Engineering quality allowable deviation detection system and construction method based on laser point cloud

Abstract

The present invention relates to a system and construction method for detecting allowable deviations of engineering quality based on laser point cloud, wherein the detection system includes a user operation interface module, a three-dimensional rendering module, a plane detection module and a measurement and calculation module; the user operation interface module is used to provide a user operation interface and a graphical interactive interface, specifically including: file import, display rendering model, display model block list, provide plane list and output measurement information; the three-dimensional rendering module is used for point cloud segmentation, rendering and highlighting; the plane detection module is used to extract plane information of imported point cloud data; the measurement and calculation module is used to calculate the numerical results of quality inspection items. The present invention is expected to achieve the purpose of obtaining construction quality inspection results efficiently and accurately, overcoming the problems of cumbersome operation, scattered data and limited inspection indicators of current manual measurement and some intelligent inspection equipment.

Description

Technical Field

[0001] The present invention relates to the field of construction quality acceptance of building projects, and more specifically, to a system and a construction method for detecting allowable deviations of project quality based on laser point clouds. Background Art

[0002] The dimensional deviation of cast-in-place structures, masonry structures, plastering works, etc. is an important part of the quality acceptance stage. In the actual operation of the project, although the detection tools are constantly improving, these indicators still need to be measured separately using tools such as rulers, rulers, and feeler gauges. Although there are some intelligent detection equipment on the market, the collected data is relatively scattered, the indicators that can be detected are limited, and a lot of manual operation is still required. For large buildings, the construction measurement and deformation measurement are difficult and demanding, and more efficient tools are needed to measure the indicators. In addition, the various types of quality information currently collected are mainly carried out in paper or electronic reports. The information is fragmented and difficult to be converted into digitally stored data, which is not conducive to interconnection and integration with construction entities, and it is difficult to build visual cognition, which hinders the timely flow of quality control information to corporate agencies to assist supervision and decision-making, and is not conducive to reducing costs and increasing efficiency in corporate management. Therefore, it is necessary to use digital means to assist quality inspection and improve the efficiency and accuracy of information transmission.

[0003] In recent years, 3D laser scanning technology has attracted widespread attention and application in the construction industry. Existing research has explored the application of this technology in wall flatness, verticality, etc., such as the least squares method to evaluate wall flatness. However, existing research applications cannot effectively use 3D laser scanning methods to achieve efficient acquisition of entity quality information, have a low degree of automation, too few inspection items, and a deviation between theoretical calculations and actual manual operations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a system and construction method for detecting allowable deviations in engineering quality based on laser point clouds, which can quickly obtain quality inspection index results and improve the efficiency and accuracy of construction engineering quality acceptance work.

[0005] The technical solution adopted by the present invention to solve the technical problem is: constructing an engineering quality allowable deviation detection system based on laser point cloud, including a user operation interface module, a three-dimensional rendering module, a plane detection module and a measurement calculation module;

[0006] The user operation interface module is used to provide a user operation interface and a graphical interactive interface, specifically including: file import, display rendering model, display model block list, provide plane list and output measurement information;

[0007] The three-dimensional rendering module is used for point cloud segmentation, rendering and highlighting;

[0008] The plane detection module is used to extract plane information of the imported point cloud data;

[0009] The measurement and calculation module is used to calculate the numerical results of the quality inspection items.

[0010] According to the above scheme, the file import is to select a point cloud data file and upload it to the system platform for processing; the displayed model block list is to use the point cloud semantic segmentation model to identify the components of the imported point cloud data file and form a component block list; the provided plane list is the planes in the area detected by the plane detection algorithm after selecting the model block area, and forms a corresponding plane list; the measurement information is the quality information of the output appearance quality, position and size.

[0011] According to the above scheme, the point cloud segmentation and rendering is to use the deep learning model to segment the point cloud into components, and render the model interactively; the highlight display is to display the selected plane or the selected component area in a color that is different from other planes and component areas.

[0012] According to the above scheme, the plane information of the point cloud data is a pair of vectors (C, N) represented by the center point C on the plane and the plane normal vector N, which are used to represent the plane.

[0013] The present invention also provides a method for constructing an engineering quality allowable deviation detection system based on laser point cloud, comprising the following steps:

[0014] S1, obtain on-site point cloud data in real time by laser scanning on-site entities;

[0015] S2, importing the on-site point cloud data, and performing block division and thinning simplification;

[0016] S3. Use Pointnet++ to perform semantic segmentation on the simplified on-site point cloud data, extract the locations of door openings and wall openings, and render the model;

[0017] S4, performing plane extraction on the area selected from the model block list to obtain a plane data set;

[0018] S5. Select a plane area to measure the quality acceptance indicators.

[0019] According to the above scheme, step S2 includes the following steps:

[0020] S201, thinning and simplifying the on-site point cloud data to reduce point cloud density and file size;

[0021] S202, using an octree to spatially divide the on-site point cloud data until the number of samples contained in the leaf nodes is 0.1% of the total number of samples;

[0022] S203, storing the divided original on-site point cloud data and simplified on-site point cloud data separately.

[0023] According to the above scheme, step S4 includes the following steps:

[0024] S401, selecting the divided regional point cloud to perform robustness plane test and detecting the minimum plane;

[0025] S402, repeatedly growing and merging the detected minimum plane until a stable detection plane is reached.

[0026] According to the above scheme, the robustness plane test includes the following steps:

[0027] S40101. For the plane fitted by the point cloud on the leaf node, the plane is represented by a pair of vector groups (C, N), where C is the center point of the plane and N is the normal vector of the plane, where:

[0028] C=[median(S x ), median(S y ), median(S z )] T

[0029]

[0030] S x , S y , S z Represents the components of the point cloud coordinates on the x, y, and z axes, S Nx , S Ny , S Nz Represents the normal vector of the point cloud, which is obtained by principal component analysis;

[0031] S40102. After successfully passing the plane distance test, the plane angle test, and the outlier test, the point cloud data group enters the growth and fusion stage.

[0032] According to the above scheme, the measurement of the relevant quality acceptance index utilizes the information (C, N) of the extracted plane, and three basic operations are extended based on the pair of vectors, including plane-plane distance measurement, plane-plane angle measurement, and point-plane distance calculation;

[0033] The plane-to-plane distance measurement is used for calculating the cross-sectional dimensions of concrete structure engineering, indoor net height deviation of plastering engineering, and room span / depth deviation;

[0034] The plane-plane angle measurement is used to measure the verticality of concrete structure engineering, the verticality and squareness of masonry engineering, the verticality, squareness of internal and external corners, and squareness of plastering engineering;

[0035] The point-plane distance measurement is used for measuring the surface flatness of concrete structure engineering, the horizontality of top plate, the surface flatness of masonry engineering, the surface flatness of plastering engineering, the horizontality of top plate, the surface flatness of ground, and the extreme difference of ground horizontality.

[0036] The implementation of the engineering quality allowable deviation detection system and construction method based on laser point cloud of the present invention has the following beneficial effects:

[0037] 1. The present invention can remotely obtain various acceptance measurement indicators through a one-time on-site three-dimensional laser scanning and upload it to the system platform. The implementation efficiency and the accuracy of the measurement items are higher than those of manual measurement, which effectively improves the level of intelligent delivery of engineering projects;

[0038] 2. The present invention obtains quality acceptance inspection indicators through on-site point cloud data, and compares them with relevant standards and specifications to determine whether the engineering quality deviation is within the allowable range. This method can make up for the time-consuming and labor-intensive shortcomings of traditional acceptance inspection methods, and various measurement indicators can be obtained on the system platform in one scan;

[0039] 3. The present invention is based on plane detection of the three-dimensional point cloud data of the construction target, and then inputs the obtained plane into the code for simulating construction quality detection. It is expected to achieve the purpose of obtaining construction quality detection results efficiently and accurately, overcoming the problems of cumbersome operation, scattered data and limited detection indicators of current manual measurement and some intelligent detection equipment;

[0040] 4. The present invention realizes efficient collection of engineering quality dimensional deviation indicators through high-precision 3D scanning and reconstruction technology, intelligent plane segmentation algorithm, intelligent semantic recognition algorithm, dimensional deviation index calculation, etc., which can effectively improve the management level of the engineering quality acceptance stage, improve the measurement accuracy of dimensional deviation and information reliability, and is conducive to the efficient circulation and safe storage of information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0042] Figure 1 It is a schematic diagram of a construction method of an engineering quality allowable deviation detection system based on laser point cloud of the present invention;

[0043] Figure 2 It is an architecture diagram of the engineering quality allowable deviation detection system based on laser point cloud of the present invention;

[0044] Figure 3It is an operation flow chart of the engineering quality allowable deviation detection system based on laser point cloud of the present invention. DETAILED DESCRIPTION

[0045] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0046] like Figure 1-2 As shown, the method for constructing the engineering quality allowable deviation detection system based on laser point cloud of the present invention comprises the following steps:

[0047] S1. Obtain on-site point cloud data in real time by laser scanning on-site entities.

[0048] S2. Import data and perform block division and simplification, specifically:

[0049] S201. Simplify the point cloud data to reduce the point cloud density and file size.

[0050] S202, using the octree to perform spatial division on the point cloud data until the number of samples contained in the leaf nodes is 0.1% of the total number of samples,

[0051] S203: Store the divided original point cloud data and simplified point cloud data separately.

[0052] S3. Use Pointnet++ to perform semantic segmentation on the on-site point cloud data, extract door openings, wall openings and other parts, and render the model. The on-site point cloud data refers to the simplified point cloud data in step S203.

[0053] S4, performing plane extraction on the area selected in the model block list to obtain a plane data set, and performing plane extraction on the area selected in the model block list, including the following steps:

[0054] S401, select the regional point cloud after block division to perform robustness plane test, and detect the minimum plane, that is, the robustness plane test can determine a group of point cloud data P i (i=1, 2, ..., n) whether it can represent a plane.

[0055] For a set of point cloud data on leaf nodes, the robustness plane test steps are as follows:

[0056] First, for the plane fitted by the point cloud on the leaf node, the plane can be represented by a pair of vector groups (C, N), where C is the center point of the plane and N is the normal vector of the plane, where:

[0057] C=[median(S x ), median(S y ), median(Sz )] T

[0058]

[0059] Among them, S x , S y , S z Represents the components of the point cloud coordinates on the x, y, and z axes, S Nx , S Ny , S Nz Represents the normal vector of the point cloud, which is obtained through principal component analysis.

[0060] Secondly, after successfully passing the plane distance test, plane angle test, and outlier test, this set of point cloud data can enter the growth and fusion stage.

[0061] Plane distance test: passed Obtain a set of observation values ​​D = {d1, d2, ..., d n},in, Coordinates of representative points; set interval I D as follows:

[0062] I D =[median(D)-αMAD(D),median(D)+αMAD(D)]

[0063] Among them, MAD = 1.4286 × median (|x i -median(X)|), α is set to 3; this is used as the basis for judging whether the point cloud distance is abnormal.

[0064] Plane angle test: obtained by comparing the angle data between the point cloud normal vector and the plane normal vector. Represents the normal vector of the point cloud, and obtains a set of observation values ​​of the angle between the point cloud normal vector and the plane normal vector Φ = {φ1, φ2, ..., φ n}. Set interval I Φ as follows:

[0065] I Φ =[median(Φ)-aMAD(Φ),median(Φ)+aMAD(Φ)]

[0066] The definitions of α and MAD are the same as those of the plane distance test, which are used as the basis for judging whether the point cloud normal vector is abnormal.

[0067] Outlier test: When more than 25% of the point cloud data in the leaf node group fails the plane distance test and the plane angle test, it is considered that the point cloud data group does not meet the robustness plane requirement. If the point cloud data group passes the outlier test, it enters the growth and fusion stage.

[0068] The robust plane check starts from the point cloud data on the leaf node in step S202. When the point cloud data on the leaf node meets the robust plane condition, it enters the growth and fusion stage. If the point cloud data in the eight child leaf nodes of the same parent node fail the robust plane check, the parent node will replace its own child node and enter the planarity check from the leaf node to the root until the minimum plane is detected.

[0069] S402. Repeat the growth and fusion of the detected minimum plane until a stable detection plane is reached, that is, growth refers to the expansion of the plane and the addition of sample points that do not belong to any other plane. At the same time, the sample points must meet the internal conditions of the plane and comply with the priority order: the internal condition means that the sample point is within the reasonable interval of the plane robustness test of the plane. Priority refers to the ordering of the normal vectors of the sample points and the normal vectors of different planes according to the size of the deviation, from small to large, and the plane with the smallest angle deviation will be given priority to growth. Fusion is the fusion of two planes. The two planes A and B need to meet the fusion conditions: adjacent, the normal vector directions are basically the same, and at least one sample in each plane meets the internal conditions of the other plane.

[0070] S5. Select a plane area to measure the relevant quality acceptance index. The measurement of the relevant quality acceptance index uses the information of the extracted plane (C, N), and extends three basic operations based on the pair of vectors: plane-plane distance measurement; plane-plane angle measurement; point-plane distance calculation. The measurement of each index is based on the above three basic operations.

[0071] Specifically, the plane-to-plane distance measurement is used to measure the distance between parallel planes. Two parallel planes (C1, N1) and (C2, N2) are selected. The distance d between the two planes is 12 It can be calculated using the following formula:

[0072]

[0073] The plane-to-plane distance calculation can be used to calculate the cross-sectional dimensions of concrete structure projects, indoor net height deviation of plastering projects, room span / depth deviation, etc.

[0074] Plane-plane angle measurement, select two plane areas (C1, N1), (C2, N2), the distance between the two planes θ 12 It can be calculated using the following formula:

[0075]

[0076] The plane-plane angle measurement can be used to measure the verticality of concrete structure projects, the verticality and squareness of masonry projects, the verticality, squareness of corners and the squareness of plastering projects.

[0077] Point-plane distance measurement, selected point coordinates For plane (C1, N1), the point-plane distance d′ can be calculated using the following formula:

[0078]

[0079] The point-plane distance measurement can be used for the surface flatness of concrete structure projects, top plate levelness, masonry project surface flatness, plastering project surface flatness, top plate levelness, ground surface flatness, and ground levelness extreme difference.

[0080] The present invention also provides a specific embodiment of a method for constructing an engineering quality allowable deviation detection system based on laser point cloud, comprising the following steps:

[0081] S1. Input the original point cloud data and perform point cloud thinning and segmentation, including the following steps:

[0082] S101, calling the third-party library PCL to randomly sample the imported point cloud data to a sampling ratio;

[0083] S102, using an octree to spatially divide the sampled point cloud data into blocks to form a block list;

[0084] S103, adjusting the point cloud data format to .las format for output.

[0085] S2. Segmenting and rendering the point cloud data, including the following steps:

[0086] S201, train the Pointnet++ network using the Stanford S3DIS dataset;

[0087] S202, using the trained Pointnet++ network to perform point cloud segmentation and rendering on the input point cloud data.

[0088] S3, performing plane extraction on the area selected from the model block list to obtain a plane data set, including the following steps:

[0089] S301: Perform plane detection on the selected block area, specifically:

[0090] First, the point cloud on the leaf nodes in the block area will be subjected to a robustness plane test. Assuming that the leaf node contains n points P i(i=1, 2, ..., n), calculate the center point C and the plane normal vector N of n points, expressed as (C, N);

[0091] Secondly, perform plane distance test, plane angle test, and calculate abnormal point data (outlier test);

[0092] Furthermore, if more than 25% of the point cloud data in the group of leaf nodes fail the plane distance test and the plane angle test, it is considered that the group of point cloud data does not meet the robust plane requirements; if the group of point cloud data passes the outlier test, it enters the growth and fusion stage; if the point cloud data in the 8 child leaf nodes of the same parent node all fail the robust planarity test, the parent node will replace its own child nodes and enter the planarity test, from the leaf node to the root, until the minimum plane is detected;

[0093] S302: Obtain plane attribute information (C, N) that passes the robustness plane test and form a plane list.

[0094] S4. The user selects the plane on which the corresponding index measurement is required, including the following steps:

[0095] S401, select the plane and point to be measured, and select the items to be measured;

[0096] S402: Determine whether the selected plane and point meet the measurement requirements.

[0097] S5. Calculate the corresponding indicators according to the selected measurement items, specifically:

[0098] (1) Flatness: includes the surface flatness of concrete structure engineering, the surface flatness of masonry engineering, the wall flatness of plastering engineering and the ground flatness. It is obtained by calculating the maximum deviation distance between each point in the plane area and the plane to which it belongs. The unit is mm. The point-plane distance calculation formula is:

[0099]

[0100] in, is the coordinate of the point, and (C, N) is the information.

[0101] (2) Verticality: includes the verticality of concrete structure engineering, masonry engineering, wall verticality of plastering engineering and the verticality of the front and side surfaces of door frames. It is obtained by selecting the length H of the simulation ruler and combining the plane-plane angle calculation formula. The unit is mm and is expressed as l:

[0102]

[0103] H is 1m or 2m. When the wall height is less than 2m, a 1m long analog ruler can be used for detection.

[0104] (3) Levelness: including the extreme difference in levelness of the top plate of the concrete structure project and the extreme difference in levelness of the top plate of the plastering project. The plane where the 3D laser scanner is located is used as the reference horizontal plane. The vertical distance between the four corner points and one center point of the top plate plane and the reference horizontal plane is calculated. The point-plane distance calculation formula is used. The distance to the lowest point is used as the reference point. The deviation between the other four points and the lowest point is calculated. The threshold is used to determine whether it is qualified.

[0105] (4) Sectional dimensions: including the section dimensions of concrete structure engineering. For the deviation between the construction and design dimensions of shear walls and concrete columns within the storey height range, the section dimensions of the target components are calculated once at 300 mm and 1500 mm above the ground plane, using the plane-to-plane distance calculation formula, in mm:

[0106]

[0107] d 12 is the distance between two planes (C1, N1) and (C2, N2).

[0108] (5) Squareness: This includes the squareness of masonry works and the squareness of plastering works, which comprehensively reflects the squareness of the same room. Select two planes P for measuring squareness, and translate the center point C of the plane by 50 cm in the direction of the plane normal vector N to obtain point C′, which is actually equivalent to obtaining a new plane P′, whose attribute information is (C′, N). According to the point-plane distance, calculate the distances d1, d2, and d3 from the points on both sides and in the middle of the original plane P to the new plane P′. Calculate whether the range of the three is within [0, 10] mm as an indicator for judging squareness.

[0109] (6) Indoor net height deviation: including the measurement of indoor net height deviation in plastering engineering, which comprehensively reflects the degree of deviation between the actual measured value and the theoretical value of the indoor net height of the same room; select one point at each of the four corners of the plane where the ground is located, about 30 cm away from the edge of the ground, and select one point at the geometric center of the ground, and calculate the five vertical distances between the ground and the plane where the ceiling is located according to the point-plane distance measurement method as the actual measured value of the indoor net height.

[0110] (7) Squareness of internal and external corners: This includes the measurement of the squareness of internal and external corners in plastering projects. Take the two planes that form the internal and external corners and measure them once respectively at 300 mm and 1500 mm above the ground according to the plane-plane angle calculation formula. The two measured values ​​are used as the two calculation points for judging the pass rate of this measured indicator.

[0111] (8) Ground levelness: This includes the extreme difference in ground levelness during the plastering project. Take the four corner points and one midpoint of the ground plane, calculate the extreme difference in the distance between the five points and the horizontal plane where the three-dimensional laser scan is located, and use the point-to-plane calculation formula to comprehensively reflect the ground levelness of the leveling layer in the same room.

[0112] (9) Other projects involving simple plane-to-plane distance calculations include: the depth deviation of the house span during the plastering project, the size deviation of indoor door openings, and the extreme difference in the thickness of the inner wall of the exterior window. Select the plane to be measured and calculate the distance between the two planes.

[0113] The engineering quality allowable deviation detection system based on laser point cloud constructed according to the above method includes a user operation interface module, a three-dimensional rendering module, a plane detection module, and a measurement calculation module. Among them: the user operation module is used for (1) file import, (2) displaying the rendering model, (3) displaying the model block list, (4) providing the plane list, and (5) outputting the measurement information; the user operation interface module is used to provide a user operation interface and a graphical interactive interface, including the following functions: file import, displaying the rendering model, displaying the model block list, providing the plane list, and outputting the measurement information. File import means that the point cloud data file can be selected and uploaded to the system platform for processing; displaying the model block list means using the point cloud semantic segmentation model to identify the components of the imported point cloud data and form a component block list; providing the plane list means that after selecting the model block area, the plane within the area is detected by the plane detection algorithm and the corresponding plane list is formed; the measurement information means outputting the quality information of the appearance quality, position and size according to the relevant standards of the national construction quality acceptance, involving concrete structure engineering, plastering engineering, masonry engineering, and interior door installation engineering. The 3D rendering module is used to segment and render the point cloud and highlight the selected area; point cloud segmentation and rendering refers to the use of deep learning models to segment the point cloud into components and render the model interactively; highlighting refers to displaying the selected plane or selected component area in a color that is different from other planes and component areas. The plane detection module is used to extract the plane information of the imported point cloud data; the plane information of the point cloud data refers to a pair of vectors (C, N) represented by the center point C on the plane and the plane normal vector N, which can be used to represent the plane; the measurement and calculation module is used to calculate the numerical results of the quality inspection items based on the selected planes and points. The inspection items are formulated in accordance with the relevant national standards for the quality acceptance of construction projects.

[0114] like Figure 3 As shown, the engineering quality allows the deviation detection system to run, including the following steps:

[0115] S1. The user reads the data file, which is divided into blocks, simplified, segmented and rendered and loaded into the client interface;

[0116] S2. The user selects the block area to be detected, and the system extracts the plane of the block area and loads the display result plane;

[0117] S3. The user selects the plane and point that need to be measured for the corresponding indicator, and the system determines whether the selected plane and point meet the measurement requirements of the indicator;

[0118] S4. The system measures the corresponding indicators and outputs the measurement result file.

[0119] The specific operation process and involved formulas in each module are the same as those in the construction method and will not be repeated here.

[0120] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A system for detecting allowable deviation of engineering quality based on laser point cloud, characterized in that: It includes a user operation interface module, a three-dimensional rendering module, a plane detection module and a measurement and calculation module; The user operation interface module is used to provide a user operation interface and a graphical interactive interface, specifically including: file import, display rendering model, display model block list, provide plane list and output measurement information; The three-dimensional rendering module is used for point cloud segmentation, rendering and highlighting; The plane detection module is used to extract plane information of the imported point cloud data; The measurement and calculation module is used to calculate the numerical results of the quality inspection items; The file import is to select a point cloud data file and upload it to the system platform for processing; the model block list is to use octree to spatially divide the point cloud data after thinning the imported point cloud to a certain proportion to form a component block list; the plane list is to form a corresponding plane list after the model block area is selected by the plane detection algorithm; the regional point cloud after the block is selected is selected for robustness plane inspection to detect the minimum plane; the detected minimum plane is repeatedly grown and merged until a stable detection plane is reached; The measurement information is quality information of appearance quality, position and size.

2. The engineering quality allowable deviation detection system based on laser point cloud according to claim 1 is characterized in that: The point cloud segmentation and rendering is to use a deep learning model to segment the point cloud into components and render the model interactively; the highlight display is to display the selected plane or the selected component area in a color that is different from other planes and component areas.

3. The engineering quality allowable deviation detection system based on laser point cloud according to claim 1 is characterized in that: The plane information of the point cloud data is based on the center point on the plane. C and the plane normal vector N A pair of vectors ( C, N ) to represent a plane.

4. A method for constructing an engineering quality allowable deviation detection system based on laser point cloud according to claim 3, characterized in that: The following steps are involved: S1, obtain on-site point cloud data in real time by laser scanning on-site entities; S2, importing the on-site point cloud data, and performing block division and thinning simplification; S3. Use Pointnet++ to perform semantic segmentation on the simplified on-site point cloud data, extract the locations of door openings and wall openings, and render the model; S4, performing plane extraction on the area selected from the model block list to obtain a plane data set; S5. Select a plane area to measure the quality acceptance indicators.

5. The method for constructing an engineering quality allowable deviation detection system based on laser point cloud according to claim 4 is characterized in that: The step S2 comprises the following steps: S201, thinning and simplifying the on-site point cloud data to reduce point cloud density and file size; S202, using an octree to spatially divide the on-site point cloud data until the number of samples contained in the leaf nodes is 0.1% of the total number of samples; S203, storing the divided original on-site point cloud data and simplified on-site point cloud data separately.

6. The method for constructing an engineering quality allowable deviation detection system based on laser point cloud according to claim 4, characterized in that: The robustness plane test comprises the following steps: S40101. For the plane fitted by the point cloud on the leaf node, the plane is represented by a pair of vectors express, is the center point of the plane, is the normal vector of the plane, where: Represents the components of the point cloud coordinates on the x, y, and z axes, Represents the normal vector of the point cloud, which is obtained by principal component analysis; S40102. After the group point cloud data successfully passes the plane distance test, plane angle test, and outlier test, it enters the growth and fusion stage.

7. The method for constructing an engineering quality allowable deviation detection system based on laser point cloud according to claim 4, characterized in that: The measurement of the relevant quality acceptance index utilizes the information of the extracted plane ( C,N ), based on this pair of vectors, three basic operations are extended, including plane-plane distance measurement, plane-plane angle measurement, and point-plane distance calculation; The plane-to-plane distance measurement is used for calculating the cross-sectional dimensions of concrete structure engineering, indoor net height deviation of plastering engineering, and room span / depth deviation; The plane-plane angle measurement is used to measure the verticality of concrete structure engineering, the verticality and squareness of masonry engineering, the verticality, squareness of internal and external corners, and squareness of plastering engineering; The point-plane distance measurement is used for measuring the surface flatness of concrete structure engineering, the horizontality of top plate, the surface flatness of masonry engineering, the surface flatness of plastering engineering, the horizontality of top plate, the surface flatness of ground, and the extreme difference of ground horizontality.

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