Steel pipe arch rib pre-assembling method, device and system and storage medium

Abstract

The invention provides a steel pipe arch rib pre-assembling method, device and system and a storage medium, and belongs to the technical field of steel pipe splicing. The method comprises the steps that a to-be-assembled steel pipe arch rib section and a neighbor steel pipe arch rib section are scanned through a three-dimensional laser scanner to obtain original point cloud data and original adjacent point cloud data; performing control point analysis on the original point cloud data and the original adjacent point cloud data to obtain an initial assembly surface control point three-dimensional coordinate set and an initial adjacent assembly surface control point three-dimensional coordinate set; and performing pre-assembly analysis on the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set to obtain a pre-assembly result of the steel pipe arch rib. According to the method, the engineering efficiency is improved, errors caused by traditional manual measurement are avoided, the manpower and material resource cost is saved, the on-site pre-assembly frequency is reduced, the construction period is shortened, and a foundation is laid for subsequent overall linear calculation and digital modeling of the steel pipe arch rib.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of steel pipe assembly, and in particular to a steel pipe arch rib pre-assembly method, device, system and storage medium. Background Art

[0002] During the construction of concrete-filled steel tube arch bridges, the machining accuracy and assembly quality of the arch rib segments directly determine the bridge's alignment and structural safety. Traditional pre-assembly requires transporting components to a dedicated site for physical assembly, which has the following limitations: It consumes significant time and transportation costs, especially for long-span bridges, where pre-assembly cycles can take weeks. Site conditions limit full-scale simulations. Physical assembly relies on manual measurement, whose accuracy is affected by factors such as ambient temperature and instrument errors, and repeated adjustments lead to low efficiency.

[0003] In recent years, 3D laser scanning technology has provided a new approach for digital inspection of prefabricated components. By acquiring point cloud data from the surface of steel arch rib segments and using computer technology to perform virtual pre-assembly to check the overall arch rib linear shape, this method avoids the tedious physical pre-assembly and reduces site usage. However, most current digital virtual assembly methods require adjusting the pre-assembly posture of the physical 3D point cloud model in conjunction with the BIM model. This often involves multiple posture adjustments, and poor matching between the theoretical model and the actual model results in low virtual pre-assembly accuracy. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a steel tube arch rib pre-assembly method, device, system and storage medium in response to the deficiencies in the prior art.

[0005] The present invention solves the above technical problems with the following technical solutions: A method for pre-assembling steel tube arch ribs comprises the following steps:

[0006] Scanning the steel tube arch rib segment to be assembled and the neighboring steel tube arch rib segments adjacent to the steel tube arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segments;

[0007] Performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of initial assembly surface control points of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel tube arch rib segment;

[0008] A pre-assembly analysis is performed on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain a pre-assembly result of the steel tube arch rib.

[0009] Another technical solution of the present invention to solve the above technical problem is as follows: a steel tube arch rib pre-assembly device, comprising:

[0010] a scanning module for scanning the steel pipe arch rib segment to be assembled and the neighboring steel pipe arch rib segments adjacent to the steel pipe arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel pipe arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel pipe arch rib segments;

[0011] a control point analysis module for performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of control points of an initial assembly surface of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of control points of an initial adjacent assembly surface of the neighboring steel tube arch rib segment;

[0012] The pre-assembly result obtaining module is used to perform pre-assembly analysis on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain the pre-assembly result of the steel tube arch rib.

[0013] Based on the above-mentioned steel tube arch rib pre-assembly method, the present invention also provides a steel tube arch rib pre-assembly system.

[0014] Another technical solution of the present invention to solve the above-mentioned technical problem is as follows: a steel tube arch rib pre-assembly system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steel tube arch rib pre-assembly method as described above is implemented.

[0015] Based on the above-mentioned steel tube arch rib pre-assembly method, the present invention also provides a computer-readable storage medium.

[0016] Another technical solution of the present invention to solve the above technical problem is as follows: a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steel tube arch rib pre-assembly method as described above is implemented.

[0017] The beneficial effects of the present invention are as follows: original point cloud data and original adjacent point cloud data are obtained by scanning the steel pipe arch rib segment to be assembled and the neighboring steel pipe arch rib segment with a three-dimensional laser scanner, the control points of the original point cloud data and the original adjacent point cloud data are analyzed to obtain the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points, and the pre-assembly result of the steel pipe arch rib is obtained by pre-assembly analysis of the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points, which improves engineering efficiency, avoids errors in traditional manual measurement, saves manpower and material costs, reduces the number of pre-assemblies on site, shortens the construction period, and lays the foundation for subsequent overall linear calculation and digital modeling of the steel pipe arch rib. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for pre-assembling steel tube arch ribs according to an embodiment of the present invention;

[0019] Figure 2 This is a module block diagram of the steel tube arch rib pre-assembly device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0021] Figure 1 A schematic flow chart of a method for pre-assembling steel tube arch ribs provided in an embodiment of the present invention.

[0022] like Figure 1 As shown, a method for pre-assembling steel tube arch ribs includes the following steps:

[0023] Scanning the steel tube arch rib segment to be assembled and the neighboring steel tube arch rib segments adjacent to the steel tube arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segments;

[0024] Performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of initial assembly surface control points of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel tube arch rib segment;

[0025] A pre-assembly analysis is performed on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain a pre-assembly result of the steel tube arch rib.

[0026] It should be understood that the neighboring steel tube arch rib segment may be a steel tube arch rib segment adjacent to the steel tube arch rib segment to be assembled.

[0027] It should be understood that the scanning sites are planned and arranged, and the three-dimensional laser scanner and the rotating triangular pyramid target are set according to the site location. The three-dimensional laser scanner scans the steel pipe arch rib segments (i.e., the steel pipe arch rib segments to be assembled and the neighboring steel pipe arch rib segments) to obtain on-site point cloud data (i.e., the original point cloud data and the original adjacent point cloud data).

[0028] Specifically, the planning and layout of scanning sites and target placement are as follows:

[0029] At least 4 stations are set up in each arch rib segment;

[0030] A station is set up at each end of the splicing surface. The distance dz between the instrument and the end face of the arch rib segment should meet the requirement of 5m≤dz≤10m, so as to better collect the end face detail data.

[0031] Stations are set on the side of the arch rib segment according to the length of the arch rib. If the length of the arch rib segment is between 20m and 35m, stations can be set in the middle; and the scanning distance dz should meet the requirement of 10m≤dz≤15m;

[0032] Set up the 3D laser scanner at the site and place the triangular pyramid target so that it can be seen from adjacent stations. When scanning stations A and B, rotate the target so that it faces the scanner.

[0033] In the above embodiment, the steel pipe arch rib segment to be assembled and the neighboring steel pipe arch rib segments are scanned by a three-dimensional laser scanner to obtain original point cloud data and original adjacent point cloud data, the control points of the original point cloud data and the original adjacent point cloud data are analyzed to obtain the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set, the pre-assembly result of the steel pipe arch rib is obtained by pre-assembly analysis of the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set, which improves engineering efficiency, avoids errors in traditional manual measurement, saves manpower and material costs, reduces the number of on-site pre-assemblies, shortens the construction period, and lays the foundation for subsequent overall linear calculation and digital modeling of the steel pipe arch rib.

[0034] Optionally, as an embodiment of the present invention, the process of performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment to obtain a three-dimensional coordinate set of initial assembly surface control points of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel tube arch rib segment includes:

[0035] Performing registration analysis on all the original point cloud data to obtain a plurality of initial flange plane point cloud data;

[0036] Preprocessing all the initial flange plane point cloud data to obtain a first rotation matrix, a first center of mass coordinate, and a plurality of target flange plane point cloud data;

[0037] Extract control points from all target flange plane point cloud data according to the first rotation matrix and the first centroid coordinates to obtain a three-dimensional coordinate set of control points of the initial assembly surface of the steel pipe arch rib segment to be assembled;

[0038] Performing registration analysis on all the original adjacent point cloud data to obtain a plurality of initial adjacent flange plane point cloud data;

[0039] Preprocessing all the initial adjacent flange plane point cloud data to obtain a second rotation matrix, a second centroid coordinate, and a plurality of target adjacent flange plane point cloud data;

[0040] Control points of all target adjacent flange plane point cloud data are extracted according to the second rotation matrix and the second centroid coordinates to obtain a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel pipe arch rib segment.

[0041] It should be understood that the process of performing registration analysis on all the original point cloud data and the process of performing registration analysis on all the original adjacent point cloud data have the same data processing process, and only the processed data are different.

[0042] It should be understood that the process of preprocessing all the initial flange plane point cloud data and the process of preprocessing all the initial adjacent flange plane point cloud data are the same data processing process, and only the processed data are different.

[0043] Specifically, the process of extracting control points for all the target flange plane point cloud data based on the first rotation matrix and the first center of mass coordinates and the process of extracting control points for all the target adjacent flange plane point cloud data based on the second rotation matrix and the second center of mass coordinates have the same data processing process, and only the processed data is different.

[0044] In the above embodiment, the control points of the original point cloud data and the original adjacent point cloud data are analyzed to obtain the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points, which improves engineering efficiency, avoids errors in traditional manual measurement, saves manpower and material costs, and reduces the number of on-site pre-assembly times.

[0045] Optionally, as an embodiment of the present invention, the process of performing registration analysis on all the original point cloud data to obtain a plurality of initial flange plane point cloud data includes:

[0046] Scanning each of the original point cloud data A and any adjacent original point cloud data B respectively to obtain a plurality of first original target point cloud data corresponding to each of the original point cloud data A and a plurality of second original target point cloud data corresponding to each of the original point cloud data B;

[0047] extracting a plurality of first original plane equations corresponding to each of the first original target point cloud data respectively from each of the first original target point cloud data;

[0048] extracting a plurality of second original plane equations corresponding to each of the second original target point cloud data respectively from each of the second original target point cloud data;

[0049] The first original plane equation and the second original plane equation are defined as:

[0050] a0x1+b0y1+c0z1+d0=0,

[0051] Wherein, a0 is the first X-axis normal vector component, b0 is the first Y-axis normal vector component, c0 is the first Z-axis normal vector component, and d0 is the first plane offset;

[0052] Calculating parameters of first original plane equations corresponding to the first original target point cloud data using a random sampling consistency method and the first original target point cloud data to obtain first plane parameter sets corresponding to the first original plane equations;

[0053] performing parameter updates on each of the first original plane equations according to each of the first plane parameter sets to obtain a plurality of first updated plane equations corresponding to each of the first original target point cloud data;

[0054] Utilizing a random sampling consensus method and each of the second original target point cloud data, parameter calculations are performed on each of the second original plane equations corresponding to each of the second original target point cloud data to obtain a second plane parameter set corresponding to each of the second original plane equations;

[0055] performing parameter updates on each of the second original plane equations according to each of the second plane parameter sets to obtain a plurality of second updated plane equations corresponding to each of the second original target point cloud data;

[0056] Calculating intersection points of a plurality of first updated plane equations in each of the first original target point cloud data respectively, thereby obtaining first feature point coordinates corresponding to each of the first original target point cloud data;

[0057] respectively calculating intersection points of a plurality of second updated plane equations in each of the second original target point cloud data, thereby obtaining coordinates of second feature points corresponding to each of the second original target point cloud data;

[0058] Collecting the plurality of first feature point coordinates corresponding to each of the original point cloud data A to obtain a first initial feature point coordinate set corresponding to each of the original point cloud data A;

[0059] respectively collecting a plurality of second feature point coordinates corresponding to each of the original point cloud data B to obtain a second initial feature point coordinate set corresponding to each of the original point cloud data B;

[0060] Using a fast four-point consensus set algorithm, coarse registration is performed on each of the first initial feature point coordinate sets and the second initial feature point coordinate set corresponding to each of the original point cloud data B to obtain a plurality of coarsely registered feature point coordinate sets;

[0061] Using the ICP algorithm to perform fine registration processing on each of the coarsely registered feature point coordinate sets, to obtain an initial rotation matrix corresponding to each of the coarsely registered feature point coordinate sets and a translation vector corresponding to each of the coarsely registered feature point coordinate sets;

[0062] The Cloud Compare tool is used to segment and extract all the initial rotation matrices and all the translation vectors to obtain a plurality of initial flange plane point cloud data.

[0063] It should be understood that for the on-site registration of multi-station arch rib point cloud data (i.e., original point cloud data), the inter-station arch rib segment point cloud data registration can be achieved based on the spatial transformation matrix obtained by the inter-station rotating triangular pyramid target point cloud registration.

[0064] Specifically, the registration process based on the rotating triangular pyramid target is as follows:

[0065] Assume that the number of targets visible between the two stations should be no less than 2. There are k targets between the two stations, and the target placement points cannot be collinear. Where Ai and Bi are the point clouds of the i-th cone target in scan point cloud A and scan point cloud B, respectively (i.e., the first original target point cloud data and the second original target point cloud data).

[0066] The cone plane is fitted using the random sampling consistency method, and the fitted plane equation (i.e., the first original plane equation and the second original plane equation) can be expressed as:

[0067] ax+by+cz+d=0

[0068] Where: a, b, c, d are all plane feature parameters. RANSAC sampling is performed on Ai (i.e., the first original target point cloud data). The parameters a, b, c, d are calculated using the data of each sampling. The coordinates corresponding to the maximum value of the probability density distribution of the parameters aj, bj, cj, dj (i.e., the first plane parameter set) are taken as the deterministic solution of the plane equation coefficients. A target has three non-intersecting planes, and each plane parameter has three sets of peaks, j = 1, 2, 3. Similarly, for B i (i.e. the second original target point cloud data) to calculate the plane parameters.

[0069] Calculate the intersection of the three planes fitted in scanning station A and station B, that is, the feature point coordinates ai (that is, the first feature point coordinates ) , Bi’s feature point coordinates bi (i.e., the second feature point coordinates), combine the feature points of k targets, and the target feature point sets in station A and station B (i.e., the first initial feature point coordinate set and the second initial feature point coordinate set) are Ai={i|i=1:k}, Bi={bi|i=1:k}

[0070] Use the fast four-point consensus set algorithm to identify the target feature point set A k 、B k The first and second initial feature point coordinate sets are aligned to complete the coarse registration of the scanned point cloud data. The ICP algorithm is then used to finely align the target feature point set. The rotation matrix and translation vector are obtained, enabling efficient and fine registration of the two site clouds.

[0071] It should be understood that the Nth arch rib segment data after registration (i.e., the initial rotation matrix and translation vector) is imported into the Cloud compare point cloud data processing software (i.e., Cloud Compare tool), and the arch rib segment assembly surface (flange plane) is segmented and extracted using the region cropping function, and saved in PCD file format.

[0072] In the above embodiment, all original point cloud data are registered and analyzed to obtain a plurality of initial flange plane point cloud data, thereby avoiding traditional manual measurement errors and achieving millimeter-level accuracy.

[0073] Optionally, as an embodiment of the present invention, the process of preprocessing all the initial flange plane point cloud data to obtain the first rotation matrix, the first center of mass coordinates and the plurality of target flange plane point cloud data includes:

[0074] Performing noise reduction analysis on all the initial flange plane point cloud data to obtain a first centroid coordinate, a fourth updated plane equation, and a plurality of processed flange plane point cloud data;

[0075] A rotation analysis is performed on all the processed flange plane point cloud data according to the first centroid coordinates and the fourth updated plane equation to obtain a plurality of target flange plane point cloud data.

[0076] In the above embodiment, all initial flange plane point cloud data are preprocessed to obtain the first rotation matrix, the first center of mass coordinates and multiple target flange plane point cloud data, which solves the problem of extracting three-dimensional point feature points and significantly improves the accuracy and reliability of control point positioning.

[0077] Optionally, as an embodiment of the present invention, the process of performing noise reduction analysis on all the initial flange plane point cloud data to obtain the first centroid coordinates, the fourth updated plane equation, and the plurality of processed flange plane point cloud data includes:

[0078] Define the equation of the third primitive plane as:

[0079] a1x2+b1y2+c1z2+d1=0,

[0080] Wherein, a1 is the second X-axis normal vector component, b1 is the second Y-axis normal vector component, c1 is the second Z-axis normal vector component, and d1 is the second plane offset;

[0081] Construct a hybrid loss function, which is:

[0082]

[0083] Where L is the mixed loss value, a2 is the third X-axis normal vector component, b2 is the third Y-axis normal vector component, c2 is the third Z-axis normal vector component, d2 is the third plane offset, n is the number of initial flange plane point cloud data, and λ is the Lagrange multiplier;

[0084] Solving the hybrid loss function to obtain a third plane parameter set;

[0085] performing parameter updating on the third original plane equation according to the third plane parameter set to obtain a third updated plane equation;

[0086] The first centroid coordinates are obtained by calculating all the initial flange plane point cloud data using the first formula, which is:

[0087]

[0088] in,

[0089] in, is the first centroid coordinate, is the X-axis average value of the initial flange plane point cloud data, is the Y-axis average value of the initial flange plane point cloud data, is the Z-axis average value of the initial flange plane point cloud data, n is the number of initial flange plane point cloud data, x4 i is the X-axis coordinate of the i-th initial flange plane point cloud data, y4 i is the Y-axis coordinate of the i-th initial flange plane point cloud data, z4 i is the Z-axis coordinate of the i-th initial flange plane point cloud data;

[0090] Performing eigenvalue decomposition on the first centroid coordinates and all the initial flange plane point cloud data using a covariance matrix eigenvalue decomposition algorithm to obtain a first eigenvalue, a first flange eigenvector corresponding to the first eigenvalue, a second eigenvalue, a second flange eigenvector corresponding to the second eigenvalue, a third eigenvalue, and a third flange eigenvector corresponding to the third eigenvalue;

[0091] Screening out the minimum value of the first eigenvalue, the second eigenvalue, and the third eigenvalue, and after screening, using the first flange eigenvector, the second flange eigenvector, or the third flange eigenvector corresponding to the minimum value as the plane normal vector parameter group;

[0092] Solving the plane normal vector parameter group and the first centroid coordinates using the third original plane equation to obtain a fourth plane offset;

[0093] performing parameter updating on the third updated plane equation according to the plane normal vector parameter group and the fourth plane offset to obtain a fourth updated plane equation;

[0094] respectively calculating the distance between each of the initial flange plane point cloud data and the fourth updated plane equation to obtain a target distance corresponding to each of the initial flange plane point cloud data;

[0095] The average error value is obtained by calculating all the target distances using the second formula, which is:

[0096]

[0097] Among them, δ is the average error value, D i is the target distance corresponding to the i-th initial flange plane point cloud data, and n is the number of initial flange plane point cloud data;

[0098] If the target distance satisfies the determination condition, the initial flange plane point cloud data corresponding to the target distance is used as the flange plane point cloud data to be processed, thereby obtaining a plurality of flange plane point cloud data to be processed. The determination condition is:

[0099] D i >1.5δ,

[0100] Among them, D i is the target distance corresponding to the i-th initial flange plane point cloud data, and δ is the average error value;

[0101] Determine whether the target distances all meet the judgment conditions, and whether the absolute value of the difference between the average error value of the current iteration number and the average error value of the previous iteration number is less than a first preset threshold value. If not, reconstruct the process of the hybrid loss function; if so, all the flange plane point cloud data to be processed are used as the processed flange plane point cloud data, thereby obtaining multiple processed flange plane point cloud data.

[0102] It should be understood that the plane point distance constraint condition is used to perform noise reduction processing on the arch rib segment assembly surface (flange plane) (ie, the initial flange plane point cloud data).

[0103] It should be understood that the optimal plane of the flange (ie, the fourth updated plane equation) is fitted based on the least squares plane fitting method.

[0104] Specifically, the plane equation parameters are set to satisfy the normal vector normalization condition, that is, a 2 +b 2 +c 2 =1, ensuring the uniqueness of the plane direction; Objective function construction: Taking the minimization of the sum of squares of the distance from the point cloud to the plane as the optimization goal, the hybrid loss function is constructed by combining the Lagrange multiplier method, and the normalization condition of the plane normal vector is coupled with the error function; Eigenvalue decomposition solution: The eigenvalue decomposition method of the covariance matrix is ​​used to obtain the eigenvector corresponding to the minimum eigenvalue as the plane normal vector parameters a, b, c (i.e., the plane normal vector parameter group), combined with the centroid coordinates The offset d (ie, the fourth plane offset) is calculated, and the optimal fitting plane equation (ie, the fourth updated plane equation) is finally determined.

[0105] Specifically, a dynamic threshold is set to iteratively remove the point cloud noise data that deviates from the flange plane, and the average error δ (i.e., the average error value) is calculated:

[0106]

[0107] Set the dynamic threshold to 1.5δ and delete d i >1.5δ abnormal points, d i For point p i =(x i ,y i ,z i) to the distance value of the fitting plane (i.e., the target distance), repeat the plane fitting and noise removal until it satisfies: the average error difference between the two iterations |δ k+1 -δ k |<ε, no new d i >1.5δ, the iteration is terminated, and the denoised point cloud data of the flange plane (i.e., the processed point cloud data of the flange plane) is output.

[0108] In the above embodiment, noise reduction analysis is performed on all initial flange plane point cloud data to obtain the first centroid coordinates, the fourth updated plane equation, and multiple processed flange plane point cloud data, effectively eliminating environmental interference and redundant data.

[0109] Optionally, as an embodiment of the present invention, the process of performing rotation analysis on all the processed flange plane point cloud data according to the first centroid coordinates and the fourth updated plane equation to obtain multiple target flange plane point cloud data includes:

[0110] Projecting the fourth updated plane equation and each of the processed flange plane point cloud data is performed respectively through the third, fourth, and fifth equations to obtain projected flange plane point cloud data corresponding to each of the processed flange plane point cloud data. The third equation is:

[0111] x1 j ′=

[0112] x5 j ×(b3 2 +c3 2 )-a3×(y5 j ×b3+z5 j ×c3+d3) / norm(|a3,b3,c3|), the fourth formula is:

[0113] y1′ j =

[0114] y5 j ×(a3 2 +c3 2 )-b3×(x5 j ×a3+z5 j ×c3+d3) / norm(|a3,b3,c3|), the fifth formula is:

[0115] z1 j ′=

[0116] z5 j ×(a3 2 +b3 2 )-c3×(x5 j ×a3+y5j ×b3+d3) / norm(|a3,b3,c3|),

[0117] Among them, x1 j ′ is the X-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, y1′ j z1 is the Y-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, j ′ is the Z-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, x5 j is the X-axis coordinate of the j-th processed flange plane point cloud data, y5 j is the Y-axis coordinate of the j-th processed flange plane point cloud data, z5 j is the Z-axis coordinate of the j-th processed flange plane point cloud data, a3 is the fourth X-axis normal vector component, b3 is the fourth Y-axis normal vector component, c3 is the fourth Z-axis normal vector component, d3 is the fourth plane offset, and norm(||) is the Euclidean length function;

[0118] Performing accuracy verification on all the projected flange plane point cloud data according to preset verification rules; if the verification fails, re-projecting the fourth updated plane equation and each of the processed flange plane point cloud data using the third formula, the fourth formula, and the fifth formula respectively; if the verification succeeds, using the projected flange plane point cloud data as the flange plane point cloud data to be rotated, thereby obtaining a plurality of flange plane point cloud data to be rotated;

[0119] Extracting a first angle and a second angle from the fourth updated plane equation, the first angle being the angle between the normal vector of the fourth updated plane equation and the Z axis when projected in the YOZ coordinate system, and the second angle being the angle between the normal vector of the fourth updated plane equation and the Z axis when projected in the XOZ coordinate system;

[0120] A first rotation matrix is ​​constructed by using the first angle and the second angle. The first rotation matrix is:

[0121]

[0122] Where R1 is the first rotation matrix, α is the first angle, and β is the second angle;

[0123] Taking the first centroid coordinate as the rotation center, all the plane point cloud data of the flange to be rotated are rotated using the first rotation matrix to obtain a plurality of target flange plane point cloud data.

[0124] It should be understood that the flange spatial plane point cloud after noise reduction processing (i.e. the processed flange plane point cloud data) is projected into the plane coordinate system of the best fitting plane (i.e. the fourth updated plane equation), and the projected point cloud data (i.e. the projected flange plane point cloud data) is rotated to be parallel to the XOY plane.

[0125] It should be understood that the plane fitting calculation is performed on the flange plane point cloud by the least squares fitting plane method to obtain the optimal fitting plane, where a, b and c are the normal vector components corresponding to the optimal fitting plane, and d is the position parameter of the optimal fitting plane relative to the coordinate origin.

[0126] Specifically, the flange plane point cloud p is calculated using the coordinate calculation formula i =(x i ,y i ,z i ) (i.e., the processed flange plane point cloud data) is orthogonally projected to the new coordinates on the optimal fitting plane (i.e., the fourth updated plane equation), and the coordinate calculation formula is:

[0127] x i ′=x i ×(b 2 +c 2 )-a×(y i ×b+z i ×c+d) / norm(|a,b,c|),

[0128] y i ′=y i ×(a 2 +c 2 )-b×(x i ×a+z i ×c+d) / norm(|a,b,c|),

[0129] z i ′=z i ×(a 2 +b 2 )-c×(x i ×a+y i ×b+d) / norm(|a,b,c|),

[0130] Among them, the projected point coordinates (i.e. the projected flange plane point cloud data) are p′ i =(x′ i ,y′ i ,z′ i ), norm(|a,b,c|) is the Euclidean length of the optimal fitting plane normal vector;

[0131] The flange plane point cloud (i.e., the processed flange plane point cloud data) is projected onto the plane of the optimal fit plane (i.e., the fourth updated plane equation) to obtain the projection point cloud set of the flange plane (i.e., the projected flange plane point cloud data); the accuracy of the projected point cloud data set (i.e., the projected flange plane point cloud data) is verified to ensure that the error in the projection process is within the preset range to ensure the accuracy of the subsequent flange and bolt hole contour extraction. The optimal fit plane (i.e., the fourth updated plane equation) is rotated to be parallel to the XOY plane, and its rotation matrix (i.e., the first rotation matrix) is:

[0132]

[0133] Where: α is the angle between the plane normal vector and the Z axis in the YOZ coordinate system projection (i.e., the first angle), β is the angle between the plane normal vector and the Z axis in the XOZ coordinate system projection (i.e., the second angle); sinα, cosα, sinβ, and cosβ can all be calculated from the optimal fitting plane normal vector n; the projection point cloud set of the flange plane (i.e., the flange plane point cloud data after projection) is calculated with the centroid as the center. The rotation matrix R (ie, the first rotation matrix) is used to rotate the XOY plane to be parallel to the XOY plane.

[0134] In the above embodiment, the processed flange plane point cloud data is rotated and analyzed according to the first centroid coordinates and the fourth updated plane equation to obtain multiple target flange plane point cloud data, thereby avoiding the errors of traditional manual measurement, saving manpower and material costs, and reducing the number of on-site pre-assembly times.

[0135] Optionally, as an embodiment of the present invention, the process of extracting control points from all the target flange plane point cloud data according to the first rotation matrix and the first centroid coordinates to obtain a three-dimensional coordinate set of control points of the initial assembly surface of the steel pipe arch rib segment to be assembled includes:

[0136] Extracting a plurality of flange contour points and a plurality of bolt hole contour points from all the target flange plane point cloud data using an adaptive Alpha-Shape algorithm, and collecting all the flange contour points and all the bolt hole contour points to obtain an initial contour point dataset;

[0137] Segmenting the initial contour point data set to obtain a plurality of initial contour point data subsets;

[0138] Using a least squares algorithm to fit each of the initial contour point data subsets, respectively, to obtain a circle equation corresponding to each of the initial contour point data subsets;

[0139] Construct an error function equation group, solve the error function equation group and each circle equation respectively, and obtain the initial circle center coordinates corresponding to each initial contour point data subset. The error function equation group is:

[0140]

[0141] in, is the X-axis coordinate of the kth initial contour point in the ath initial contour point data subset, is the Y-axis coordinate of the k-th initial contour point in the a-th initial contour point data subset, is the X-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, E is the error function, r a is the radius corresponding to the ath initial contour point data subset, and m is the number of initial contour points;

[0142] Performing homogeneous coordinate transformation on each of the initial circle center coordinates to obtain transformed circle center coordinates corresponding to each of the initial contour point data subsets;

[0143] The first rotation matrix, each of the initial contour point data subsets, and the converted circle center coordinates corresponding to each of the initial contour point data subsets are respectively transformed into three-dimensional coordinates by the sixth formula to obtain the target circle center coordinates corresponding to each of the initial contour point data subsets, and all of the target circle center coordinates are combined to obtain the three-dimensional coordinate set of the initial assembly surface control points of the steel pipe arch rib segment to be assembled. The sixth formula is:

[0144]

[0145] in, is the X-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the Z-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the X-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, R1 is the first rotation matrix, is the X-axis average value of the initial flange plane point cloud data, is the Y-axis average value of the initial flange plane point cloud data, is the Z-axis average value of the initial flange plane point cloud data.

[0146] It should be understood that the adaptive alpha-shape algorithm is used to extract the flange and bolt hole contour point sets (ie, the three-dimensional coordinate set of the initial assembly surface control points).

[0147] Specifically, the adaptive alpha-shape algorithm is as follows:

[0148] Parameter initialization: Set the initial search radius parameter α0 of the adaptive Alpha-Shape algorithm as the benchmark scale for neighborhood division;

[0149] Neighborhood construction: Based on the KD tree spatial index structure, each point p in the flange plane projection point cloud is i Perform neighborhood search and obtain i The spherical neighborhood point set H with the center and radius α0 i ;

[0150] Neighborhood density calculation: Count the point cloud density of each neighborhood, defined as the number of points in the neighborhood H i Ratio to the volume of spherical space

[0151] Dynamic optimization of radius: Dynamically adjust the search radius according to the local density gradient, increase the radius in sparse areas, and reduce the radius α in dense areas i , the formula is: Among them, ρ max is the global maximum density value;

[0152] Based on the dynamic radius α i Construct a scale-variable rolling ball model and traverse the point cloud to generate a topologically closed boundary;

[0153] Feature contour extraction: Identify the boundary point set covered by the Alpha-Shape model and output millimeter-level precision contour data of the flange plane projection point cloud (i.e., flange contour points and bolt hole contour points) for flange hole and bolt hole geometric parameter calculation.

[0154] It should be understood that the least squares method (i.e., the least squares algorithm) is used to perform circle fitting on the flange plate and bolt hole contours (i.e., the initial contour point data set) to solve the center coordinates of the circle (i.e., the initial center coordinates of the circle); and the obtained center coordinates of the circle (i.e., the initial center coordinates of the circle) are used to obtain the bolt hole flange hole coordinates (XYZ) (i.e., the target center coordinates of the circle) using the inverse rotation operation to obtain the three-dimensional coordinates of the assembly control points of the assembly surface (i.e., the initial assembly surface control point three-dimensional coordinate set).

[0155] Specifically, the extracted flange and bolt hole contour points (i.e., initial contour point data set) are classified and segmented, and each contour point set (i.e., initial contour point data subset) is recorded as U i, there are k contour point sets (i.e., the initial contour point data subset); contour point set U i The coordinate points in the initial contour point data subset are: (x1, y1), (x2, y2), ..., (x i ,y i ),…,(x n ,y n ), let the contour point set U i The center coordinates (i.e. the initial center coordinates) are (x0, y0), the radius of the circle is r, according to the circle equation (x-x0)+(y-y0) 2 = r The least squares method is used to fit the circle. Based on the error function E, the partial derivatives of the center coordinates (i.e., the initial center coordinates) and the radius are calculated and the error function E is set to 0. The error function E equations are solved to obtain the values ​​of the center coordinates (x0, y0) (i.e., the initial center coordinates) and the radius r. The error function E equations are:

[0156]

[0157] The center coordinates of the k solved contour point sets are grouped into a point set G (i.e., the initial center coordinates corresponding to each of the initial contour point data subsets), and the fitted center coordinates p0 = (x0, y0, 0) are converted into the corresponding homogeneous coordinates: p0 = [x0, y0, 0, 1] T , apply the inverse transformation to restore the center coordinate p in the original coordinate system c =(x c ,y c ,z c )(i.e. target circle center coordinates), where R T is the inverse matrix of R; it will restore the center point p in the original coordinate system ci (x ci ,y ci ,z ci ) are collected into D (i.e. the initial assembly surface control point three-dimensional coordinate set), which is the assembly surface control point set of digital pre-assembly (i.e. the initial assembly surface control point three-dimensional coordinate set); repeat the above steps to solve the assembly surface control point set of adjacent arch rib segments (i.e. the initial adjacent assembly surface control point three-dimensional coordinate set), which are D n-1 (i.e. the initial three-dimensional coordinate set of adjacent assembly surface control points), D (i.e. the initial three-dimensional coordinate set of assembly surface control points).

[0158] In the above embodiment, control points of all target flange plane point cloud data are extracted according to the first rotation matrix and the first centroid coordinates to obtain the three-dimensional coordinate set of the initial assembly surface control points of the steel pipe arch rib segment to be assembled, which solves the problem of extracting three-dimensional point feature points and significantly improves the accuracy and reliability of control point positioning.

[0159] Optionally, as an embodiment of the present invention, the process of performing pre-assembly analysis on the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set to obtain the pre-assembly result of the steel tube arch rib includes:

[0160] Using a fast four-point consensus set algorithm, the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set are respectively coarsely registered to obtain a registered assembly surface control point three-dimensional coordinate set corresponding to the initial assembly surface control point three-dimensional coordinate set and a registered adjacent assembly surface control point three-dimensional coordinate set corresponding to the initial adjacent assembly surface control point three-dimensional coordinate set;

[0161] An ICP error function is constructed, and the singular value decomposition algorithm is used to calculate the ICP error function, the three-dimensional coordinate set of the control points of the assembly surface after registration, and the three-dimensional coordinate set of the control points of the adjacent assembly surfaces after registration to obtain a third rotation matrix and a translation matrix. The ICP error function is:

[0162]

[0163] Among them, R2 is the third rotation matrix, T1 is the translation matrix, b is the three-dimensional coordinates of the control points of the assembly surface after registration or the number of three-dimensional coordinates of the control points of the adjacent assembly surfaces after registration, q u is the three-dimensional coordinate of the u-th adjacent assembly surface control point in the three-dimensional coordinate set of the adjacent assembly surface control points after registration, p u is the three-dimensional coordinate of the u-th control point of the assembled surface after registration in the three-dimensional coordinate set of the control points of the assembled surface after registration;

[0164] The three-dimensional coordinate set of the control points of the assembly surface after registration, the third rotation matrix and the translation matrix are calculated by the seventh formula to obtain the three-dimensional coordinate set of the control points of the assembly surface to be processed. The seventh formula is:

[0165] D″=R2D′+T1,

[0166] Where D″ is the three-dimensional coordinate set of the control points of the assembly surface to be processed, R2 is the third rotation matrix, T1 is the translation matrix, and D′ is the three-dimensional coordinate set of the control points of the assembly surface after registration;

[0167] The target average distance is obtained by calculating the three-dimensional coordinate set of the control points of the assembly surface to be processed and the three-dimensional coordinate set of the control points of the adjacent assembly surfaces after registration through the eighth formula, and the eighth formula is:

[0168]

[0169] in, is the average distance to the target, b is the three-dimensional coordinates of the control points of the assembly surface after registration or the number of three-dimensional coordinates of the control points of the adjacent assembly surfaces after registration, q u is the three-dimensional coordinate of the u-th adjacent assembly surface control point in the three-dimensional coordinate set of the adjacent assembly surface control points after registration, p′ u The three-dimensional coordinates of the u-th control point on the assembly surface to be processed in the three-dimensional coordinate set of the control points on the assembly surface to be processed;

[0170] Determine whether the target average distance is less than a second preset threshold. If not, re-use the fast four-point consistency set algorithm to roughly align the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points respectively; if so, use the third rotation matrix and the translation matrix as the pre-assembly results of the steel tube arch rib.

[0171] It should be understood that the points within the control point set of the splicing surfaces of adjacent arch rib segments are aligned, and the spatial transformation matrix (i.e., the third rotation matrix and the translation matrix) is output, and the spatial transformation matrix parameters (i.e., the third rotation matrix and the translation matrix) are applied to the complete arch rib segment point cloud data, so that the adjacent arch rib segments are digitally pre-spliced.

[0172] Specifically, the fast four-point consistent set algorithm is used to calculate the control point set D of the assembly surface of adjacent arch rib segments. n-1 (i.e., the initial three-dimensional coordinate set of adjacent assembly surface control points), D n (i.e. the initial assembly surface control point three-dimensional coordinate set) is roughly aligned; the assembly control point set D of the pre-assembled N segments is n (p1…p n ) (i.e. the three-dimensional coordinate set of the initial assembly surface control points) and the corresponding assembly control point set D on the N-1 segment n-1 (q1…q n ) (i.e., the initial three-dimensional coordinate set of adjacent assembly surface control points) is used for precise registration, and the ICP error function (i.e., ICP error function) is constructed: Using the singular value decomposition method, D n-1 (i.e. the initial three-dimensional coordinate set of adjacent assembly surface control points) and D n (i.e. the initial assembly surface control point three-dimensional coordinate set) match the point pair to calculate the rotation matrix R (i.e. the third rotation matrix) and the translation matrix T to obtain the new point cloud set (i.e. the assembly surface control point three-dimensional coordinate set to be processed) D' n =RD n +T, calculate the point set D′ n With D n-1 The average distance (i.e. the target average distance) is: when (i.e., the target average distance) is less than the set value and the 1mm iteration stops; the optimal rotation matrix R (i.e., the third rotation matrix) and translation vector T obtained through coarse alignment and fine alignment of the assembly control point set are used for the spatial transformation of the overall point cloud data of the N segments of the arch rib to realize the digital pre-assembly of adjacent steel arch rib segments.

[0173] In the above embodiment, a pre-assembly analysis is performed on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain the pre-assembly results of the steel tube arch rib, which improves engineering efficiency, avoids errors in traditional manual measurement, saves manpower and material costs, reduces the number of on-site pre-assemblies, shortens the construction period, and lays the foundation for subsequent overall linear calculation and digital modeling of the steel tube arch rib.

[0174] Optionally, as another embodiment of the present invention, the present invention relates to the technical field of steel tube concrete arch bridge construction; the method includes: accurately acquiring point cloud data of steel tube arch ribs through multi-station scanning with a ground-based three-dimensional laser scanner, and extracting point clouds of the arch rib segment assembly surface; performing noise reduction processing on the arch rib segment assembly surface (flange plane) using plane point distance constraints; projecting the flange spatial plane point cloud after noise reduction processing into the plane coordinate system of the best fitting plane, and rotating the projected point cloud data to be parallel to the XOY plane; extracting flange and bolt hole contour point sets using an adaptive alpha-shape algorithm; performing circle fitting on the flange and bolt hole contours using the least squares method to solve the coordinates of the center of the circle; obtaining bolt hole flange hole coordinates (XYZ), i.e., the three-dimensional coordinates of the assembly control points of the assembly surface, using an inverse rotation operation; aligning the points in the splicing surface control point set of adjacent arch rib segments, outputting a spatial transformation matrix, and applying the spatial transformation matrix parameters to the complete arch rib segment point cloud data, so that adjacent arch rib segments are digitally pre-spliced. The virtual pre-assembly method proposed in the present invention improves engineering efficiency compared to physical pre-assembly, saves manpower and material costs, and can lay the foundation for subsequent overall linear calculation and digital modeling of steel tube arch ribs.

[0175] Optionally, as another embodiment of the present invention, the present invention includes the following steps:

[0176] S1. Plan and arrange scanning sites, set up 3D laser scanners and rotating triangular pyramid targets according to the site locations, and use the 3D laser scanners to scan the steel tube arch rib segments to obtain on-site point cloud data.

[0177] S2. Build a complete point cloud model of the actual arch rib segment based on the on-site multi-site cloud data, and segment and extract the arch rib segment assembly surface;

[0178] S3. Use plane point distance constraint conditions to perform noise reduction on the arch rib segment assembly surface (flange plane);

[0179] S4, projecting the flange spatial plane point cloud after noise reduction processing into the plane coordinate system of the best fitting plane, and rotating the projected point cloud data to be parallel to the XOY plane;

[0180] S5, using adaptive alpha-shape algorithm to extract flange and bolt hole contour point sets;

[0181] S6. Use the least squares method to perform circle fitting on the flange and bolt hole contours to solve for the coordinates of the circle center; and use the obtained circle center coordinates to obtain the bolt hole and flange hole coordinates (XYZ) by using the inverse rotation operation to obtain the three-dimensional coordinates of the assembly control point of the assembly surface;

[0182] S7. Align the points in the control point set of the splicing surfaces of adjacent arch rib segments, output a spatial transformation matrix, and apply the parameters of the spatial transformation matrix to the complete arch rib segment point cloud data, so that the adjacent arch rib segments are digitally pre-spliced.

[0183] Optionally, as another embodiment of the present invention, the present invention includes:

[0184] The data acquisition and preprocessing module plans and arranges scanning sites, sets up a 3D laser scanner and a rotating triangular pyramid target according to the site location, and uses the 3D laser scanner to scan the steel tube arch rib segment to obtain on-site point cloud data; aligns the on-site multi-site cloud data to construct a complete actual arch rib segment point cloud model, and segments and extracts the arch rib segment assembly surface.

[0185] The assembly control point extraction module uses plane point distance constraints to perform noise reduction on the arch rib segment assembly surface (flange plane); projects the de-noised flange spatial plane point cloud onto the plane coordinate system of the best-fit plane, and rotates the projected point cloud data to be parallel to the XOY plane; uses an adaptive alpha-shape algorithm to extract the flange and bolt hole contour point sets; uses the least squares method to perform circle fitting on the flange and bolt hole contours to determine the center coordinates; and uses the inverse rotation operation to obtain the bolt hole and flange hole coordinates (XYZ), thereby obtaining the three-dimensional coordinates of the assembly control points on the assembly surface.

[0186] The adjacent arch rib pre-assembly module aligns the points within the control point set of the splicing surfaces of adjacent arch rib segments, outputs a spatial transformation matrix, and applies the spatial transformation matrix parameters to the point cloud data of the complete arch rib segment, so that the adjacent arch rib segments are digitally pre-spliced.

[0187] Optionally, as another embodiment of the present invention, the beneficial effects of the present invention are: obtaining on-site point cloud data through three-dimensional laser scanning technology, combining multi-site cloud registration to construct a complete arch rib model, avoiding traditional manual measurement errors, and achieving millimeter-level accuracy. Adopting plane projection to optimize the data processing process, combining the alpha-shape algorithm to accurately extract the outline of the bolt hole, solving the problem of extracting three-dimensional point feature points, and significantly improving the accuracy and reliability of control point positioning. Intelligent noise reduction is performed on the flange point cloud through plane point distance constraints, effectively eliminating environmental interference and redundant data. By extracting the three-dimensional coordinates of the assembly control points, "digital pre-assembly" of the arch rib segments is achieved, reducing the number of on-site pre-assembly times and shortening the construction period. At the same time, it avoids repeated adjustments of traditional welding positioning, reducing labor costs and material loss. The entire process from data acquisition to control point generation is digitized, supporting reverse verification and quality traceability, providing a scientific basis for arch rib linear deviation analysis and construction correction, and ensuring the overall structural safety of the bridge.

[0188] Figure 2 This is a module block diagram of a steel tube arch rib pre-assembly device provided in an embodiment of the present invention.

[0189] Alternatively, as another embodiment of the present invention, Figure 2 As shown, a steel tube arch rib pre-assembly device includes:

[0190] a scanning module for scanning the steel pipe arch rib segment to be assembled and the neighboring steel pipe arch rib segments adjacent to the steel pipe arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel pipe arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel pipe arch rib segments;

[0191] a control point analysis module for performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of control points of an initial assembly surface of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of control points of an initial adjacent assembly surface of the neighboring steel tube arch rib segment;

[0192] The pre-assembly result obtaining module is used to perform pre-assembly analysis on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain the pre-assembly result of the steel tube arch rib.

[0193] Alternatively, another embodiment of the present invention provides a steel tube arch rib pre-assembly system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-described steel tube arch rib pre-assembly method is implemented. The system may be a computer or other system.

[0194] Optionally, another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steel tube arch rib pre-assembly method as described above is implemented.

[0195] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0196] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0198] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention.

[0199] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0201] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for pre-assembling steel tube arch ribs, characterized in that: The steps include: Scanning the steel tube arch rib segment to be assembled and the neighboring steel tube arch rib segments adjacent to the steel tube arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segments; Performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of initial assembly surface control points of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel tube arch rib segment; A pre-assembly analysis is performed on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain a pre-assembly result of the steel tube arch rib.

2. The steel tube arch rib pre-assembly method according to claim 1, characterized in that: The process of performing control point analysis on the multiple original point cloud data of the steel tube arch rib segment to be assembled and the multiple original adjacent point cloud data of the neighboring steel tube arch rib segment to obtain the three-dimensional coordinate set of the initial assembly surface control points of the steel tube arch rib segment to be assembled and the three-dimensional coordinate set of the initial adjacent assembly surface control points of the neighboring steel tube arch rib segment includes: Performing registration analysis on all the original point cloud data to obtain a plurality of initial flange plane point cloud data; Preprocessing all the initial flange plane point cloud data to obtain a first rotation matrix, a first center of mass coordinate, and a plurality of target flange plane point cloud data; Extract control points from all target flange plane point cloud data according to the first rotation matrix and the first centroid coordinates to obtain a three-dimensional coordinate set of control points of the initial assembly surface of the steel pipe arch rib segment to be assembled; Performing registration analysis on all the original adjacent point cloud data to obtain a plurality of initial adjacent flange plane point cloud data; Preprocessing all the initial adjacent flange plane point cloud data to obtain a second rotation matrix, a second centroid coordinate, and a plurality of target adjacent flange plane point cloud data; Control points of all target adjacent flange plane point cloud data are extracted according to the second rotation matrix and the second centroid coordinates to obtain a three-dimensional coordinate set of initial adjacent assembly surface control points of the neighboring steel pipe arch rib segment.

3. The steel tube arch rib pre-assembly method according to claim 2, characterized in that: The process of performing registration analysis on all the original point cloud data to obtain a plurality of initial flange plane point cloud data includes: Scanning each of the original point cloud data A and any adjacent original point cloud data B respectively to obtain a plurality of first original target point cloud data corresponding to each of the original point cloud data A and a plurality of second original target point cloud data corresponding to each of the original point cloud data B; extracting a plurality of first original plane equations corresponding to each of the first original target point cloud data respectively from each of the first original target point cloud data; extracting a plurality of second original plane equations corresponding to each of the second original target point cloud data respectively from each of the second original target point cloud data; The first original plane equation and the second original plane equation are defined as: a0x1+b0y1+c0z1+d0=0, Wherein, a0 is the first X-axis normal vector component, b0 is the first Y-axis normal vector component, c0 is the first Z-axis normal vector component, and d0 is the first plane offset; Calculating parameters of first original plane equations corresponding to the first original target point cloud data using a random sampling consistency method and the first original target point cloud data to obtain first plane parameter sets corresponding to the first original plane equations; performing parameter updates on each of the first original plane equations according to each of the first plane parameter sets to obtain a plurality of first updated plane equations corresponding to each of the first original target point cloud data; Utilizing a random sampling consensus method and each of the second original target point cloud data, parameter calculations are performed on each of the second original plane equations corresponding to each of the second original target point cloud data to obtain a second plane parameter set corresponding to each of the second original plane equations; performing parameter updates on each of the second original plane equations according to each of the second plane parameter sets to obtain a plurality of second updated plane equations corresponding to each of the second original target point cloud data; Calculating intersection points of a plurality of first updated plane equations in each of the first original target point cloud data respectively, thereby obtaining first feature point coordinates corresponding to each of the first original target point cloud data; respectively calculating intersection points of a plurality of second updated plane equations in each of the second original target point cloud data, thereby obtaining coordinates of second feature points corresponding to each of the second original target point cloud data; Collecting the plurality of first feature point coordinates corresponding to each of the original point cloud data A to obtain a first initial feature point coordinate set corresponding to each of the original point cloud data A; respectively collecting a plurality of second feature point coordinates corresponding to each of the original point cloud data B to obtain a second initial feature point coordinate set corresponding to each of the original point cloud data B; Using a fast four-point consensus set algorithm, coarse registration is performed on each of the first initial feature point coordinate sets and the second initial feature point coordinate set corresponding to each of the original point cloud data B to obtain a plurality of coarsely registered feature point coordinate sets; Using the ICP algorithm to perform fine registration processing on each of the coarsely registered feature point coordinate sets, to obtain an initial rotation matrix corresponding to each of the coarsely registered feature point coordinate sets and a translation vector corresponding to each of the coarsely registered feature point coordinate sets; The Cloud Compare tool is used to segment and extract all the initial rotation matrices and all the translation vectors to obtain a plurality of initial flange plane point cloud data.

4. The steel tube arch rib pre-assembly method according to claim 2, characterized in that: The process of preprocessing all the initial flange plane point cloud data to obtain a first rotation matrix, a first centroid coordinate, and a plurality of target flange plane point cloud data includes: Performing noise reduction analysis on all the initial flange plane point cloud data to obtain a first centroid coordinate, a fourth updated plane equation, and a plurality of processed flange plane point cloud data; A rotation analysis is performed on all the processed flange plane point cloud data according to the first centroid coordinates and the fourth updated plane equation to obtain a plurality of target flange plane point cloud data.

5. The steel tube arch rib pre-assembly method according to claim 4, characterized in that: The process of performing noise reduction analysis on all the initial flange plane point cloud data to obtain the first centroid coordinates, the fourth updated plane equation, and the plurality of processed flange plane point cloud data includes: Define the equation of the third primitive plane as: a1x2+b1y2+c1z2+d1=0, Wherein, a1 is the second X-axis normal vector component, b1 is the second Y-axis normal vector component, c1 is the second Z-axis normal vector component, and d1 is the second plane offset; Construct a hybrid loss function, which is: Where L is the mixed loss value, a2 is the third X-axis normal vector component, b2 is the third Y-axis normal vector component, c2 is the third Z-axis normal vector component, d2 is the third plane offset, n is the number of initial flange plane point cloud data, and λ is the Lagrange multiplier; Solving the hybrid loss function to obtain a third plane parameter set; performing parameter updating on the third original plane equation according to the third plane parameter set to obtain a third updated plane equation; The first centroid coordinates are obtained by calculating all the initial flange plane point cloud data using the first formula, which is: in, in, is the first centroid coordinate, is the X-axis average value of the initial flange plane point cloud data, is the Y-axis average value of the initial flange plane point cloud data, is the Z-axis average value of the initial flange plane point cloud data, n is the number of initial flange plane point cloud data, x4 i is the X-axis coordinate of the i-th initial flange plane point cloud data, y4 i is the Y-axis coordinate of the i-th initial flange plane point cloud data, z4 i is the Z-axis coordinate of the i-th initial flange plane point cloud data; Performing eigenvalue decomposition on the first centroid coordinates and all the initial flange plane point cloud data using a covariance matrix eigenvalue decomposition algorithm to obtain a first eigenvalue, a first flange eigenvector corresponding to the first eigenvalue, a second eigenvalue, a second flange eigenvector corresponding to the second eigenvalue, a third eigenvalue, and a third flange eigenvector corresponding to the third eigenvalue; Screening out the minimum value of the first eigenvalue, the second eigenvalue, and the third eigenvalue, and after screening, using the first flange eigenvector, the second flange eigenvector, or the third flange eigenvector corresponding to the minimum value as the plane normal vector parameter group; Solving the plane normal vector parameter group and the first centroid coordinates using the third original plane equation to obtain a fourth plane offset; performing parameter updating on the third updated plane equation according to the plane normal vector parameter group and the fourth plane offset to obtain a fourth updated plane equation; respectively calculating the distance between each of the initial flange plane point cloud data and the fourth updated plane equation to obtain a target distance corresponding to each of the initial flange plane point cloud data; The average error value is obtained by calculating all the target distances using the second formula, which is: Among them, δ is the average error value, D i is the target distance corresponding to the i-th initial flange plane point cloud data, and n is the number of initial flange plane point cloud data; If the target distance satisfies the determination condition, the initial flange plane point cloud data corresponding to the target distance is used as the flange plane point cloud data to be processed, thereby obtaining a plurality of flange plane point cloud data to be processed. The determination condition is: D i >1.5s, Among them, D i is the target distance corresponding to the i-th initial flange plane point cloud data, and δ is the average error value; Determine whether the target distances all meet the judgment conditions, and whether the absolute value of the difference between the average error value of the current iteration number and the average error value of the previous iteration number is less than a first preset threshold value. If not, reconstruct the process of the hybrid loss function; if so, all the flange plane point cloud data to be processed are used as the processed flange plane point cloud data, thereby obtaining multiple processed flange plane point cloud data.

6. The steel tube arch rib pre-assembly method according to claim 4, characterized in that: The process of performing rotation analysis on all the processed flange plane point cloud data according to the first centroid coordinates and the fourth updated plane equation to obtain a plurality of target flange plane point cloud data includes: Projecting the fourth updated plane equation and each of the processed flange plane point cloud data is performed respectively through the third, fourth, and fifth equations to obtain projected flange plane point cloud data corresponding to each of the processed flange plane point cloud data. The third equation is: x1′ j ＝ x5 j ×(b3 2 +c3 2 )–a3×(y5 j ×b3+z5 j ×c3+d3) / norm(|a3,b3,c3|), The fourth formula is: y1′ j ＝ <h2 style=";text-align:left;direction:ltr">y5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×(a3<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +c3<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> )-b3×(x5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×a3+z5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×c3+d3) / norm(|a3,b3,c3|), The fifth formula is: z1′ j ＝ <h2 style=";text-align:left;direction:ltr">z5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×(a3<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +b3<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> )-c3×(x5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×a3+y5<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> ×b3+d3) / norm(|a3,b3,c3|), Among them, x1′ j is the X-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, y1′ j z1′ is the Y-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, j is the Z-axis coordinate of the projected flange plane point cloud data corresponding to the j-th processed flange plane point cloud data, x5 j is the X-axis coordinate of the j-th processed flange plane point cloud data, y5 j is the Y-axis coordinate of the j-th processed flange plane point cloud data, z5 j is the Z-axis coordinate of the j-th processed flange plane point cloud data, a3 is the fourth X-axis normal vector component, b3 is the fourth Y-axis normal vector component, c3 is the fourth Z-axis normal vector component, d3 is the fourth plane offset, and norm(||) is the Euclidean length function; Performing accuracy verification on all the projected flange plane point cloud data according to preset verification rules; if the verification fails, re-projecting the fourth updated plane equation and each of the processed flange plane point cloud data using the third formula, the fourth formula, and the fifth formula respectively; if the verification succeeds, using the projected flange plane point cloud data as the flange plane point cloud data to be rotated, thereby obtaining a plurality of flange plane point cloud data to be rotated; Extracting a first angle and a second angle from the fourth updated plane equation, the first angle being the angle between the normal vector of the fourth updated plane equation and the Z axis when projected in the YOZ coordinate system, and the second angle being the angle between the normal vector of the fourth updated plane equation and the Z axis when projected in the XOZ coordinate system; A first rotation matrix is ​​constructed by using the first angle and the second angle. The first rotation matrix is: Where R1 is the first rotation matrix, α is the first angle, and β is the second angle; Taking the first centroid coordinate as the rotation center, all the plane point cloud data of the flange to be rotated are rotated using the first rotation matrix to obtain a plurality of target flange plane point cloud data.

7. The steel tube arch rib pre-assembly method according to claim 2, characterized in that: The process of extracting control points from all target flange plane point cloud data according to the first rotation matrix and the first centroid coordinates to obtain a three-dimensional coordinate set of control points of the initial assembly surface of the steel pipe arch rib segment to be assembled includes: Extracting a plurality of flange contour points and a plurality of bolt hole contour points from all the target flange plane point cloud data using an adaptive Alpha-Shape algorithm, and collecting all the flange contour points and all the bolt hole contour points to obtain an initial contour point dataset; Segmenting the initial contour point data set to obtain a plurality of initial contour point data subsets; Using a least squares algorithm to fit each of the initial contour point data subsets, respectively, to obtain a circle equation corresponding to each of the initial contour point data subsets; Construct an error function equation group, solve the error function equation group and each circle equation respectively, and obtain the initial circle center coordinates corresponding to each initial contour point data subset. The error function equation group is: in, is the X-axis coordinate of the kth initial contour point in the ath initial contour point data subset, is the Y-axis coordinate of the k-th initial contour point in the a-th initial contour point data subset, is the X-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, E is the error function, r a is the radius corresponding to the ath initial contour point data subset, and m is the number of initial contour points; Performing homogeneous coordinate transformation on each of the initial circle center coordinates to obtain transformed circle center coordinates corresponding to each of the initial contour point data subsets; The first rotation matrix, each of the initial contour point data subsets, and the converted circle center coordinates corresponding to each of the initial contour point data subsets are respectively transformed into three-dimensional coordinates by the sixth formula to obtain the target circle center coordinates corresponding to each of the initial contour point data subsets, and all of the target circle center coordinates are combined to obtain the three-dimensional coordinate set of the initial assembly surface control points of the steel pipe arch rib segment to be assembled. The sixth formula is: in, is the X-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the Z-axis coordinate of the target circle center corresponding to the a-th initial contour point data subset, is the X-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, is the Y-axis coordinate of the initial circle center corresponding to the a-th initial contour point data subset, R1 is the first rotation matrix, is the X-axis average value of the initial flange plane point cloud data, is the Y-axis average value of the initial flange plane point cloud data, is the Z-axis average value of the initial flange plane point cloud data.

8. The steel tube arch rib pre-assembly method according to claim 1, characterized in that: The process of performing pre-assembly analysis on the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set to obtain the pre-assembly result of the steel tube arch rib includes: Using a fast four-point consensus set algorithm, the initial assembly surface control point three-dimensional coordinate set and the initial adjacent assembly surface control point three-dimensional coordinate set are respectively coarsely registered to obtain a registered assembly surface control point three-dimensional coordinate set corresponding to the initial assembly surface control point three-dimensional coordinate set and a registered adjacent assembly surface control point three-dimensional coordinate set corresponding to the initial adjacent assembly surface control point three-dimensional coordinate set; An ICP error function is constructed, and the singular value decomposition algorithm is used to calculate the ICP error function, the three-dimensional coordinate set of the control points of the assembly surface after registration, and the three-dimensional coordinate set of the control points of the adjacent assembly surfaces after registration to obtain a third rotation matrix and a translation matrix. The ICP error function is: Among them, R2 is the third rotation matrix, T1 is the translation matrix, b is the three-dimensional coordinates of the control points of the assembly surface after registration or the number of three-dimensional coordinates of the control points of the adjacent assembly surfaces after registration, q u is the three-dimensional coordinate of the u-th adjacent assembly surface control point in the three-dimensional coordinate set of the adjacent assembly surface control points after registration, p u is the three-dimensional coordinate of the u-th control point of the assembled surface after registration in the three-dimensional coordinate set of the control points of the assembled surface after registration; The three-dimensional coordinate set of the control points of the assembly surface after registration, the third rotation matrix and the translation matrix are calculated by the seventh formula to obtain the three-dimensional coordinate set of the control points of the assembly surface to be processed. The seventh formula is: D″=R2D′+T1, Where D″ is the three-dimensional coordinate set of the control points of the assembly surface to be processed, R2 is the third rotation matrix, T1 is the translation matrix, and D′ is the three-dimensional coordinate set of the control points of the assembly surface after registration; The target average distance is obtained by calculating the three-dimensional coordinate set of the control points of the assembly surface to be processed and the three-dimensional coordinate set of the control points of the adjacent assembly surfaces after registration through the eighth formula, and the eighth formula is: in, is the average distance to the target, b is the three-dimensional coordinates of the control points of the assembly surface after registration or the number of three-dimensional coordinates of the control points of the adjacent assembly surfaces after registration, q u is the three-dimensional coordinate of the u-th adjacent assembly surface control point in the three-dimensional coordinate set of the adjacent assembly surface control points after registration, p′ u The three-dimensional coordinates of the u-th control point on the assembly surface to be processed in the three-dimensional coordinate set of the control points on the assembly surface to be processed; Determine whether the target average distance is less than a second preset threshold. If not, re-use the fast four-point consistency set algorithm to roughly align the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points respectively; if so, use the third rotation matrix and the translation matrix as the pre-assembly results of the steel tube arch rib.

9. A steel tube arch rib pre-assembly device, characterized in that: include: a scanning module for scanning the steel pipe arch rib segment to be assembled and the neighboring steel pipe arch rib segments adjacent to the steel pipe arch rib segment to be assembled respectively by a three-dimensional laser scanner to obtain a plurality of original point cloud data of the steel pipe arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel pipe arch rib segments; a control point analysis module for performing control point analysis on a plurality of original point cloud data of the steel tube arch rib segment to be assembled and a plurality of original adjacent point cloud data of the neighboring steel tube arch rib segment, respectively, to obtain a three-dimensional coordinate set of control points of an initial assembly surface of the steel tube arch rib segment to be assembled and a three-dimensional coordinate set of control points of an initial adjacent assembly surface of the neighboring steel tube arch rib segment; The pre-assembly result obtaining module is used to perform pre-assembly analysis on the three-dimensional coordinate set of the initial assembly surface control points and the three-dimensional coordinate set of the initial adjacent assembly surface control points to obtain the pre-assembly result of the steel tube arch rib.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steel tube arch rib pre-assembly method according to any one of claims 1 to 8 is implemented.

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