Point cloud deviation detection method and product for dam structure based on BIM model

Abstract

The present application provides a method and product for detecting point cloud deviation of a dam structure based on a BIM model, the method comprising: collecting point cloud data of the structure and obtaining a building information model of the structure; converting the coordinates of the point cloud data and the building information model respectively so that both the point cloud data and the building information model are in a target engineering coordinate system; dividing the surface of the building information model into a plurality of triangular faces according to a model segmentation parameter; constructing a reference cube in the target engineering coordinate system, and dividing the reference cube into a plurality of grids based on a grid division parameter; for each grid, when at least one point cloud and at least one triangular face are contained in the grid, determining the minimum distance from each point cloud to at least one triangular face respectively, and using the minimum distance as a deviation value to complete the point cloud deviation detection of the structure. The present application can improve the applicability of deviation detection.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of engineering construction technology, and in particular to a method and product for detecting point cloud deviations of dam structures based on a BIM model. Background Art

[0002] During engineering construction, it is necessary to ensure that the current structure is consistent with the designed structure as much as possible. For example, when building a dam, it is necessary to ensure that the actual construction of the dam is consistent with the plan to avoid engineering quality problems. Therefore, deviation detection is necessary.

[0003] At present, point cloud data of structures are collected, and the point cloud data are compared with the two-dimensional cross-section of the structure to determine the deviation between the point cloud and the two-dimensional cross-section of the structure.

[0004] However, the current solution can only extract two-dimensional structural cross-sections based on simple engineering structures, and cannot cope with more complex dam structures, so its applicability is poor. Summary of the invention

[0005] In view of the above problems, embodiments of the present application are proposed to provide a method and product for detecting point cloud deviations of dam structures based on a BIM model, which overcome the above problems or at least partially solve the above problems.

[0006] In a first aspect, the present application discloses a method for detecting point cloud deviation of a dam structure based on a BIM model, comprising:

[0007] Collecting point cloud data of a structure and obtaining a building information model of the structure;

[0008] Respectively performing coordinate conversion on the point cloud data and the building information model so that both the point cloud data and the building information model are in a target engineering coordinate system;

[0009] According to the model segmentation parameters, the surface of the building information model is segmented into a plurality of triangular faces; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters;

[0010] A reference cube is constructed in the target engineering coordinate system, and the reference cube is divided into a plurality of grids based on a grid division parameter; the grid division parameter has a positive correlation with the number of grids;

[0011] For each grid, when the grid contains at least one point cloud and at least one triangular face, the minimum distance from each point cloud to the at least one triangular face is determined respectively, and the minimum distance is used as a deviation value to complete the point cloud deviation detection of the structure.

[0012] In a second aspect, the embodiment of the present application discloses a point cloud deviation detection device for a dam structure based on a BIM model, comprising:

[0013] A data model acquisition module, used to collect point cloud data of a structure and obtain a building information model of the structure;

[0014] A data model conversion module, used to perform coordinate conversion on the point cloud data and the building information model respectively, so that the point cloud data and the building information model are both in a target engineering coordinate system;

[0015] A model surface segmentation module, used to segment the surface of the building information model into a plurality of triangular faces according to the model segmentation parameters; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters;

[0016] A reference grid segmentation module, used for constructing a reference cube in the target engineering coordinate system, and dividing the reference cube into a plurality of grids based on a grid segmentation parameter; the grid segmentation parameter has a positive correlation with the number of grids;

[0017] The point cloud deviation detection module is used to determine, for each grid, the minimum distance from each point cloud to the at least one triangular face when the grid contains at least one point cloud and at least one triangular face, and use the minimum distance as a deviation value to complete the point cloud deviation detection of the structure.

[0018] In a third aspect, an embodiment of the present application further discloses an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the point cloud deviation detection method for dam structure based on the BIM model as described in the first aspect are implemented.

[0019] In a fourth aspect, an embodiment of the present application further discloses a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the point cloud deviation detection method for dam structure based on the BIM model as described in the first aspect are implemented.

[0020] In the embodiment of the present application, it is possible to perform coordinate conversion between point cloud data and building information model, compare point cloud data and building information model in the same engineering coordinate system, and distinguish and accommodate different point clouds and triangular faces based on the divided grids, and then determine the deviation value of each point cloud based on each grid. On the one hand, for each grid, the point cloud in the grid only needs to perform deviation detection based on the triangular faces in the grid, avoiding the need to calculate the distance between each point cloud and a large number of triangular faces, reducing the calculation pressure and improving the efficiency of deviation detection; on the other hand, even for complex structures, deviation detection can be completed through point clouds and building information models, which improves the applicability of deviation detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of a scenario of deviation detection provided in an embodiment of the present application;

[0022] Figure 2 It is a flowchart of the steps of a method for detecting point cloud deviation of a dam structure based on a BIM model provided in an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of some point clouds and triangular surfaces provided in an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of the overall point cloud and the overall deviation of the structure provided in the embodiment of the present application;

[0025] Figure 5 It is a flowchart of another method for detecting point cloud deviation of a dam structure based on a BIM model provided in an embodiment of the present application;

[0026] Figure 6 It is a block diagram of a point cloud deviation detection device for a dam structure based on a BIM model provided in an embodiment of the present application;

[0027] Figure 7 is a block diagram of an electronic device provided in an embodiment of the present application;

[0028] Figure 8 This is a block diagram of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0030] 3D laser scanning technology is often used to detect dimensional deviations of structures. The actual measured 3D laser point cloud coordinates can be compared with the designed dimensions to detect deviations in the construction of the structure.

[0031] Figure 1 1 is a schematic diagram of a deviation detection scenario provided by an embodiment of the present application; the outline 101 is the outline of a simple structure, the black dots are point cloud data, and the point cloud in the inner area B of the outline 101 indicates that the point is underbuilt and needs to be further built. The point cloud in the outer area A of the outline 101 indicates that the point is overbuilt.

[0032] Understandably, Figure 1 It can represent the deviation detection scenario of simple structures, but the geometric features of the dam structure in the actual scenario are often complex. The dam body needs to be constructed in combination with the actual geographical environment, and it is difficult to use a parametric method to represent the outline of the section.

[0033] refer to Figure 2 , which shows a flowchart of the steps of a method for detecting point cloud deviation of a dam structure based on a BIM model provided in an embodiment of the present application, the method comprising:

[0034] Step 201, collecting point cloud data of a structure and obtaining a building information model of the structure;

[0035] In an embodiment of the present application, a three-dimensional laser scanning device can be used to scan a complex structure space to collect point cloud data of a dam structure (hereinafter referred to as the structure). A point cloud is a data structure composed of a large number of discrete points, and each point cloud has its own coordinates.

[0036] Building Information Modeling (BIM) is a three-dimensional model of a building project created through digital technology. The model contains a complete building project information library that is consistent with the actual situation. This information library not only includes geometric information, professional attributes and status information describing building components, but also includes status information of non-component objects (such as space and motion behavior). You can directly obtain a designed BIM model, or you can design a new BIM model in real time, without specific restrictions here.

[0037] Step 202, coordinate conversion is performed on the point cloud data and the building information model respectively, so that the point cloud data and the building information model are both in a target engineering coordinate system;

[0038] In the disclosed embodiment, after the point cloud data is generated, it can be in a point cloud coordinate system. The point cloud coordinate system is a set of coordinate systems used to describe the position and direction of the point cloud. The point cloud coordinate system can be a Cartesian coordinate system (x, y, z coordinate system), a polar coordinate system (r, 0, mid coordinate system) and a laser radar coordinate system (with the laser radar as the origin, the front as the x-axis, the right as the y-axis, and the top as the z-axis).

[0039] The target engineering coordinate system can be an engineering coordinate system defined during engineering construction and can be applied to the construction site. The point cloud coordinates of each point cloud in the point cloud data can be converted to display the point cloud in the target engineering coordinate system. Similarly, the BIM design coordinate system can be defined according to the building geometry when the BIM model is constructed. It is also necessary to convert the coordinates of the building information model and present the building information model in the target engineering coordinate system so that both the point cloud data and the building information model are in the same target engineering coordinate system.

[0040] Step 203, dividing the surface of the building information model into a plurality of triangular faces according to the model segmentation parameters; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters;

[0041] In the disclosed embodiment, the surface of the building information model is determined. According to different model segmentation parameters, the model surface can be segmented differently, and the segmentation can obtain triangular faces, which can be combined into the model surface. The model segmentation parameter can be an area parameter. According to different area parameters, triangular faces of different areas can be obtained by segmentation. The model segmentation parameter can also be a perimeter parameter. According to different perimeter parameters, triangular faces of different perimeters can be obtained by segmentation. When the model segmentation parameter is larger, the geometric parameters (such as area, perimeter, etc.) of the triangular faces obtained by segmentation are larger. The model segmentation parameter is not specifically limited here.

[0042] Step 204, constructing a reference cube in the target engineering coordinate system, and dividing the reference cube into a plurality of grids based on a grid division parameter; there is a positive correlation between the grid division parameter and the number of grids;

[0043] In the disclosed embodiment, a reference cube is constructed in the same target engineering coordinate system, and the reference cube is used for deviation detection. Based on the grid division parameter, the reference cube is divided into multiple grids. Each grid is also a smaller cube. The grid division parameter can directly represent the number of required grids or the number of divisions. When the number of required grids is large, the number of grids finally divided is larger; or when the number of divisions is large, the number of grids finally obtained will also be larger.

[0044] It can be understood that, since the point cloud data, the building information model, and the reference cube are all in the same target engineering coordinate system, different divided grids can accommodate part of the point cloud and part of the triangular faces of the building information model.

[0045] Step 205, for each grid, when the grid contains at least one point cloud and at least one triangular face, respectively determine the minimum distance from each point cloud to the at least one triangular face, and use the minimum distance as a deviation value to complete the point cloud deviation detection of the structure.

[0046] In the embodiment of the present disclosure, there are multiple grids, and each grid can be used as a basic division unit to perform deviation detection separately. A single grid includes at least one point cloud and at least one triangular face, and for each point cloud in the grid, the minimum distance from each point cloud to at least one triangular face in the grid is determined.

[0047] It can be understood that the distance from the point cloud to the triangle surface can be the normal distance from the point to the triangle surface. When the foot of the perpendicular from the point to the triangle surface is not within the triangle surface, the distance value is the shortest distance from the point to any edge of the triangle surface.

[0048] It should be noted that there is not a one-to-one correspondence between point clouds and triangular faces. For each point cloud, the minimum distance between a single point cloud and multiple triangular faces needs to be determined. Therefore, a single point cloud will correspond to multiple distance results.

[0049] Since the triangular face is the surface of the building information model, the minimum distance is the distance from the point cloud to the building information model. It can be understood that when there is no deviation, the minimum distance corresponding to the point cloud can be 0. When there is a deviation, the minimum distance is greater than 0.

[0050] Figure 3 is a schematic diagram of a portion of point clouds and triangular surfaces provided by an embodiment of the present disclosure; Figure 3 The grid 31 includes a grid 31, which contains a plurality of point clouds 311 and a plurality of triangular faces 312, and the minimum distance from each point cloud 311 to the plurality of triangular faces 312 is calculated.

[0051] Figure 4 is a schematic diagram of the overall point cloud and the overall deviation of the structure provided by the embodiment of the present disclosure; Figure 4 It includes an outline formed by the point cloud data and the outline corresponding to the building information model. The distance between the two outlines is the deviation value.

[0052] In summary, by implementing the embodiments of the present application, it is possible to perform coordinate conversion between point cloud data and building information models, compare point cloud data and building information models in the same engineering coordinate system, and distinguish and accommodate different point clouds and triangular faces based on the divided grids, and then determine the deviation value of each point cloud based on each grid. On the one hand, for each grid, the point cloud in the grid only needs to be detected for deviation based on the triangular faces in the grid, avoiding the need to calculate the distance between each point cloud and a large number of triangular faces, reducing the calculation pressure and improving the efficiency of deviation detection; on the other hand, even for complex structures, deviation detection can be completed through point clouds and building information models, improving the applicability of deviation detection.

[0053] refer to Figure 5 , which shows a flowchart of the steps of a method for detecting point cloud deviation of a dam structure based on a BIM model provided in an embodiment of the present application, the method comprising:

[0054] Step 501, collecting point cloud data of a structure and obtaining a building information model of the structure;

[0055] Step 502, coordinate conversion is performed on the point cloud data and the building information model respectively, so that the point cloud data and the building information model are both in a target engineering coordinate system;

[0056] Step 503, dividing the surface of the building information model into a plurality of triangular faces according to the model segmentation parameters; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters;

[0057] Step 504, constructing a reference cube in the target engineering coordinate system, and dividing the reference cube into a plurality of grids based on a grid division parameter; there is a positive correlation between the grid division parameter and the number of grids;

[0058] Step 505, for each grid, when the grid contains at least one point cloud and at least one triangular face, respectively determine the minimum distance from each point cloud to the at least one triangular face, and use the minimum distance as a deviation value to complete the point cloud deviation detection of the structure.

[0059] The above steps 501-505 can refer to the above Figure 2 The contents of the embodiments will not be described in detail here.

[0060] Optionally, the step of respectively determining the minimum distance from each point cloud to the at least one triangular face and using the minimum distance as a deviation value to complete the point cloud deviation detection of the structure includes:

[0061] For each point cloud, determining the distance from the point cloud to the at least one triangular face to obtain at least one distance value;

[0062] Among the at least one distance value, the smallest distance value is used as the deviation value to complete the point cloud deviation detection of the structure.

[0063] In the disclosed embodiment, each grid may contain one or more point clouds and one or more triangular faces. For each point cloud, the distance from the point cloud to each triangular face needs to be calculated. Based on the number of triangular faces, there are a corresponding number of distance calculation results, i.e., distance values. Among at least one distance value, the minimum distance value is determined, and the minimum distance value is used as the deviation value of the current point cloud. Then, for all point clouds in the grid, a minimum distance value can be determined, and then the deviation of the entire grid as a whole can be determined.

[0064] When implementing the embodiments of the present disclosure, for the point clouds in each grid, these point clouds only need to perform distance calculations with the triangular faces in the same grid to determine the deviation value corresponding to the point cloud. There is no need to perform distance calculations with a large number of triangular faces outside the grid, which reduces the calculation pressure and improves the efficiency of deviation determination.

[0065] Optionally, the method further includes:

[0066] Step 506: if the point cloud is within the coordinate range of the building information model, mark the point cloud as a first deviation type; the first deviation type indicates that the point cloud intrudes into the building information model;

[0067] Step 507: When the point cloud is outside the coordinate range of the building information model, mark the point cloud as a second deviation type; the second deviation type indicates that the point cloud is out of the building information model.

[0068] In the disclosed embodiment, when the building information model is displayed or exists in the target engineering coordinate system, it has a certain spatial range, which is determined by the coordinate range of the building information model. The larger the building information model, the larger the coordinate range. It is possible to determine whether the point cloud is within the coordinate range of the building information model or outside the coordinate range of the building information model based on the coordinates of the point cloud in the target engineering coordinate system and the coordinate range of the building information model. Of course, it can also be exactly on the surface of the building information model (that is, there is no deviation).

[0069] The internal and external relationship between the point cloud and the building information model can be directly identified by coordinate comparison or the signed distance function in the convex hull detection (convexHull). When the point cloud is within the coordinate range of the building information model, the point cloud can be marked as the first deviation type (for example, represented by -1) to indicate that the point cloud invades the building information model, that is, there is an under-excavation situation; when the point cloud is outside the coordinate range of the building information model, the point cloud is marked as the second deviation type (for example, represented by +1) to indicate that the point cloud is out of the building information model, that is, there is an over-excavation situation.

[0070] In the implementation of the embodiments of the present disclosure, when the point cloud is within the coordinate range of the building information model, the point cloud is marked as the first deviation type; when the point cloud is outside the coordinate range of the building information model, the point cloud is marked as the second deviation type. While determining the deviation value corresponding to the point cloud, the deviation type of the point cloud can also be determined, so that the detection content of the deviation detection is richer and the accuracy is stronger.

[0071] Optionally, when the grid contains at least one point cloud and at least one triangular face, before the step of respectively determining the minimum distance from each point cloud to the at least one triangular face, the method further includes:

[0072] When any point of a triangular face is within a mesh, it is determined that the mesh contains the triangular face.

[0073] In the disclosed embodiment, a mesh may contain at least one point cloud and at least one triangular face. It should be noted that since the triangular face is the surface of the building information model, the point cloud is still near the surface of the building information model even if there is a deviation. Therefore, the distribution of triangular faces and point clouds is relatively concentrated, so there may be some meshes that do not contain point clouds and triangular faces.

[0074] Since a point cloud is a point, when the coordinates of a point cloud are within the coordinate range of a grid, it is considered that the grid contains the point cloud, so a point cloud can only belong to one grid. However, a triangle is a face, and a grid may contain the entire face of the triangle, or only part of the face, or only one point of the triangle.

[0075] When determining the triangular face contained in a mesh, it can be determined that the mesh contains the triangular face when any point of the triangular face is located in the mesh. Therefore, a triangular face may belong to multiple meshes.

[0076] In the implementation of the embodiments of the present disclosure, when any point of a triangular face is within a certain grid, the triangular face is divided into the grid, which can avoid the loss of triangular face due to the triangular face not being completely within the grid. That is, if the triangular face is required to be completely within a single grid, the triangular face will be included in the deviation calculation of the grid, which will cause some triangular faces that do not belong to a single grid to be ignored, which will cause errors in the calculation of the deviation result. When any point of a triangular face is within a grid, it is determined that the grid contains the triangular face, which can improve the accuracy of deviation detection.

[0077] Optionally, the grid division parameter is a preset first value;

[0078] The step of constructing a reference cube in the target engineering coordinate system and dividing the reference cube into a plurality of grids based on a grid division parameter comprises:

[0079] Constructing a reference cube according to the coordinate ranges of the point cloud data and the building information model in the target engineering coordinate system; the reference cube contains the point cloud data and the building information model;

[0080] Based on the preset first value, the reference cube is divided into octree grids for a number of times corresponding to the preset first value to obtain a plurality of grids.

[0081] In the disclosed embodiment, since the grid needs to accommodate the triangular faces of the point cloud and the building information model, the reference cube used to divide the grid first needs to be able to accommodate the entire point cloud and the building information model. Then, according to the coordinate range of the point cloud data and the building information model in the target engineering coordinate system, a reference cube can be constructed so that the reference cube can accommodate all the point cloud data and the building information model.

[0082] Based on the preset first value, the reference cube is divided into octree grids for the number of times corresponding to the preset first value. Among them, octree grid division refers to recursively dividing the divided object into eight equal small cubes (or "sub-cubes"), each of which can be further divided into smaller sub-cubes until specific conditions are met or the required accuracy is achieved. For example, when the preset first value is 3, it means that 3 octree grid divisions are required, and 512 grids can be obtained based on the initial reference cube.

[0083] In the implementation of the disclosed embodiment, a reference cube is constructed through the coordinate range of the point cloud data and the building information model in the target engineering coordinate system; based on the preset first value, the reference cube is divided into octree meshes for a corresponding number of times to obtain multiple meshes. The triangular mesh model can be refined, and more refined triangular faces can be provided for subsequent deviation detection based on point clouds and triangular faces, which can reduce the error of point cloud deviation detection.

[0084] Optionally, the step 502 of performing coordinate conversion on the point cloud data and the building information model respectively so that both the point cloud data and the building information model are in a target engineering coordinate system includes:

[0085] Sub-step 5021, determining a preset first number of target point clouds in the point cloud data, and determining a preset second number of target key points in the building information model; the target point clouds and the target key points both have corresponding engineering coordinates in the target engineering coordinate system;

[0086] Sub-step 5022, calculating a first transformation matrix based on the point cloud coordinates of the target point cloud data and the first engineering coordinates in the target engineering coordinate system, and calculating a second transformation matrix based on the model coordinates of the target key point and the second engineering coordinates in the target engineering coordinate system;

[0087] Sub-step 5023, based on the first transformation matrix, the point cloud data is subjected to coordinate transformation, and based on the second transformation matrix, the building information model is subjected to coordinate transformation, so that both the point cloud data and the building information model are in the target engineering coordinate system.

[0088] In the embodiment of the present disclosure, a preset first number (e.g., 3, 5, etc.) of target point clouds may be selected from point cloud data including a plurality of point clouds, and the target point clouds may be referred to as control points. Similarly, a preset second number (e.g., 3, 5, etc.) of target key points may be determined in the building information model. The preset first number and the preset second number may be the same.

[0089] The first engineering coordinates of the target point cloud can be selected in the target engineering coordinate system. That is, the target point cloud has both the point cloud coordinates in the point cloud coordinate system and the first engineering coordinates in the target engineering coordinate system.

[0090] Based on the point cloud coordinates of the target point cloud data in the point cloud coordinate system and the first engineering coordinates in the target engineering coordinate system, a first transformation matrix, i.e., a rotation matrix, is calculated. The first transformation matrix is ​​applicable to any point cloud in the point cloud data, so all point cloud data can be transformed based on the first transformation matrix.

[0091] Similarly, based on the model coordinates of the target key point in the model design coordinate system and the second engineering coordinates in the target engineering coordinate system, a second transformation matrix is ​​calculated. Based on the second transformation matrix, the building information model is transformed. The point cloud data and the building information model are both displayed in the target engineering coordinate system, so that the point cloud data and the building information model can be compared in the same scene.

[0092] Optionally, the model segmentation parameter is a preset second value, and the geometric parameter is a side length;

[0093] The step of segmenting the surface of the building information model into a plurality of triangular faces according to the model segmentation parameters comprises:

[0094] According to the preset second value, the surface of the building information model is divided into a plurality of triangular faces; the side length of each triangular face is less than or equal to the preset second value.

[0095] In the disclosed embodiment, the model segmentation parameter may be a preset first value such as 5, 10, etc. According to the preset first value, the surface of the building information model is segmented into a plurality of triangular faces, and the side length of each triangular face is less than or equal to a preset second value. The surface may be segmented into triangular faces by a software tool or algorithm program, such as a 3D modeling tool Unreal Engine, ZBrush, etc.

[0096] By implementing the embodiments of the present disclosure, the surface of the building information model is divided into multiple triangular faces by presetting the second value; the side length of each triangular face is less than or equal to the preset second value, so that the surface of the building information model can be divided into fine triangular faces, thereby improving the accuracy of subsequent deviation detection based on point clouds and triangular faces.

[0097] In summary, in the embodiments of the present application, it is possible to perform coordinate conversion between point cloud data and building information models, compare point cloud data and building information models in the same engineering coordinate system, and distinguish and accommodate different point clouds and triangular faces based on the divided grids, and then determine the deviation value of each point cloud based on each grid. On the one hand, for each grid, the point cloud in the grid only needs to be detected for deviation based on the triangular faces in the grid, avoiding the need to calculate the distance between each point cloud and a large number of triangular faces, reducing the calculation pressure and improving the efficiency of deviation detection; on the other hand, even for complex structures, deviation detection can be completed through point clouds and building information models, which improves the applicability of deviation detection.

[0098] refer to Figure 6 , which shows a dam structure point cloud deviation detection device 60 based on a BIM model provided in an embodiment of the present application, comprising:

[0099] The data model acquisition module 601 is used to collect point cloud data of a structure and obtain a building information model of the structure;

[0100] A data model conversion module 602 is used to perform coordinate conversion on the point cloud data and the building information model respectively, so that the point cloud data and the building information model are both in a target engineering coordinate system;

[0101] The model surface segmentation module 603 is used to segment the surface of the building information model into a plurality of triangular faces according to the model segmentation parameters; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters;

[0102] A reference grid segmentation module 604 is used to construct a reference cube in the target engineering coordinate system and divide the reference cube into a plurality of grids based on a grid segmentation parameter; the grid segmentation parameter has a positive correlation with the number of grids;

[0103] The point cloud deviation detection module 605 is used to determine, for each grid, the minimum distance from each point cloud to the at least one triangular face when the grid contains at least one point cloud and at least one triangular face, and use the minimum distance as the deviation value to complete the point cloud deviation detection of the structure.

[0104] Optional, point cloud deviation detection module, including:

[0105] A multi-distance submodule, used for determining, for each point cloud, the distance from the point cloud to the at least one triangular face, so as to obtain at least one distance value;

[0106] The minimum distance submodule is used to use the minimum distance value among the at least one distance value as the deviation value to complete the point cloud deviation detection of the structure.

[0107] Optionally, the device further comprises:

[0108] A first type module is used to mark the point cloud as a first deviation type when the point cloud is within the coordinate range of the building information model; the first deviation type indicates that the point cloud intrudes into the building information model;

[0109] The second type module is used to mark the point cloud as a second deviation type when the point cloud is outside the coordinate range of the building information model; the second deviation type indicates that the point cloud is out of the building information model.

[0110] Optionally, the grid division parameter is a preset first value;

[0111] Reference mesh segmentation module, including:

[0112] A cube construction submodule, for constructing a reference cube according to the coordinate range of the point cloud data and the building information model in the target engineering coordinate system; the reference cube contains the point cloud data and the building information model;

[0113] The cube segmentation submodule is used to divide the reference cube into octree grids for a number of times corresponding to the preset first value based on the preset first value, so as to obtain a plurality of grids.

[0114] Optionally, the device further comprises:

[0115] The mesh triangle module is used to determine that the mesh contains the triangle when any point of the triangle is located in the mesh.

[0116] Optional, data model conversion module, including:

[0117] a target determination submodule, configured to determine a preset first number of target point clouds in the point cloud data, and a preset second number of target key points in the building information model; the target point clouds and the target key points both have corresponding engineering coordinates in the target engineering coordinate system;

[0118] A conversion matrix submodule, for calculating a first conversion matrix based on the point cloud coordinates of the target point cloud data and the first engineering coordinates in the target engineering coordinate system, and for calculating a second conversion matrix based on the model coordinates of the target key points and the second engineering coordinates in the target engineering coordinate system;

[0119] The point cloud model conversion submodule is used to perform coordinate transformation on the point cloud data based on the first transformation matrix, and to perform coordinate transformation on the building information model based on the second transformation matrix, so that both the point cloud data and the building information model are in a target engineering coordinate system.

[0120] Optionally, the model segmentation parameter is a preset second value, and the geometric parameter is a side length;

[0121] Model surface segmentation module, including:

[0122] The side length segmentation submodule is used to segment the surface of the building information model into a plurality of triangular faces according to the preset second value; the side length of each triangular face is less than or equal to the preset second value.

[0123] In summary, in the embodiments of the present application, it is possible to perform coordinate conversion between point cloud data and building information models, compare point cloud data and building information models in the same engineering coordinate system, and distinguish and accommodate different point clouds and triangular faces based on the divided grids, and then determine the deviation value of each point cloud based on each grid. On the one hand, for each grid, the point cloud in the grid only needs to be detected for deviation based on the triangular faces in the grid, avoiding the need to calculate the distance between each point cloud and a large number of triangular faces, reducing the calculation pressure and improving the efficiency of deviation detection; on the other hand, even for complex structures, deviation detection can be completed through point clouds and building information models, which improves the applicability of deviation detection.

[0124] Figure 7 8 is a block diagram of an electronic device 800 shown in an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0125] Reference Figure 7 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0126] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0127] The memory 804 is used to store various types of data to support the operation of the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, multimedia, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0128] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0129] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a multimedia mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0130] The audio component 810 is used to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is used to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0131] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0132] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0133] The communication component 816 is used to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0134] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to implement the BIM model-based dam structure point cloud deviation detection method provided in an embodiment of the present application.

[0135] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0136] Figure 8is a block diagram of an electronic device 900 shown in an exemplary embodiment. For example, the electronic device 900 may be provided as a server. Referring to Figure 8 , the electronic device 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions executable by the processing component 922, such as application programs. The application programs stored in the memory 932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 922 is configured to execute instructions to perform a method for detecting point cloud deviation of a dam structure based on a BIM model provided in an embodiment of the present application.

[0137] The electronic device 900 may also include a power supply component 926 configured to perform power management of the electronic device 900, a wired or wireless network interface 950 configured to connect the electronic device 900 to a network, and an input / output (I / O) interface 958. The electronic device 900 may operate based on an operating system stored in the memory 932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSD TM or the like.

[0138] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0139] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A point cloud deviation detection method for dam structure based on BIM model, characterized in that: The method comprises: Collecting point cloud data of a structure and obtaining a building information model of the structure; The point cloud data and the building information model are respectively subjected to coordinate transformation so that both the point cloud data and the building information model are in a target engineering coordinate system, including: determining a preset first number of target point clouds in the point cloud data, and determining a preset second number of target key points in the building information model; both the target point cloud and the target key points have corresponding engineering coordinates in the target engineering coordinate system; a first transformation matrix is ​​obtained by calculation based on the point cloud coordinates of the target point cloud data and the first engineering coordinates in the target engineering coordinate system, and a second transformation matrix is ​​obtained by calculation based on the model coordinates of the target key points and the second engineering coordinates in the target engineering coordinate system; the point cloud data is subjected to coordinate transformation based on the first transformation matrix, and the building information model is subjected to coordinate transformation based on the second transformation matrix, so that both the point cloud data and the building information model are in a target engineering coordinate system; the target engineering coordinate system is an engineering coordinate system applied to the construction site; According to the model segmentation parameters, the surface of the building information model is segmented into a plurality of triangular faces; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameters; The model segmentation parameter is a preset second value, and the geometric parameter is a side length; the step of segmenting the surface of the building information model into a plurality of triangular faces according to the model segmentation parameter comprises: segmenting the surface of the building information model into a plurality of triangular faces according to the preset second value by a software tool or an algorithm program; the side length of each triangular face is less than or equal to the preset second value; A reference cube is constructed in the target engineering coordinate system, and the reference cube is divided into a plurality of grids based on a grid division parameter; the grid division parameter has a positive correlation with the number of grids; For each grid, when any point of a triangular face is within the grid, it is determined that the grid contains the triangular face. When the grid contains at least one point cloud and at least one triangular face, the minimum distance from each point cloud to the at least one triangular face is determined respectively, and the minimum distance is used as a deviation value to complete the point cloud deviation detection of the structure.

2. The method according to claim 1, characterized in that The step of respectively determining the minimum distance from each point cloud to the at least one triangular face and using the minimum distance as a deviation value to complete the point cloud deviation detection of the structure includes: For each point cloud, determining the distance from the point cloud to the at least one triangular face to obtain at least one distance value; Among the at least one distance value, the smallest distance value is used as the deviation value to complete the point cloud deviation detection of the structure.

3. The method according to claim 1, characterized in that The method further comprises: In the case where the point cloud is within the coordinate range of the building information model, marking the point cloud as a first deviation type; the first deviation type indicates that the point cloud intrudes into the building information model; In the case where the point cloud is outside the coordinate range of the building information model, the point cloud is marked as a second deviation type; the second deviation type indicates that the point cloud is out of the building information model.

4. The method according to claim 1, characterized in that: The grid division parameter is a preset first value; The step of constructing a reference cube in the target engineering coordinate system and dividing the reference cube into a plurality of grids based on a grid division parameter comprises: Constructing a reference cube according to the coordinate ranges of the point cloud data and the building information model in the target engineering coordinate system; the reference cube contains the point cloud data and the building information model; Based on the preset first value, the reference cube is divided into octree grids for a number of times corresponding to the preset first value to obtain a plurality of grids.

5. A point cloud deviation detection device for dam structure based on BIM model, characterized in that: include: A data model acquisition module, used to collect point cloud data of a structure and obtain a building information model of the structure; A data model conversion module, used to perform coordinate conversion on the point cloud data and the building information model respectively, so that the point cloud data and the building information model are both in a target engineering coordinate system; A model surface segmentation module, used for segmenting the surface of the building information model into a plurality of triangular faces according to a model segmentation parameter; there is a positive correlation between the geometric parameters of the triangular faces and the model segmentation parameter; the model segmentation parameter is a preset second value, and the geometric parameter is a side length; The step of dividing the surface of the building information model into a plurality of triangular faces according to the model division parameter comprises: dividing the surface of the building information model into a plurality of triangular faces according to the preset second value by a software tool or an algorithm program; the side length of each triangular face is less than or equal to the preset second value; A reference grid segmentation module, used for constructing a reference cube in the target engineering coordinate system, and dividing the reference cube into a plurality of grids based on a grid segmentation parameter; the grid segmentation parameter has a positive correlation with the number of grids; A point cloud deviation detection module, for each grid, when the grid contains at least one point cloud and at least one triangular face, respectively determines a minimum distance from each point cloud to the at least one triangular face, and uses the minimum distance as a deviation value to complete the point cloud deviation detection of the structure; A mesh triangle module, used for determining that the mesh contains the triangle when any point of the triangle is within the mesh; The data model conversion module includes: a target determination submodule, configured to determine a preset first number of target point clouds in the point cloud data, and a preset second number of target key points in the building information model; the target point clouds and the target key points both have corresponding engineering coordinates in the target engineering coordinate system; A conversion matrix submodule, for calculating a first conversion matrix based on the point cloud coordinates of the target point cloud data and the first engineering coordinates in the target engineering coordinate system, and for calculating a second conversion matrix based on the model coordinates of the target key points and the second engineering coordinates in the target engineering coordinate system; The point cloud model conversion submodule is used to perform coordinate transformation on the point cloud data based on the first transformation matrix, and to perform coordinate transformation on the building information model based on the second transformation matrix, so that both the point cloud data and the building information model are in a target engineering coordinate system; the target engineering coordinate system is an engineering coordinate system applied to the construction site.

6. The device according to claim 5, characterized in that The point cloud deviation detection module comprises: A multi-distance submodule, used for determining, for each point cloud, the distance from the point cloud to the at least one triangular face, so as to obtain at least one distance value; The minimum distance submodule is used to use the minimum distance value among the at least one distance value as the deviation value to complete the point cloud deviation detection of the structure.

7. The device according to claim 5, characterized in that The device also includes: A first type module is used to mark the point cloud as a first deviation type when the point cloud is within the coordinate range of the building information model; the first deviation type indicates that the point cloud intrudes into the building information model; The second type module is used to mark the point cloud as a second deviation type when the point cloud is outside the coordinate range of the building information model; the second deviation type indicates that the point cloud is out of the building information model.

8. The device according to claim 5, characterized in that The grid division parameter is a preset first value; The reference grid segmentation module comprises: A cube construction submodule, for constructing a reference cube according to the coordinate range of the point cloud data and the building information model in the target engineering coordinate system; the reference cube contains the point cloud data and the building information model; The cube segmentation submodule is used to divide the reference cube into octree grids for a number of times corresponding to the preset first value based on the preset first value, so as to obtain a plurality of grids.

9. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the steps in the point cloud deviation detection method for dam structure based on BIM model as described in any one of claims 1 to 4 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for detecting point cloud deviation of a dam structure based on a BIM model as described in any one of claims 1 to 4 are implemented.

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