[0042] Step S001: At each construction stage during the high-speed railway expansion process, point cloud data of different directions of each position is obtained, and the point cloud data includes a plurality of data points.

[0043] It should be noted that the general logic of this embodiment is to obtain point cloud data at different positions and directions, perform voxel downsampling on the obtained point cloud data to obtain simplified point cloud data, i.e., voxel blocks, and perform matching through the simplified point cloud data. By matching, multiple point cloud data are combined into a complete point cloud data to obtain a BIM model to facilitate subsequent construction. Before starting the analysis, collect data first.

[0044] Specifically, at each construction stage during the high-speed rail expansion process, point cloud data of different directions at each location is obtained, and the point cloud data includes several data points, as follows:

[0045] The laser radar equipment is used to obtain point cloud data in several directions at different locations during several construction stages of the high-speed railway expansion process.

[0046] It should be noted that each construction stage corresponds to multiple positions, each position corresponds to multiple directions, and the point cloud data contains a number of data points. The specific positions and directions are not fixed in this embodiment, and the model can be completely constructed.

[0047] So far, point cloud data of several directions and several locations at several construction stages during the expansion of the high-speed railway have been obtained.

[0048] Step S002: Obtain a two-dimensional fluctuation curve for each data point in each point cloud data according to the coordinate distribution of data points in the point cloud data at the same position and the same direction at different construction stages; obtain the stress degree of each data point in each point cloud data according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves.

[0049] It should be noted that the model obtained in this embodiment is used for stress distribution analysis of high-speed rail, so as to avoid the problem of ground settlement and engineering structure deformation of the original high-speed rail caused by construction during the subsequent expansion process, which affects the original high-speed rail performance. When constructing the BIM model of the high-speed rail, it is necessary to obtain the point cloud data of the high-speed rail at different positions and directions, and obtain a complete point cloud data by matching the point cloud data at different positions and directions to obtain the required BIM model. The point cloud data is too large, which will affect the accuracy of the matching calculation. Therefore, it is necessary to divide the obtained point cloud data into voxel blocks by voxel downsampling. In the process of voxel downsampling, it is necessary to consider the stress of the data points in the point cloud data, so as to build a more accurate model. In order to obtain the stress degree of the data points in the point cloud data, the stress degree of each data point in the point cloud data is obtained by analyzing the fluctuation of the data points at the same position and direction in different construction stages, and the correlation between the fluctuations of different data points; the point cloud data at the same position and direction in different construction stages is ideally unchanged, and the point cloud data at the same position and direction in different construction stages may be affected during the expansion of the high-speed rail.

[0050] Specifically, according to the coordinate distribution of data points in the point cloud data at the same position and the same direction at different construction stages, a two-dimensional fluctuation curve of each data point in each point cloud data is obtained. As a specific example, a specific method for obtaining the two-dimensional fluctuation curve is as follows:

[0051] The point cloud data of any position and any direction in all construction stages are recorded as the first point cloud data.

[0052] Any first point cloud data is recorded as the target first point cloud data.

[0053] Any data point in the target first point cloud data is recorded as data points.

[0054] The first point cloud data of the target The coordinates of the data point are recorded as the first coordinates. In the other first point cloud data except the target first point cloud data, the data point closest to the first coordinates is recorded as the first point cloud data. The matching data points of the data points are The coordinates of the data points and their matching data points are fitted in the order of the construction stages to obtain a four-dimensional fluctuation curve. The four-dimensional fluctuation curve is reduced by PCA (Principal Components Analysis) to obtain a two-dimensional fluctuation curve, which is recorded as A two-dimensional fluctuation curve of 1 data point.

[0055] It should be noted that the four dimensions in the four-dimensional wave curve are the spatial coordinates plus the construction stage sequence. The coordinates of the data points and their matching data points are fitted in the order of the construction stages to obtain a four-dimensional fluctuation curve, and PCA dimension reduction is performed to obtain a two-dimensional fluctuation curve, which is an existing method and will not be repeated in this embodiment; the two-dimensional fluctuation curve contains several points.

[0056] Preferably, the stress degree of each data point in each point cloud data is obtained according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves, as follows:

[0057] The cumulative value of the Pearson correlation coefficient between the data point and the two-dimensional fluctuation curve of the data points in its neighborhood and the fluctuation degree of the two-dimensional fluctuation curve of the data point are fused to obtain the stress degree of the data point in the point cloud data; the cumulative value of the Pearson correlation coefficient and the fluctuation degree of the two-dimensional fluctuation curve of the data point are directly proportional to the stress degree.

[0058] As a specific example, the specific method for obtaining the force degree is as follows:

[0059]

[0060] In the formula, For the The variance of the slopes of all points in the two-dimensional fluctuation curve of the data points. It should be noted that obtaining the slope of the midpoint of the curve and then obtaining the variance of the slopes of all points is an existing method, which will not be described in detail in this embodiment; For the The number of data points in the neighborhood set of the data point, the specific method of obtaining the neighborhood set is as follows: in the target first point cloud data, obtain the neighborhood set corresponding to the first point cloud data. The data point closest to data points, will be The data point closest to The set of data points is denoted as The neighborhood set of data points, is a preset first value. Narrate, if If the number of the nearest data points to a data point is less than 26, the set of actual data points is used as the neighborhood set; For the The neighborhood set of data points The two-dimensional fluctuation curve of the data point The Pearson correlation coefficient of the two-dimensional fluctuation curve of the data points is as follows: The two-dimensional fluctuation curve of the data points and the The two-dimensional fluctuation curve method for each data point is the same, and will not be described in detail in this embodiment; To take the absolute value, For the The force level of each data point.

[0061] It should be noted that The larger the two-dimensional fluctuation curve, the greater the fluctuation degree, indicating that the The more obvious the displacement of a data point and its matching data point over time, the more obvious the corresponding deformation, and the greater the force on the data point; since deformation will not only affect one data point, the consistency of the change of the two-dimensional fluctuation curve of the data point and its neighboring data points is analyzed. The larger it is, the more similar the changes of the data point are to those of the surrounding neighborhood data points, that is, the more correlated they are. The fluctuation degree of the two-dimensional fluctuation curve is more likely to be caused by deformation, and the more it can reflect the stress condition of the data point.

[0062] At this point, the force degree of each data point in each point cloud data is obtained.

[0063] Step S003, divide the point cloud data into several voxel blocks, and obtain the dividing line of each data point in each voxel block through the force fluctuation of all data points on the straight line passing through the data point in the voxel block; obtain the translation position of the dividing line according to the straight line parallel to the dividing line in the voxel block and the force degree of the data points on the straight line, translate the dividing line according to the translation position, and obtain several force planes of the data points belonging to the dividing line; obtain the contribution of each data point in each voxel block according to the change of force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data point.

[0064] It should be noted that the stress level of each data point in each point cloud data obtained above can reflect the deformation of the data point. The data points collected during the construction process have not been deformed and their contribution to the overall model may be small. The acquired point cloud data cannot be matched directly by the stress level. It is also necessary to analyze the contribution of the data points in the voxel block after the initial segmentation of the point cloud data in order to better determine the segmentation accuracy. In order to better obtain the contribution, it is necessary to consider the angle change of the force plane and the distance between the data points when the segmentation line of the data point in the voxel block is translated, and the contribution of the data point in the voxel block is obtained in combination with the change in the stress level between the data points, so as to avoid the loss of key information due to the acquired voxel block, which affects the accuracy of the matching during matching.

[0065] Specifically, the point cloud data is segmented to obtain a number of voxel blocks, and the segmentation line of each data point in each voxel block is obtained by the force fluctuation of all data points on the straight line passing through the data point in the voxel block. As a specific example, the specific method for obtaining the segmentation line of the data point is as follows:

[0066] The target first point cloud data is divided into an octree to obtain eight voxel blocks of the target first point cloud data. Any voxel block is recorded as the target voxel block, and any data point in the target voxel block is recorded as the first voxel block. data points, obtained in the target voxel block All the straight lines with data points are obtained, and the variance of the stress degree of all data points in each straight line is obtained. The straight line corresponding to the minimum variance of the stress degree of all data points in the straight line is recorded as It should be noted that the smaller the variance of the stress levels of all data points in the straight line, the closer the data points on the corresponding straight line are to the first data point. The more similar the force conditions between the data points are, the more likely the straight line is on the force plane. The dividing line is subsequently used to determine the force plane, which is a straight line on the force plane.

[0067] Specifically, according to the straight line parallel to the dividing line in the voxel block and the force degree of the data points on the straight line, the translation position of the dividing line is obtained, and the dividing line is translated according to the translation position to obtain several force planes of the data points to which the dividing line belongs. As a specific example, the specific method for obtaining several force planes of the data points is as follows:

[0068] In the target voxel block, The data points in the neighborhood set of the data point are all recorded as the first data point, and the data points that pass through the first data point and are connected to the first data point are recorded as the first data point. The straight line parallel to the dividing line of the data points is recorded as The neighborhood segmentation line of the data point is obtained The absolute value of the difference between the average force degree of all data points on the dividing line of the first data point and the average force degree of all data points on each neighborhood dividing line is taken, and the position of the neighborhood dividing line corresponding to the minimum absolute value of the difference is taken as the first The translation position of the dividing line of the data point is The dividing line of the data points is translated to the The translation position of the dividing line of the data point is obtained A force plane of data points is obtained by The same method as the translation position of the segmentation line of the data point is used to obtain the new translation position, and the segmentation line after translation is further translated until the data point corresponding to the newly obtained translation position is at the boundary of the target voxel block, and the first Several force planes for each data point.

[0069] Preferably, the contribution of each data point in each voxel block is obtained according to the change in the force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data point, as follows:

[0070] In the voxel block, the cumulative value of the angle cosine value between adjacent force planes of the data point, the change in the force degree between the data point and the adjacent data points, and the distance between the data point and the adjacent data points are obtained; the line connecting the data point and the adjacent data points and the acute angle cosine value between the normal vector of the data point are obtained; the force degree change, the distance, and the acute angle cosine value are fused to obtain the structural distribution factor of the data point and the adjacent data points, and the distance is inversely proportional to the structural distribution factor; the cumulative value of the structural distribution factor of the data point and all adjacent data points within a preset range is recorded as a first cumulative value; the cumulative value of the angle cosine value between adjacent force planes of the data point is recorded as a second cumulative value; the first cumulative value and the second cumulative value are fused to obtain the contribution of the data point, and the first cumulative value and the second cumulative value are directly proportional to the contribution.

[0071] As a specific example, the specific method of obtaining contribution is as follows:

[0072]

[0073]

[0074] In the formula, For the The number of force planes for each data point, For the The data point The force plane and The angle between the normal vectors of the force planes, is the cosine function, For the The number of data points in the neighborhood set of a data point, For the The data point and the The Euclidean distance between data points is The specific method of obtaining is as follows: In the target voxel block, obtain the The normal vector of the data point. It should be noted that the target voxel block is a part of the point cloud data. Obtaining the normal vector of the data point in the point cloud data is an existing method, which will not be described in detail in this embodiment. The data point and the The line between the data points is denoted as The first line of the data point The first line connecting the data points The acute angle between the normal vectors of the data points is recorded as , For the The stress level of each data point, For the The neighborhood set of data points The stress level of each data point, To take the absolute value, For the The contribution of each data point, is the linear normalization function, For the The data point and the The structural distribution factor of each data point.

[0075] It should be noted that Indicates The cumulative value of the angle change between adjacent force planes of data points. The larger the value, the greater the The distribution range of the stress conditions where the data points are located is more likely to be the influence of themselves. The more likely a data point is to be at the center of the force deformation, The bigger, the The greater the contribution of a data point, is the angle between adjacent force-bearing planes; By analyzing the The structural distribution of the local neighborhood of each data point determines the contribution of the data point. Indicates the change in stress between adjacent data points. represents the structural distribution factor, The smaller, and The larger the difference, the closer the corresponding data point is to the boundary of the structure distribution or the tip of the structure. When matching, the corresponding voxel block should lose less information. The greater the contribution of each data point, The smaller the The closer a data point is to a data point in the neighborhood set, the tighter the corresponding structure distribution is, and the greater the contribution is when matching.

[0076] At this point, the contribution of each data point in each voxel block is obtained.

[0077] Step S004: obtain the first contribution of the voxel block through the contribution of all data points in the voxel block, compare the first contribution with the preset threshold to obtain the key voxel block, match the key voxel blocks in all point cloud data in the same construction stage, and obtain the BIM model of each construction stage.

[0078] It should be noted that the contribution of each data point in the voxel block is obtained above. The key voxel block is obtained by obtaining the first contribution of the voxel block and adjusting the segmentation accuracy according to the first contribution of the voxel block. By matching the key voxel blocks in all point cloud data of the same construction stage, the BIM model of each construction stage in the high-speed rail expansion process can be obtained, which is convenient for subsequent construction.

[0079] Specifically, the first contribution of the voxel block is obtained through the contribution of all data points in the voxel block, and the first contribution is compared with a preset threshold to obtain a key voxel block. The key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage. As a specific example, the specific method for obtaining the BIM model is as follows:

[0080] The contribution of all data points in the target voxel block is accumulated to obtain the first contribution of the target voxel block. If the normalized value of the first contribution of the target voxel block is less than the preset first threshold, the target voxel block is taken as a key voxel block. If the normalized value of the first contribution of the target voxel block is greater than or equal to the preset first threshold, the target voxel block is further divided into eight voxel blocks of the target voxel block, which are all recorded as first voxel blocks. The first contribution of each first voxel block is obtained. If the normalized value of the first contribution of the first voxel block is less than the preset first threshold, the first voxel block is taken as a key voxel block. If the first voxel block is The normalized value of the first contribution of the voxel block is greater than or equal to the preset first threshold value. The first voxel block is further divided into eight voxel blocks of the first voxel block, and the first contribution of each of the eight voxel blocks of the first voxel block is obtained, and compared with the preset first threshold value until it is less than the preset first threshold value. All key voxel blocks in the target first point cloud data are obtained, and the construction stage where the target first point cloud data is located is recorded as the target construction stage. The key voxel blocks in all point cloud data of the target construction stage are matched by the ICP algorithm to obtain the BIM model of the target construction stage. This embodiment is described with the first threshold value of 0.38.

[0081] It should be noted that the first voxel block is a smaller voxel block than the target voxel block. The first contribution of the first voxel block is the same as the method for obtaining the first contribution of the target voxel block, which will not be repeated in this embodiment. If the smaller voxel block after the voxel block is divided into octrees contains only one data point, it is directly used as a key voxel block. The ICP algorithm (Iterative Closest Point) is used to match the key voxel blocks in all point cloud data of a construction stage to obtain the BIM model of the construction stage. This is an existing method and will not be repeated in this embodiment.

[0082] It should be noted that the BIM models of each construction stage in the high-speed rail expansion process were obtained, and the obtained models were manually refined to facilitate subsequent construction management.

[0083] Through the above steps, the BIM-based intelligent management method for high-speed rail expansion construction is completed.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The intelligent management method for high-speed railway expansion construction based on BIM is characterized by: The method comprises the following steps: At each construction stage during the high-speed rail expansion process, point cloud data in different directions of each location is obtained, wherein the point cloud data includes a plurality of data points; According to the coordinate distribution of data points in the point cloud data at the same position and the same direction at different construction stages, the two-dimensional fluctuation curve of each data point in each point cloud data is obtained; according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves, the stress degree of each data point in each point cloud data is obtained; The point cloud data is segmented to obtain a number of voxel blocks, and the segmentation line of each data point in each voxel block is obtained by the force fluctuation of all data points on the straight line passing through the data point in the voxel block; According to the straight line parallel to the dividing line in the voxel block and the force degree of the data points on the straight line, the translation position of the dividing line is obtained, and the dividing line is translated by the translation position to obtain several force planes of the data points to which the dividing line belongs; According to the change of force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data points, the contribution of each data point in each voxel block is obtained; The first contribution of the voxel block is obtained through the contribution of all data points in the voxel block, and the first contribution is compared with the preset threshold to obtain the key voxel block. The key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage; The specific steps of obtaining the two-dimensional fluctuation curve of each data point in each point cloud data according to the coordinate distribution of the data points in the point cloud data at the same position and the same direction at different construction stages are as follows: The point cloud data of any position and any direction at all construction stages are recorded as the first point cloud data; Record any first point cloud data as target first point cloud data; Any data point in the target first point cloud data is recorded as data points; The first point cloud data of the target The coordinates of the data point are recorded as the first coordinates. In the other first point cloud data except the target first point cloud data, the data point closest to the first coordinates is recorded as the first point cloud data. The matching data points of the data points are The coordinates of the data points and their matching data points are fitted in the order of the construction stages to obtain a four-dimensional fluctuation curve. The four-dimensional fluctuation curve is subjected to PCA dimensionality reduction to obtain a two-dimensional fluctuation curve, which is recorded as Two-dimensional fluctuation curve of data points; The method of obtaining the stress degree of each data point in each point cloud data according to the correlation between the two-dimensional fluctuation curves of adjacent data points and the fluctuation degree of the two-dimensional fluctuation curves includes the following specific steps: The cumulative value of the Pearson correlation coefficient between the data point and the two-dimensional fluctuation curve of the data points in its neighborhood and the fluctuation degree of the two-dimensional fluctuation curve of the data point are merged to obtain the stress degree of the data point in the point cloud data; the cumulative value of the Pearson correlation coefficient and the fluctuation degree of the two-dimensional fluctuation curve of the data point are in direct proportion to the stress degree; The point cloud data is segmented to obtain a number of voxel blocks, and the segmentation line for each data point in each voxel block is obtained by the force fluctuation of all data points on the straight line passing through the data point in the voxel block, and the specific steps include the following: Perform an octree segmentation on the target first point cloud data to obtain eight voxel blocks of the target first point cloud data. Any voxel block is recorded as the target voxel block, and any data point in the target voxel block is recorded as the first voxel block. data points, obtained in the target voxel block All the straight lines with data points are obtained, and the variance of the stress degree of all data points in each straight line is obtained. The straight line corresponding to the minimum variance of the stress degree of all data points in the straight line is recorded as The dividing line of the data points; The specific steps of obtaining the contribution of each data point in each voxel block according to the change in the force degree between adjacent data points, the angle between adjacent force planes, the distance between data points, and the normal vector of the data point are as follows: Obtain the cumulative value of the angle cosine value between adjacent force planes of the data point, the change in force degree between the data point and the adjacent data points, and the distance between the data point and the adjacent data points in the voxel block, obtain the acute angle cosine value between the connecting line of the data point and the adjacent data points and the normal vector of the data point, fuse the force degree change, the distance, and the acute angle cosine value to obtain the structural distribution factor of the data point and the adjacent data points, the distance is inversely proportional to the structural distribution factor, the cumulative value of the structural distribution factor of the data point and all adjacent data points within a preset range is recorded as a first cumulative value, the cumulative value of the angle cosine value between adjacent force planes of the data point is recorded as a second cumulative value, the first cumulative value and the second cumulative value are merged to obtain the contribution of the data point, and the first cumulative value and the second cumulative value are in direct proportion to the contribution; The key voxel blocks in all point cloud data in the same construction stage are matched to obtain the BIM model of each construction stage, and the specific steps include the following: All key voxel blocks in the first point cloud data of the target are obtained, the construction stage where the first point cloud data of the target is located is recorded as the target construction stage, and the key voxel blocks in all point cloud data of the target construction stage are matched by the ICP algorithm to obtain the BIM model of the target construction stage.

2. According to the BIM-based intelligent management method for high-speed railway expansion construction according to claim 1, it is characterized in that: The specific steps of obtaining the translation position of the segmentation line according to the straight line parallel to the segmentation line and the force degree of the data points on the straight line in the voxel block are as follows: In the target voxel block, The data points in the neighborhood set of the data points are all recorded as the first data points. The neighborhood set is a set of data points within a preset neighborhood range. The straight line parallel to the dividing line of the data points is recorded as The neighborhood segmentation line of the data point is obtained The absolute value of the difference between the average force degree of all data points on the dividing line of the first data point and the average force degree of all data points on each neighborhood dividing line is taken, and the position of the neighborhood dividing line corresponding to the minimum absolute value of the difference is taken as the first The translation position of the dividing line of the data points.

3. According to claim 2, the BIM-based intelligent management method for high-speed railway expansion construction is characterized in that: The specific steps of translating the segmentation line by translating the position to obtain a plurality of force planes of the data points to which the segmentation line belongs are as follows: The first The dividing line of the data point is translated to the The translation position of the dividing line of the data point is obtained A force plane of data points is obtained by The same method as the translation position of the segmentation line of the data point is used to obtain the new translation position, and the segmentation line after translation is further translated until the data point corresponding to the newly obtained translation position is at the boundary of the target voxel block, and the first Several force planes for each data point.

4. According to claim 1, the BIM-based intelligent management method for high-speed railway expansion construction is characterized in that: The step of obtaining the first contribution of the voxel block by using the contribution of all data points in the voxel block includes the following specific steps: The contribution of all data points in the target voxel block is accumulated to obtain the first contribution of the target voxel block.

5. According to claim 4, the BIM-based intelligent management method for high-speed railway expansion construction is characterized in that: The step of comparing the first contribution with a preset threshold to obtain a key voxel block includes the following specific steps: If the normalized value of the first contribution of the target voxel block is less than the preset first threshold, the target voxel block is taken as a key voxel block. If the normalized value of the first contribution of the target voxel block is greater than or equal to the preset first threshold, the target voxel block is further divided into an octree to obtain eight voxel blocks of the target voxel block, all of which are recorded as first voxel blocks. The first contribution of each first voxel block is obtained. If the normalized value of the first contribution of the first voxel block is less than the preset first threshold, the first voxel block is taken as a key voxel block. If the normalized value of the first contribution of the first voxel block is greater than or equal to the preset first threshold, the first voxel block is further divided into an octree to obtain eight voxel blocks of the first voxel block, and the first contribution of each of the eight voxel blocks of the first voxel block is obtained and compared with the preset first threshold until it is less than the preset first threshold.

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