Precast beam size matching method and system based on three-dimensional laser scanner

Abstract

The invention relates to the technical field of medium and small span bridge construction and control, in particular to a precast beam size matching method and system based on a three-dimensional laser scanner, and the method comprises the steps: obtaining precast beam point cloud data, carrying out the preprocessing of the precast beam point cloud data, carrying out the registration of the preprocessed precast beam point cloud data with a BIM model, and obtaining the size of a precast beam. The automatic detection of the structure size is realized; then, by obtaining interface angular points of the precast beams, conducting virtual splicing by aligning the cross section angular points of the adjacent precast beams, simulating the field splicing process and obtaining key construction details, accurate and reliable data support is provided for follow-up construction, it is ensured that each beam section can conform to design specifications during splicing, and therefore potential safety hazards are reduced; the controllability and scientificity of the construction process are improved, the construction efficiency is improved, the construction period is shortened, the service life of the bridge structure is prolonged, and the structural reliability is improved. The problems of low precast beam parameter detection efficiency, high operation difficulty and low accuracy in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of construction and control technology of small and medium span bridges, and specifically to a precast beam size matching method and system based on a three-dimensional laser scanner, in particular to a high-precision precast beam size matching method, system, equipment and storage medium based on a three-dimensional laser scanner and an intelligent algorithm. Background Art

[0002] Prestressed concrete precast beams refer to concrete structural components that are prestressed in the factory to improve their bearing capacity and crack resistance. The basic principle is to use artificial force to tension the steel bars in the tensile zone of the concrete beam, and use the retraction force of the steel bars to pre-stress the tensile zone of the concrete, so that the positive bending moment can be partially offset during the use of the beam, thereby improving the mechanical properties of the beam. With the vigorous development of the bridge industry, prestressed concrete precast beams have been widely used in highway bridge construction due to their advantages such as convenient construction, strong bearing capacity, large span, good crack resistance and short construction period.

[0003] However, during the manufacturing and storage stages of precast beams, their structural internal forces and linear shapes may change due to various factors such as manufacturing process, material properties, and external environment. In order to ensure the quality and safety of precast beams, key parameters such as their size, linear shape, and elevation need to be tested and evaluated. Traditional detection methods mostly use tools such as total stations, levels, and tape measures for manual detection. Although these methods can meet the detection needs to a certain extent, manual detection requires a lot of time and effort, especially in large-scale bridge projects, where there are a large number of precast beams and the detection task is arduous, which may lead to slow detection progress and affect the construction progress of the entire project. For precast beams with some complex structures or special locations, manual detection may be difficult to achieve the ideal detection effect, and due to the subjectivity and uncertainty of manual detection, the detection results may have large errors. Therefore, traditional detection methods have the disadvantages of low efficiency, high difficulty in operation, and poor reliability, which leads to low construction efficiency and large splicing errors of precast beams during construction. This will seriously affect the service life of the bridge, structural bearing performance, and transportation safety.

[0004] Therefore, it is crucial to measure the structural dimensions of precast beams efficiently and accurately to achieve high-precision matching of precast beams. Summary of the invention

[0005] In view of the problems of low efficiency, great operating difficulty and low accuracy in the detection of precast beam parameters in the prior art, the present invention provides a precast beam size matching method and system based on a three-dimensional laser scanner.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a prefabricated beam size matching method based on a three-dimensional laser scanner, comprising: Acquire the precast beam point cloud data and preprocess the precast beam point cloud data; The preprocessed precast beam point cloud data is registered with the BIM model to obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data; If the deviation between the preprocessed point cloud data of the precast beam and the BIM model reference point cloud data meets the standard, the point cloud data of the butt joint section of the precast beam is obtained; According to the point cloud data of the butt joint section, the corner points of the butt joint section of the precast beam are obtained; Align the corner points of the butt joint sections of the precast beams and perform virtual assembly. If the corner point alignment meets the accuracy requirements, obtain the position information of the precast elevation point and the alignment control point according to the state information after the virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side surface of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; Subsequent construction guidance is provided based on the location information of the prefabrication elevation points and the alignment control points.

[0007] Furthermore, the method for obtaining the prefabricated beam point cloud data and preprocessing the prefabricated beam point cloud data is: Acquire precast beam point cloud data; the precast beam point cloud data is the outer surface point cloud data of the precast beam; The noise points in the precast beam point cloud data are removed to complete the preprocessing of the precast beam point cloud data.

[0008] Optionally, the method for removing noise points in the precast beam point cloud data is: Assume that each point in the initial point cloud data contains at least a certain number of neighboring points within the specified radius neighborhood; Assume that the specified search radius is r, and calculate the number of points in the point neighborhood :

[0009] in, is the query point, is the neighborhood point, , =1, 2, 3······; Then specify the neighborhood point number threshold ,if , then point will be considered as noise points and deleted, otherwise, they will be retained.

[0010] Furthermore, the method of registering the preprocessed precast beam point cloud data with the BIM model and obtaining the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data is: Convert BIM models into BIM model benchmark point cloud data; The preprocessed precast beam point cloud data is registered with the BIM model benchmark point cloud data, and the deviation between the preprocessed precast beam point cloud data and the BIM model benchmark point cloud data is calculated.

[0011] Furthermore, the method for obtaining the corner points of the butt joint section of the precast beam according to the point cloud data of the butt joint section is: Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Extracting points of the outer boundary of the point cloud data of the two-dimensional butt joint section, and generating a two-dimensional curve of the points of the outer boundary; Select the two points on the two-dimensional curve with the farthest distance as the first and last points respectively; Connect the first and last points into a straight line; Calculate the distances from all points on the two-dimensional curve to the above straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than the threshold, the straight line is retained; If the maximum distance between all points on the two-dimensional curve and the straight line is greater than or equal to the threshold, the point with the largest distance to the straight line is used as the new endpoint and connected to the first and last points to form two straight lines. The distances from all points on the two-dimensional curve to the above straight lines are recalculated and iterated until no new endpoints are generated, and a rough corner point is obtained. Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary point, and perform straight line fitting to obtain the corner point of the precast beam butt joint section.

[0012] Furthermore, the method of finding the point cloud data of the two-dimensional butt joint section near the rough corner point, determining the boundary point, and performing straight line fitting to obtain the corner point of the butt joint section of the precast beam is: The KNN algorithm is used to find the point cloud data of the two-dimensional butt joint section near the rough corner points; Divide the point cloud data of the two-dimensional butt joint section near the rough corner point into 8 areas at 45°, and identify the point as a boundary point when the number of points in at least 4 areas is 0; The random sampling consistency algorithm is used to perform straight line fitting on the boundary point cloud, and the intersection of the two straight lines is the corner point of the precast beam butt section.

[0013] Furthermore, the method of aligning the corner points of the butt joint sections of the prefabricated beams for virtual assembly is as follows: if the corner point alignment meets the accuracy requirements, the method of obtaining the position information of the prefabricated elevation point and the alignment control point according to the state information after the virtual assembly is as follows: The rotation and translation matrix is ​​used to align the corner points of the precast beam butt sections for virtual assembly; Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Fit the point cloud data of the two-dimensional butt joint section to obtain the deviation of the butt joint relative to the other pair of joints; Based on the deviation of the interface relative to the other pair of interfaces, determine whether the corner point alignment meets the accuracy requirements; if the corner point alignment accuracy error is greater than the set threshold, fine-tune the corner point position until the corner point alignment accuracy error is less than or equal to the set threshold; if the corner point alignment accuracy error is less than or equal to the set threshold, obtain the position information of the prefabricated elevation point and the alignment control point.

[0014] The present invention provides a prefabricated beam size matching method system based on a three-dimensional laser scanner, comprising: Data preprocessing module: used to obtain precast beam point cloud data and preprocess the precast beam point cloud data; Size deviation acquisition module: used to register the preprocessed precast beam point cloud data with the BIM model, and obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data; Size deviation judgment and docking surface point cloud data acquisition module: used to judge whether the size deviation meets the standard. If the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the precast beam docking section is acquired; Precast beam butt joint section corner point acquisition module: used to obtain the precast beam butt joint section corner points according to the point cloud data of the butt joint section; Virtual assembly module: used to align the corner points of the butt joint section of the precast beam and perform virtual assembly. If the corner point alignment meets the accuracy requirements, the position information of the precast elevation point and the alignment control point is obtained according to the state information after the virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; Precast beam size matching and adjustment module: used to provide subsequent construction guidance based on the position information of the precast elevation points and the alignment control points.

[0015] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0016] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a prefabricated beam size matching method based on a three-dimensional laser scanner. The method obtains point cloud data of the prefabricated beam and preprocesses the point cloud data of the prefabricated beam; The deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data is obtained to determine whether the size structure of the precast beam meets the standard. If the size structure of the precast beam meets the standard, the point cloud data of the precast beam butt section is obtained to obtain the corner points of the precast beam butt section. The corner points of the precast beam butt section are aligned to perform virtual assembly to determine whether the accuracy of the alignment of the corner points of the precast beam butt section meets the standard. Finally, the elevation data of the precast beam is obtained by determining the position information of the precast elevation point and the alignment control point. The constructor can perform splicing according to the elevation data and the position information of the alignment control point. This method greatly reduces the disadvantages caused by human participation, provides the necessary and reliable basis for subsequent construction, and ensures that each subsequent beam section meets the design specifications when spliced, thereby reducing safety hazards and improving the controllability and scientificity of the construction process. It is also efficient and easy to operate, which can greatly shorten the construction period and increase the service life and structural reliability of the bridge structure.

[0018] The present invention also provides a precast beam size matching method system based on a three-dimensional laser scanner. Through the settings of a data preprocessing module, a size deviation acquisition module, a size deviation judgment and docking surface point cloud data acquisition module, a precast beam docking section corner point acquisition module, a virtual assembly module and a precast beam size matching adjustment module, the acquisition and preprocessing of precast beam point cloud data, the registration of the preprocessed precast beam point cloud data with the BIM model, the acquisition of the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data, the judgment of whether the precast beam size structure meets the standard, the acquisition of the point cloud data of the precast beam docking section, the acquisition of the precast beam docking section corner points, the virtual assembly, the accuracy of the alignment of the precast beam docking section corner points and the subsequent construction guidance process by determining the position information of the precast elevation point and the alignment control point are realized. The system has a simple structure, is easy to operate, has less human participation in the whole process, has high matching efficiency, good accuracy and strong reliability, and can provide a reliable basis for subsequent construction.

[0019] The present invention also provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The device has a simple structure, low modification cost, and small resource occupation.

[0020] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor. The storage medium has good portability and strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a schematic flow chart of a method for matching the size of precast beams based on a three-dimensional laser scanner.

[0022] Figure 2 It is a schematic diagram of the positions of prefabricated elevation points and alignment control points in an embodiment of the present invention.

[0023] Figure 3 This is a layout diagram of a method for obtaining point cloud data of precast beams in an embodiment of the present invention.

[0024] Figure 4 It is a structural schematic diagram of a prefabricated beam size matching system based on a three-dimensional laser scanner according to the present invention.

[0025] Among them, 1-the first laser instrument, 2-the second laser instrument, 3-the first positioning target, 4-the second positioning target, 5-the first lifting rod, 6-the second lifting rod, 7-the first trolley, and 8-the second trolley. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0029] The present invention discloses a prefabricated beam size matching method based on a three-dimensional laser scanner. Figure 1 ,include: S1: Obtain the precast beam point cloud data and preprocess the precast beam point cloud data, specifically: First, obtain the point cloud data of the prefabricated beam; the point cloud data of the prefabricated beam is the point cloud data of the outer surface of the prefabricated beam; the point cloud data of the prefabricated beam can be obtained by setting a three-dimensional laser scanner on both sides of the prefabricated beam and scanning the outer surface of the prefabricated beam by the three-dimensional laser scanner; Due to the influence of instrument interference, external environment, casting support, etc., the collected point cloud data contains a large number of noise points. Therefore, after obtaining the precast beam point cloud data, the noise points in the precast beam point cloud data must be removed to complete the preprocessing of the precast beam point cloud data. This method uses the outlier elimination algorithm to screen out the noise points. The specific operations are as follows: Assume that each point in the initial point cloud data contains at least a certain number of neighboring points within the specified radius neighborhood; Assume that the specified search radius is r, and calculate the number of points in the point neighborhood :

[0030] in, is the query point, is the neighborhood point, , =1, 2, 3······; Then specify the neighborhood point number threshold ,if , then point The points will be regarded as noise points and deleted, otherwise, they will be retained to complete the preprocessing of the precast beam point cloud data.

[0031] S2: Align the preprocessed precast beam point cloud data with the BIM model to obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data, specifically: First, the BIM model is converted into BIM model reference point cloud data. The BIM model is a model pre-established according to the construction drawings. Before being aligned with the pre-processed precast beam point cloud data, it needs to be converted into point cloud data as BIM model reference point cloud data through Revit API software; Then, the preprocessed precast beam point cloud data is registered with the BIM model benchmark point cloud data, and the deviation between the preprocessed precast beam point cloud data and the BIM model benchmark point cloud data is calculated. Specifically, the BIM model benchmark point cloud data and the preprocessed precast beam point cloud data are registered using the iterative nearest neighbor algorithm (ICP, Iterative ClosestPoint), and the optimal rotation matrix R and translation vector T between the two groups of point clouds are found through continuous iterations. The BIM model benchmark point cloud data is used as a benchmark, and the K-nearest neighbor algorithm (KNN, K-Nearest Neighbors) algorithm is used for calculation to obtain the deviation between the preprocessed precast beam point cloud data and the BIM model benchmark point cloud data. The deviation between the actual structural size and the theoretical design value can be analyzed through the deviation between the precast beam point cloud data and the BIM model benchmark point cloud data. The smaller the deviation, the closer the actual structural size is to the design size, and the smaller the structural manufacturing error and deformation error. Therefore, in actual use, according to the actual standard requirements, the deviation threshold between the precast beam point cloud data and the BIM model benchmark point cloud data can be set to determine whether its structural dimensions meet the construction standards. If so, S3 is executed; if not, the precast beam is replaced or recast.

[0032] S3: If the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the precast beam joint section is obtained, specifically: When obtaining the point cloud data of the butt joint section of the precast beam, the preprocessed point cloud data of the precast beam is segmented, and the point cloud data of the butt joint section can be directly obtained.

[0033] S4: According to the point cloud data of the butt joint section, the corner points of the butt joint section of the precast beam are obtained, specifically: Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Extract the points of the outer boundary of the point cloud data of the two-dimensional butt joint section, and generate a two-dimensional curve of the points of the outer boundary; this can be achieved by using methods such as convex hull or Alpha (Alpha Shape); Select the two points on the two-dimensional curve with the farthest distance as the first and last points respectively; Connect the first and last points into a straight line; Calculate the distances from all points on the two-dimensional curve to the above straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than the threshold, the straight line is retained; If the maximum distance from all points on the two-dimensional curve to the straight line is greater than or equal to the threshold, the point with the largest distance to the straight line is used as the new endpoint and connected to the first and last points to form two straight lines. The distance from all points on the two-dimensional curve to the above straight line is recalculated and iterated until no new endpoints are generated to obtain rough corner points. For the case of hollow sections, the closed curves of the inner and outer boundaries need to be processed separately, and the above method is executed for each curve. By selecting two points as the first and last points for subdivision, the rough corner points of the inner and outer boundaries are extracted.

[0034] Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary point, and perform straight line fitting to obtain the corner point of the precast beam butt joint section. Optionally, the KNN algorithm can be used to obtain the two-dimensional point cloud data near the rough corner point, and the KNN algorithm is used to find the point set around the point and divide it into 8 areas according to the angle. When the number of points in at least 4 areas is 0, the point is identified as a boundary point. Finally, the random sampling consistency algorithm is used to perform straight line fitting on the boundary point cloud. The intersection of the two straight lines is the accurate corner point of the precast beam butt joint section.

[0035] S5: Align the corner points of the precast beam butt joint section and perform virtual assembly. If the corner point alignment meets the accuracy requirements, obtain the position information of the precast elevation point and the alignment control point according to the state information after virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; specifically: after obtaining the accurate corner points, use the rotation and translation matrix to butt joint the corner points of the precast beam butt joint section, and detect the average error through To carry out docking accuracy inspection. The specific steps are as follows: Using the rotation and translation matrix :

[0036] The corner points of the prefabricated beams are connected at the joint section, and then the point cloud data after dimensionality reduction is fitted through the random sampling consistency algorithm to obtain the deviation of the joint interface relative to the other joint interface, providing a more accurate data basis for on-site splicing, so as to carry out matching. for Rotation matrix, is the 3D translation vector

[0037] The deviation index of the butt joint relative to another butt joint can be evaluated by the average detection error between the butt joint section angles of the butt joint precast beams. The specific formula is as follows:

[0038] in, is the number of detected corner points, It is The positions of the detected corner points, is the actual position of the point (the position in the real data or the design model), is the average detection error.

[0039] If the average detection error is less than the set threshold, it is considered that the corner point detection accuracy meets the requirements. When the detection error of the corner point is greater than the set threshold, the error point area is rescanned to optimize the scanning parameters, such as scanning angle, neighborhood size, etc., to ensure that the error is reduced to the allowable range, and the RANSAC fitting model is further optimized through a high-precision algorithm to fine-tune the corner point position to ensure that the accuracy of corner point detection meets the requirements.

[0040] S6: Provide follow-up construction guidance based on the position information of the prefabricated elevation points and the alignment control points. After the corner points of the prefabricated beams are aligned, the optimal splicing posture of the prefabricated beams can be accurately determined by obtaining the position information of the prefabricated alignment control points and the elevation control points. By systematically analyzing the spatial relationship between the alignment control points and the elevation control points, the splicing error is reduced, providing an accurate technical basis for subsequent construction. This precise alignment technology ensures that each beam segment meets the design specifications when spliced, thereby reducing safety hazards and improving the controllability and scientificity of the construction process. In addition, the Z coordinates of the elevation control points are used to obtain the elevation data of the spliced ​​structure, and the difference between the actual elevation and the design elevation of each elevation control point is calculated to achieve a systematic detection of the height of the joint. The actual elevation of the elevation control point is obtained through on-site measurement, while the design elevation is set according to the design standard. By analyzing the difference between the actual and design elevations, the joint points that need to be improved are promptly identified and adjusted to ensure the height consistency of the beam segments.

[0041] Take a precast beam as an example, see Figure 2 When precast beams are manufactured, four elevation control measurement points BG1, BG2, BG3 and BG4 are embedded in the top surface of the precast beams with bolts, and four alignment control points DZ1, DZ2, DZ3 and DZ4 are embedded in the sides of the precast beams. These control points are embedded in the surface of the precast beams, and there is no precision requirement for their positions. They are mainly used as references for relative positions.

[0042] See also Figure 3, a 3D laser scanner is used to obtain the point cloud data of the prefabricated beam. When laying out, slide rails are set on both sides of the prefabricated beam. A first trolley 7 is slidably set on one side of the slide rail, and a first lifting rod 5 is set on the first trolley 7. The first lifting rod 5 is set on the first laser instrument 1 and the first positioning target 3; a second trolley 8 is set on the other side of the slide rail, and a second lifting rod 6 is set on the second trolley 8. The second lifting rod 6 is set on the second laser instrument 2 and the second positioning target 4; the first trolley 7 and the second trolley 8 both include a bottom plate, a power supply, a controller, four wheels and four stepper motors. The four wheels are respectively mounted on both sides of the front and rear ends of the bottom plate in pairs and can be rotated. The four stepper motors are fixed to the bottom of the bottom plate and are set one by one with the four wheels. Each stepper motor drives one of the wheels to move. The power supply and the controller are installed at corresponding positions on the base plate, and the power supply is connected to the four stepper motors through a power supply circuit, and the controller is connected to the four stepper motors through a control circuit; the first laser instrument 1 and the second laser instrument 2 are both three-dimensional laser scanners; the first lifting rod 5 and the second lifting rod 6 both include three-section telescopic aluminum alloy columns and two lifting motors, a power supply and a controller.

[0043] When measuring, set the distance between the first small car 7 and the second car 8 and both sides of the prefabricated beam, set the distance range in which the first laser 1 and the second laser 2 can obtain the best scanning data when scanning the prefabricated beam; adjust the initial height of the first lifting rod 5 and the second lifting rod 6, set the working parameters of the first laser 1 and the second laser 2, including the scanning angle range, frame rate, pixel and image quality, wherein the scanning angle range of the laser is set to be able to collect the panoramic view of the bottom surface of the prefabricated beam when the laser moves with the car; place the scanner 1 and the laser 2 on the lifting rod 5 and the lifting rod 6 respectively, and fix the laser; place the first small car 7 and the second car 8 on the track respectively, turn on the two lasers, turn on the two cars at the same time through the controller, and set the moving speed and distance of the two cars, and the speed and distance are the same, control the moving speed of the car to match the optimal moving scanning speed of the laser corresponding to the device, and control the car to move along the track. The positioning target is located on the laser, which can send position information to the positioning base station in real time, and obtain the position coordinates of the point cloud data with the point cloud data scanned by the laser. The laser instrument moves with the trolley to scan and obtain the panoramic point cloud data of the bottom surface of the prefabricated beam. The two trolleys are returned to the starting point by the controller; the height of the lifting rod is changed, and the working parameters of the laser instrument are set, including the scanning angle range, frame rate, pixel and image quality. The scanning angle range of the laser instrument is set to be able to collect the panoramic view of the side and top surface of the prefabricated beam when the laser instrument moves with the trolley; the panoramic point cloud data of the side and top surface of the prefabricated beam are obtained by scanning. The point cloud data and control point coordinates of the beam section are obtained by scanning, and the beam section diaphragm is extracted from the scanned point cloud data.

[0044] After obtaining the precast beam point cloud data by the above method, the outlier elimination algorithm is used to filter out the noise points of the precast beam point cloud data. It is assumed that each point in the initial point cloud data contains at least a certain number of neighboring points within the specified radius neighborhood; it is assumed that the specified search radius is r, and the number of points in the point neighborhood is calculated. :

[0045] in, is the query point, is the neighborhood point, , =1, 2, 3······; Then specify the neighborhood point number threshold ,if , then point The points will be regarded as noise points and deleted, otherwise, they will be retained to complete the preprocessing of the precast beam point cloud data.

[0046] The BIM model is converted into BIM model benchmark point cloud data. The BIM model benchmark point cloud data and the preprocessed precast beam point cloud data are aligned using the iterative nearest neighbor algorithm (ICP). Through continuous iteration, the optimal rotation matrix R and translation vector T between the two sets of point clouds are found for registration. The BIM model benchmark point cloud data is used as the benchmark, and the K-nearest neighbor algorithm (KNN) is used for calculation to obtain the deviation between the preprocessed precast beam point cloud data and the BIM model benchmark point cloud data. The deviation between the actual structural size and the theoretical design value can be analyzed by the deviation between the precast beam point cloud data and the BIM model benchmark point cloud data. The smaller the deviation, the closer the actual structural size is to the design size, and the smaller the structural manufacturing error and deformation error. Determine whether its structural size meets the construction standards. If it meets the standards, obtain the point cloud data of the precast beam joint section. If it does not meet the standards, replace the precast beam or recast the precast beam.

[0047] When obtaining the point cloud data of the butt joint section of the precast beam, the preprocessed point cloud data of the precast beam is segmented, and the point cloud data of the butt joint section can be directly obtained.

[0048] Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Extract the points of the outer boundary of the point cloud data of the two-dimensional butt joint section, and generate a two-dimensional curve of the points of the outer boundary; this can be achieved by using methods such as convex hull or Alpha shape; Select the two points on the two-dimensional curve with the farthest distance as the first and last points respectively; Connect the first and last points into a straight line; Calculate the distances from all points on the two-dimensional curve to the above straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than 5 mm, the straight line is retained; If the maximum distance from all points on the two-dimensional curve to the straight line is greater than or equal to the threshold, the point with the largest distance to the straight line is used as the new endpoint and connected to the first and last points to form two straight lines. The distance from all points on the two-dimensional curve to the above straight line is recalculated and iterated until no new endpoints are generated to obtain rough corner points. For the case of hollow sections, the closed curves of the inner and outer boundaries need to be processed separately, and the above method is executed for each curve. By selecting two points as the first and last points for subdivision, the rough corner points of the inner and outer boundaries are extracted.

[0049] Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary point, and perform straight line fitting to obtain the corner point of the precast beam butt joint section. Optionally, the KNN algorithm can be used to obtain the two-dimensional point cloud data near the rough corner point, and the KNN algorithm is used to find the point set around the point and divide it into 8 areas at an angle of 45°. When the number of points in at least 4 areas is 0, the point is identified as a boundary point. Finally, the random sampling consistency algorithm is used to perform straight line fitting on the boundary point cloud. The intersection of the two straight lines is the accurate corner point of the precast beam butt joint section.

[0050] After obtaining the accurate corner points of the precast beam butt joint section, the rotation and translation matrix is ​​used to butt the corner points of the precast beam butt joint section, and the average detection error is used to To carry out docking accuracy inspection. The specific steps are as follows: Using the rotation and translation matrix :

[0051] The corner points of the prefabricated beams are connected at the joint section, and then the point cloud data after dimensionality reduction is fitted through the random sampling consistency algorithm to obtain the deviation of the joint interface relative to the other joint interface, providing a more accurate data basis for on-site splicing, so as to carry out matching. for Rotation matrix, is the 3D translation vector

[0052] The deviation index of the butt joint relative to another butt joint can be evaluated by the average detection error between the butt joint section angles of the butt joint precast beams. The specific formula is as follows:

[0053] in, is the number of detected corner points, It is The positions of the detected corner points, is the actual position of the point (the position in the real data or the design model), is the average detection error.

[0054] If the average detection error is less than the set 1mm, the corner point detection accuracy is considered to meet the requirements. When the corner point detection error is greater than 1mm, the error point area is rescanned to optimize the scanning parameters, such as scanning angle, neighborhood size, etc., to ensure that the error is reduced to the allowable range, and the RANSAC fitting model is further optimized through a high-precision algorithm to fine-tune the corner point position to ensure that the corner point detection accuracy meets the requirements.

[0055] After the corner points of the precast beam butt joint sections are aligned, the optimal splicing posture of the precast beam can be accurately determined by obtaining the position information of the four elevation control measurement points BG1, BG2, BG3 and BG4 and the four alignment control points DZ1, DZ2, DZ3 and DZ4. By systematically analyzing the spatial relationship between the alignment control points and the elevation control points, the splicing error is reduced, providing an accurate technical basis for subsequent construction. This precise alignment technology ensures that each beam segment meets the design specifications when spliced, thereby reducing safety hazards and improving the controllability and scientificity of the construction process. In addition, the Z coordinates of the elevation control points are used to obtain the elevation data of the spliced ​​structure, and the difference between the actual elevation and the design elevation of each elevation control point is calculated to achieve a systematic detection of the height of the joint. The actual elevation of the elevation control point is obtained through on-site measurement, while the design elevation is set according to the design standard. By analyzing the difference between the actual and design elevations, the joint points that need to be improved are promptly identified and adjusted to ensure the height consistency of the beam segments.

[0056] See also Figure 4 The present invention also provides a prefabricated beam size matching method system based on a three-dimensional laser scanner, comprising: Data preprocessing module: used to obtain precast beam point cloud data and preprocess the precast beam point cloud data; Size deviation acquisition module: used to register the preprocessed precast beam point cloud data with the BIM model, and obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data; Size deviation judgment and docking surface point cloud data acquisition module: used to judge whether the size deviation meets the standard. If the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the precast beam docking section is acquired; Precast beam butt joint section corner point acquisition module: used to obtain the precast beam butt joint section corner points according to the point cloud data of the butt joint section; Virtual assembly module: used to align the corner points of the butt joint section of the precast beam and perform virtual assembly. If the corner point alignment meets the accuracy requirements, the position information of the precast elevation point and the alignment control point is obtained according to the state information after the virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; Precast beam size matching and adjustment module: used to provide subsequent construction guidance based on the position information of the precast elevation points and the alignment control points.

[0057] The present invention provides a terminal device including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0058] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0059] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0060] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0061] The memory may be used to store the computer program and / or module, and the processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0062] If the module / unit integrated in the terminal device 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0063] In summary, the precast beam size matching method and system based on the three-dimensional laser scanner obtains the precast beam point cloud data, compares the precast beam point cloud data with the benchmark model, and realizes the automatic detection of the structural size; then, the point cloud data of the precast beam section and the coordinates of the control points are used to extract the beam segment diaphragm from the scanned point cloud data, and finally, the precast beam butt joint section corners of the adjacent precast beam sections are aligned to perform virtual preassembly, determine the assembly accuracy, and use this alignment to calculate the coordinates of the preset elevation control measurement points and the alignment control points to predict the bridge alignment. That is, using BIM reverse modeling technology, the precast beam digital pre-splicing technology is used to simulate the on-site assembly process, and a high-precision assembly scheme for precast beams based on control point docking is provided to achieve accurate matching of precast beams. This method is simple to operate, highly efficient, and more accurate in matching. It can greatly shorten the construction period while increasing the service life and structural reliability of the bridge structure.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to impose any limitation on the technical solution of the present invention. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can also be subjected to several simple modifications and substitutions, and these modifications and substitutions are also within the scope of protection covered by the claims.

Claims

1. A prefabricated beam size matching method based on a three-dimensional laser scanner, characterized in that: include: Acquire precast beam point cloud data and preprocess the precast beam point cloud data; The preprocessed precast beam point cloud data is registered with the BIM model to obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data; If the deviation between the preprocessed point cloud data of the precast beam and the BIM model reference point cloud data meets the standard, the point cloud data of the butt joint section of the precast beam is obtained; According to the point cloud data of the butt joint section, the corner points of the butt joint section of the precast beam are obtained; Align the corner points of the butt joint sections of the precast beams and perform virtual assembly. If the corner point alignment meets the accuracy requirements, obtain the position information of the precast elevation point and the alignment control point according to the state information after the virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side surface of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; Subsequent construction guidance is provided based on the location information of the prefabrication elevation points and the alignment control points.

2. The precast beam size matching method based on a three-dimensional laser scanner according to claim 1, characterized in that: The method for obtaining the prefabricated beam point cloud data and preprocessing the prefabricated beam point cloud data is as follows: Acquire precast beam point cloud data; the precast beam point cloud data is the outer surface point cloud data of the precast beam; The noise points in the precast beam point cloud data are removed to complete the preprocessing of the precast beam point cloud data.

3. The precast beam size matching method based on a three-dimensional laser scanner according to claim 2 is characterized in that: The method for removing noise points in the precast beam point cloud data is: Assume that each point in the initial point cloud data contains at least a certain number of neighboring points within the specified radius neighborhood; Assume that the specified search radius is r, and calculate the number of points in the point neighborhood : in, is the query point, is the neighborhood point, , =1, 2, 3······; Then specify the threshold of the number of neighborhood points ,if , then point will be considered as noise points and deleted, otherwise, they will be retained.

4. The precast beam size matching method based on a three-dimensional laser scanner according to claim 1, characterized in that: The method of registering the preprocessed precast beam point cloud data with the BIM model and obtaining the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data is as follows: Convert BIM models into BIM model benchmark point cloud data; The preprocessed precast beam point cloud data is registered with the BIM model benchmark point cloud data, and the deviation between the preprocessed precast beam point cloud data and the BIM model benchmark point cloud data is calculated.

5. The precast beam size matching method based on a three-dimensional laser scanner according to claim 1, characterized in that: The method for obtaining the corner points of the butt joint section of the precast beam according to the point cloud data of the butt joint section is: Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Extracting points of the outer boundary of the point cloud data of the two-dimensional butt joint section, and generating a two-dimensional curve of the points of the outer boundary; Select the two points on the two-dimensional curve with the farthest distance as the first and last points respectively; Connect the first and last points into a straight line; Calculate the distances from all points on the two-dimensional curve to the above straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than the threshold, the straight line is retained; If the maximum distance between all points on the two-dimensional curve and the straight line is greater than or equal to the threshold, the point with the largest distance to the straight line is used as the new endpoint and connected to the first and last points to form two straight lines. The distances from all points on the two-dimensional curve to the above straight lines are recalculated and iterated until no new endpoints are generated, and a rough corner point is obtained. Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary point, and perform straight line fitting to obtain the corner point of the precast beam butt joint section.

6. The prefabricated beam size matching method based on a three-dimensional laser scanner according to claim 5, characterized in that: The method of finding the point cloud data of the two-dimensional butt joint section near the rough corner point, determining the boundary point, and performing straight line fitting to obtain the corner point of the butt joint section of the precast beam is as follows: The KNN algorithm is used to find the point cloud data of the two-dimensional butt joint section near the rough corner points; Divide the point cloud data of the two-dimensional butt joint section near the rough corner point into 8 areas at 45°, and identify the point as a boundary point when the number of points in at least 4 areas is 0; The random sampling consistency algorithm is used to perform straight line fitting on the boundary point cloud, and the intersection of the two straight lines is the corner point of the precast beam butt section.

7. The precast beam size matching method based on a three-dimensional laser scanner according to claim 1, characterized in that: The method of aligning the corner points of the butt joint sections of the prefabricated beams for virtual assembly is as follows: if the corner point alignment meets the accuracy requirements, the method of obtaining the position information of the prefabricated elevation point and the alignment control point according to the state information after the virtual assembly is as follows: The rotation and translation matrix is ​​used to align the corner points of the precast beam butt sections for virtual assembly; Reduce the dimension of the point cloud data of the butt joint section into the point cloud data of the two-dimensional butt joint section; Fit the point cloud data of the two-dimensional butt joint section to obtain the deviation of the butt joint relative to the other pair of joints; According to the deviation of the interface relative to the other interface, determine whether the corner point alignment meets the accuracy requirements; If the corner point alignment accuracy error is greater than the set threshold, the corner point position is fine-tuned until the corner point alignment accuracy error is less than or equal to the set threshold; If the corner point alignment accuracy error is less than or equal to the set threshold, the position information of the prefabricated elevation point and the alignment control point is obtained.

8. A prefabricated beam size matching method system based on a three-dimensional laser scanner, characterized in that: include: Data preprocessing module: used to obtain precast beam point cloud data and preprocess the precast beam point cloud data; Size deviation acquisition module: used to align the preprocessed precast beam point cloud data with the BIM model, and obtain the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data; Size deviation judgment and docking surface point cloud data acquisition module: used to judge whether the size deviation meets the standard. If the deviation between the preprocessed precast beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the precast beam docking section is acquired; Precast beam butt joint section corner point acquisition module: used to obtain the precast beam butt joint section corner points according to the point cloud data of the butt joint section; Virtual assembly module: used to align the corner points of the butt joint section of the precast beam and perform virtual assembly. If the corner point alignment meets the accuracy requirements, the position information of the precast elevation point and the alignment control point is obtained according to the state information after the virtual assembly; the precast elevation point is a point embedded in the top surface of the precast beam; the alignment control point is a point embedded in the side of the precast beam; the precast elevation point and the alignment control point are used as references for relative positions; Precast beam size matching and adjustment module: used to provide subsequent construction guidance based on the position information of the precast elevation points and the alignment control points.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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