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

Abstract

The present application relates to the field of small and medium span bridge construction and control technology, especially to a prefabricated beam size matching method and system based on a three-dimensional laser scanner, by acquiring the point cloud data of the prefabricated beam, and preprocessing the point cloud data of the prefabricated beam, the preprocessed point cloud data of the prefabricated beam is matched with the BIM model, and the automatic detection of the structure size is realized; then by acquiring the interface corner points of the prefabricated beam, the adjacent prefabricated beam section corner points are aligned for virtual splicing, the on-site assembly process is simulated, the key construction details are acquired, accurate and reliable data support is provided for subsequent construction, and it is ensured that each beam section can meet the design specification when splicing, so as to reduce the safety hidden danger, improve the controllability and scientificity of the construction process, improve the construction efficiency, shorten the construction period, and increase the service life and structural reliability of the bridge structure. The problems of low prefabricated beam parameter detection efficiency, high operation difficulty and low accuracy in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of small and medium span bridge construction and control, in particular to a prefabricated beam size matching method and system based on a three-dimensional laser scanner, especially a high-precision prefabricated beam size matching method, system, device and storage medium based on a three-dimensional laser scanner and an intelligent algorithm. BACKGROUND

[0002] Prestressed concrete prefabricated beam refers to a concrete structural member that prestress is applied to the concrete beam in the factory to improve its bearing capacity and crack resistance. The basic principle is that in the tensile zone of the concrete beam, the steel bar is tensioned by artificial force, and the concrete tensile zone is pre-compressed by the shrinkage force of the steel bar, so as to partially offset the positive bending moment in the use process of the beam and improve the mechanical properties of the beam. With the vigorous development of the bridge industry, prestressed concrete prefabricated beam has been widely used in highway bridge construction due to its advantages of convenient construction, strong bearing capacity, large span, good crack resistance and short construction period.

[0003] However, the internal force and linear of the prefabricated beam may change due to various factors such as manufacturing process, material performance and external environment during the manufacturing and storage of the prefabricated beam. In order to ensure the quality and safety of the prefabricated beam, its size, linear, elevation and other key parameters need to be detected and evaluated. Traditional detection methods mostly use total station, level and tape and other tools for manual detection. Although these methods can meet the detection requirements to some extent, manual detection requires a lot of time and effort, especially in large bridge projects, the number of prefabricated beams is large, and the detection task is heavy, which may lead to slow detection progress and affect the construction progress of the whole project. For some complex structures or special location prefabricated beams, manual detection may not achieve the ideal detection effect, and due to the subjectivity and uncertainty of manual detection, the detection result may have a large error. Therefore, the traditional detection method has the disadvantages of low efficiency, high operation difficulty and poor reliability, which leads to low construction efficiency and large prefabricated beam splicing error in the construction process. This will seriously affect the service life, structural bearing capacity and traffic safety of the bridge.

[0004] Therefore, how to efficiently and accurately measure the prefabricated beam structure size and realize high-precision prefabricated beam matching is very important. SUMMARY

[0005] In view of the low detection efficiency, high operation difficulty and low accuracy of prefabricated beam parameters in the prior art, the present application provides a prefabricated beam size matching method and system based on a three-dimensional laser scanner.

[0006] To achieve the above purpose, the technical scheme is adopted as follows:

[0007] The application provides a prefabricated beam size matching method based on a three-dimensional laser scanner, comprising:

[0008] Obtaining prefabricated beam point cloud data and preprocessing the prefabricated beam point cloud data;

[0009] Registering the preprocessed prefabricated beam point cloud data with a BIM model to obtain the deviation between the preprocessed prefabricated beam point cloud data and BIM model reference point cloud data;

[0010] If the deviation between the preprocessed prefabricated beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the prefabricated beam butt joint section is obtained;

[0011] According to the point cloud data of the butt joint section, the prefabricated beam butt joint section corner point is obtained;

[0012] Aligning the prefabricated beam butt joint section corner point, performing virtual assembly, and if the corner point alignment meets the accuracy requirement, the position information of the prefabricated elevation point and the alignment control point is obtained according to the state information after the virtual assembly; the prefabricated elevation point is a point embedded on the top surface of the prefabricated beam; the alignment control point is a point embedded on the side surface of the prefabricated beam; the prefabricated elevation point and the alignment control point are used for reference of relative position;

[0013] According to the position information of the prefabricated elevation point and the alignment control point, subsequent construction guidance is performed.

[0014] Further, the method for obtaining prefabricated beam point cloud data and preprocessing the prefabricated beam point cloud data is:

[0015] Obtaining prefabricated beam point cloud data; the prefabricated beam point cloud data is the outer surface point cloud data of the prefabricated beam;

[0016] Removing the noise points in the prefabricated beam point cloud data to complete the preprocessing of the prefabricated beam point cloud data.

[0017] Optionally, the method for removing the noise points in the prefabricated beam point cloud data is:

[0018] Suppose that each point in the initial point cloud data contains at least a certain number of neighborhood points in a specified radius neighborhood; suppose that the specified search radius is r, and the number of points in the point neighborhood is calculated :

[0019]

[0020] wherein, is a query point, is a neighborhood point, , =1, 2, 3, ···;

[0021] Then a neighborhood point number threshold is specified If , the point will be considered as a noise point and deleted, otherwise, it will be kept.

[0022] Further, the method for registering the pretreated prefabricated beam point cloud data with the BIM model to obtain the deviation between the pretreated prefabricated beam point cloud data and the BIM model reference point cloud data is:

[0023] Converting the BIM model into BIM model reference point cloud data;

[0024] Registering the pretreated prefabricated beam point cloud data with the BIM model reference point cloud data, and calculating the deviation between the pretreated prefabricated beam point cloud data and the BIM model reference point cloud data.

[0025] Further, the method for obtaining the prefabricated beam butt joint section corner point according to the point cloud data of the butt joint section is:

[0026] Reducing the dimension of the point cloud data of the butt joint section into two-dimensional point cloud data of the butt joint section;

[0027] Extracting the points of the outer boundary of the two-dimensional point cloud data of the butt joint section, and generating a two-dimensional curve of the points of the outer boundary;

[0028] Selecting two points on the two-dimensional curve farthest from each other as a head point and a tail point, respectively;

[0029] Connecting the head point and the tail point into a straight line;

[0030] Calculating the distance of all points on the two-dimensional curve to the straight line;

[0031] If the maximum distance of all points on the two-dimensional curve to the straight line is less than a threshold value, the straight line is kept;

[0032] If the maximum distance of all points on the two-dimensional curve to the straight line is greater than or equal to the threshold value, the point with the maximum distance to the straight line is taken as a new end point, and the head point and the tail point are connected to form two straight lines, the distance of all points on the two-dimensional curve to the straight line is recalculated, and iteration is performed until no new end point is generated, and a rough corner point is obtained;

[0033] Finding the point cloud data of the two-dimensional butt joint section near the rough corner point, determining the boundary points, and performing straight line fitting to obtain the prefabricated beam butt joint section corner point.

[0034] Further, the method for finding the point cloud data of the two-dimensional butt joint section near the rough corner point, determining the boundary points, and performing straight line fitting to obtain the prefabricated beam butt joint section corner point is:

[0035] KNN algorithm is used to find the point cloud data of the two-dimensional butt joint section near the rough corner point;

[0036] The point cloud data of the two-dimensional butt joint section near the rough corner point is divided into 8 regions at 45°, and when the number of points in at least 4 regions is 0, the point is determined as a boundary point;

[0037] The random sample consensus algorithm is used for straight line fitting of the boundary point cloud, and the intersection line of the two straight lines is the butt joint section corner point of the prefabricated beam.

[0038] Further, the method for aligning the butt joint section corner point of the prefabricated beam and performing virtual assembly is:

[0039] The rotation and translation matrix is used to align the butt joint section corner point of the prefabricated beam and perform virtual assembly;

[0040] The point cloud data of the butt joint section is reduced to two-dimensional butt joint section point cloud data;

[0041] The point cloud data of the two-dimensional butt joint section is fitted to obtain the deviation of the butt joint relative to the other butt joint;

[0042] According to the deviation of the butt joint relative to the other butt joint, it is judged whether the corner point alignment meets the accuracy requirement; if the corner point alignment accuracy error is greater than the set threshold value, the corner point position is adjusted until the corner point alignment accuracy error is less than or equal to the set threshold value; if the corner point alignment accuracy error is less than or equal to the set threshold value, the position information of the prefabricated elevation point and the alignment control point is obtained.

[0043] The present application provides a kind of prefabricated beam size matching method system based on three-dimensional laser scanner, comprising:

[0044] Data preprocessing module: for obtaining prefabricated beam point cloud data, and pre-processing prefabricated beam point cloud data;

[0045] Size deviation acquisition module: for registering the prefabricated beam point cloud data after pre-processing with BIM model, and obtaining the deviation between the prefabricated beam point cloud data after pre-processing and BIM model reference point cloud data;

[0046] Size deviation judgment and butt joint surface point cloud data acquisition module: for judging whether the size deviation meets the standard, if the deviation between the prefabricated beam point cloud data after pre-processing and BIM model reference point cloud data meets the standard, then the point cloud data of the butt joint section of the prefabricated beam is obtained;

[0047] Prefabricated beam butt joint section corner point acquisition module: for obtaining the butt joint section corner point of the prefabricated beam according to the point cloud data of the butt joint section;

[0048] The virtual assembly module is used for aligning the butt joint section corner points of the precast beam, performing virtual assembly, and if the alignment of the corner points meets the accuracy requirement, 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 on the top surface of the precast beam; the alignment control point is a point embedded on the side surface of the precast beam; and the precast elevation point and the alignment control point are used for reference of relative position.

[0049] The precast beam size matching adjustment module is used for performing subsequent construction guidance according to the position information of the precast elevation point and the alignment control point.

[0050] A terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

[0051] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0052] Compared with the prior art, the present application has the following beneficial effects:

[0053] The present application provides a precast beam size matching method based on a three-dimensional laser scanner, which obtains precast beam point cloud data and pre-processes the precast beam point cloud data; the pre-processed precast beam point cloud data is registered with a building information (Building Information Modeling, BIM) model to obtain the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data, so as to determine whether the precast beam size structure meets the standard; if the precast beam size structure meets the standard, the point cloud data of the butt joint section of the precast beam is obtained to obtain the butt joint section corner points of the precast beam, the butt joint section corner points of the precast beam are aligned, virtual assembly is performed, the accuracy of the alignment of the butt joint section corner points of the precast beam is determined, the position information of the precast elevation point and the alignment control point is determined, and the elevation data of the precast beam is obtained; the constructor can splice according to the elevation data and the position information of the alignment control point, the method greatly reduces the disadvantages caused by human participation, provides necessary reliable basis for subsequent construction, ensures that each beam segment in the subsequent splicing meets the design specification, reduces the security risks, improves the controllability and scientificity of the construction process, has high efficiency and simple operation, greatly shortens the construction period, and prolongs the service life and structural reliability of the bridge structure.

[0054] The application further provides a prefabricated beam size matching method system based on a three-dimensional laser scanner.

[0055] The application further provides a terminal device, 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.

[0056] A computer readable storage medium stores a computer program, wherein the computer program is executed by a processor to implement the steps of the above method. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 It is a flowchart of the prefabricated beam size matching method based on the three-dimensional laser scanner.

[0058] Figure 2 It is a schematic diagram of the positions of the prefabricated elevation point and the alignment control point in the embodiment of the application.

[0059] Figure 3 It is a layout structure diagram of the prefabricated beam point cloud data acquisition method in the embodiment of the application.

[0060] Figure 4 It is a structural schematic diagram of the prefabricated beam size matching system based on the three-dimensional laser scanner.

[0061] Wherein, 1-first laser instrument, 2-second laser instrument, 3-first positioning target, 4-second positioning target, 5-first lifting rod, 6-second lifting rod, 7-first trolley, 8-second trolley. DETAILED DESCRIPTION

[0062] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall within the protection scope of the present application.

[0063] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described accompanying drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0064] The present application will be further described in detail below in conjunction with specific embodiments, which are an explanation of the present application rather than a limitation.

[0065] The present application discloses a prefabricated beam size matching method based on a three-dimensional laser scanner, referring to Figure 1 , comprising:

[0066] S1: obtaining prefabricated beam point cloud data and preprocessing the prefabricated beam point cloud data, specifically:

[0067] First, the prefabricated beam point cloud data is obtained; the prefabricated beam point cloud data is the outer surface point cloud data of the prefabricated beam; the three-dimensional laser scanner can be set on both sides of the prefabricated beam, and the outer surface of the prefabricated beam is scanned by the three-dimensional laser scanner to obtain the prefabricated beam point cloud data;

[0068] Because of the influence of instrument interference, external environment, pouring support, etc., the collected point cloud data contains a large number of noise points, so after obtaining the prefabricated beam point cloud data, the noise points in the prefabricated beam point cloud data are removed, and the preprocessing of the prefabricated beam point cloud data is completed, and the method uses an outlier elimination algorithm to screen out noise points, and the specific operation is as follows:

[0069] Suppose each point in the initial point cloud data contains at least a certain number of neighborhood points in a specified radius neighborhood; suppose the specified search radius is r, and the number of points in the point neighborhood is calculated :

[0070]

[0071] wherein, is a query point, is a neighborhood point, , = 1, 2, 3, ···;

[0072] Then, a neighborhood point number threshold value is specified If , the point will be regarded as a noise point and deleted, otherwise, it will be retained, and the pre-preparation beam point cloud data preprocessing is completed.

[0073] S2: The pre-processed pre-preparation beam point cloud data is registered with the BIM model to obtain the deviation between the pre-processed pre-preparation beam point cloud data and the BIM model reference point cloud data, specifically:

[0074] First, the BIM model is converted into BIM model reference point cloud data, and the BIM model is a model pre-established according to the construction drawings. Before registration with the pre-processed pre-preparation beam point cloud data, it needs to be converted into point cloud data by RevitAPI software as BIM model reference point cloud data;

[0075] Then, the pre-processed pre-preparation beam point cloud data is registered with the BIM model reference point cloud data, and the deviation between the pre-processed pre-preparation beam point cloud data and the BIM model reference point cloud data is calculated, specifically: the BIM model reference point cloud data and the pre-processed pre-preparation beam point cloud data are registered by using the iterative closest point algorithm (ICP, Iterative Closest Point), and the best rotation matrix R and translation vector T between the two sets of point clouds are found out by continuous iteration. Registration, and using the BIM model reference point cloud data as the reference, the K- nearest neighbor algorithm (KNN, K-Nearest Neighbors) algorithm is used to calculate the deviation between the pre-processed pre-preparation beam point cloud data and the BIM model reference point cloud data. The deviation between the pre-preparation beam point cloud data and the BIM model reference point cloud data can be used to analyze the deviation between the actual structure size and the theoretical design value. The smaller the deviation, the closer the actual structure size and the design size, and the smaller the structure manufacturing error and deformation error. Therefore, in actual use, according to the actual standard requirements, the deviation threshold value between the pre-preparation beam point cloud data and the BIM model reference point cloud data can be set to judge whether the structure size meets the construction standard. If it meets the standard, S3 is executed, and if it does not meet the standard, the pre-preparation beam is replaced or the pre-preparation beam is re-poured.

[0076] S3: If the deviation between the pre-processed pre-preparation beam point cloud data and the BIM model reference point cloud data meets the standard, the point cloud data of the pre-preparation beam butt joint section is obtained, specifically:

[0077] When the point cloud data of the butt joint section of the prefabricated beam is acquired, the preprocessed point cloud data of the prefabricated beam is segmented, and the point cloud data of the butt joint section can be directly acquired.

[0078] S4: According to the point cloud data of the butt joint section, a corner point of the butt joint section of the prefabricated beam is acquired, specifically as follows:

[0079] The point cloud data of the butt joint section is reduced in dimension to two-dimensional point cloud data of the butt joint section;

[0080] Points on the outer boundary of the two-dimensional point cloud data of the butt joint section are extracted, and a two-dimensional curve of the points on the outer boundary is generated; here, a convex hull or an Alpha (Alpha Shape) shape method can be used to achieve this;

[0081] Two points farthest apart on the two-dimensional curve are selected as a head point and a tail point, respectively;

[0082] The head point and the tail point are connected to form a straight line;

[0083] The distances of all points on the two-dimensional curve to the straight line are calculated;

[0084] If the maximum distance of all points on the two-dimensional curve to the straight line is less than a threshold value, the straight line is retained;

[0085] If the maximum distance of all points on the two-dimensional curve to the straight line is greater than or equal to the threshold value, the point with the maximum distance to the straight line is taken as a new end point, and the head point and the tail point are connected to form two straight lines, the distances of all points on the two-dimensional curve to the straight lines are recalculated, and iteration is performed until no new end point is generated, and a rough corner point is obtained; for the case of a hollow section, the closed curves of the inner and outer boundaries need to be processed respectively, and the above method is performed on each curve, and the two points are selected as the head and tail for subdivision until the rough corner points of the inner and outer boundaries are extracted.

[0086] The two-dimensional point cloud data of the butt joint section near the rough corner point is found out, the boundary points are determined, and straight line fitting is performed to obtain the corner point of the butt joint section of the prefabricated beam; optionally, the KNN algorithm can be used to obtain the two-dimensional point cloud data near the rough corner point, the point set around the point is found out by using the KNN algorithm and is divided into 8 regions according to the angle, and when the number of points in at least 4 regions is 0, the point is determined as a boundary point; finally, the random sample consensus algorithm is used to perform straight line fitting on the boundary point cloud, and the intersection line of the two straight lines is the accurate corner point of the butt joint section of the prefabricated beam.

[0087] S5: Align the butt joint section corner points of the precast beams, perform virtual assembly, and if the alignment of the corner points meets the accuracy requirements, obtain the position information of the precast elevation points and the alignment control points according to the state information after virtual assembly; the precast elevation points are points embedded on the top surface of the precast beam; the alignment control points are points embedded on the side surface of the precast beam; the precast elevation points and the alignment control points are used for reference of relative position; specifically, after obtaining the accurate corner points, use the rotation translation matrix to butt joint the butt joint section corner points of the precast beams, and perform butt joint accuracy inspection by averaging the detection errors . The specific steps are as follows:

[0088] Use the rotation translation matrix :

[0089]

[0090] Butt joint the butt joint section corner points of the precast beams, and then fit the reduced point cloud data by using the random sample consensus algorithm to obtain the deviation of one butt joint surface relative to another butt joint surface, which provides more accurate data basis for field splicing, so as to perform splicing, wherein is a rotation matrix, is a 3D translation vector

[0091]

[0092] The deviation index of one butt joint surface relative to another butt joint surface can be evaluated by the average detection error between the butt joint section corners of the butt joint precast beams, and the specific formula is as follows:

[0093]

[0094] wherein, is the number of detected corner points, is the position of the i-th detected corner point, is the actual position of the point (the position in the real data or the design model), is the average detection error.

[0095] If the average detection error is less than the set threshold value, 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 value, the error point area is rescanned, and the scanning parameters such as scanning angle, neighborhood size, etc. are optimized to ensure that the error is reduced to the allowable range, and by using high-precision algorithm, the RANSAC fitting model is further optimized to fine-tune the position of the corner point, so as to ensure that the accuracy of the corner point detection meets the requirements.

[0096] ​​S6: According to the position information of the prefabricated elevation points and the alignment control points, subsequent construction guidance is carried out. After the joint section angle points of the prefabricated beams are aligned, the position information of the prefabricated alignment control points and the elevation control points is obtained, which can accurately determine the optimal splicing posture of the prefabricated beams. By analyzing the spatial relationship between the alignment control points and the elevation control points, the splicing error is reduced, and accurate technical basis is provided for subsequent construction. This precise alignment technology ensures that each beam segment can meet the design specifications during splicing, thereby reducing safety hazards and improving the controllability and scientificity of the construction process. In addition, the structure elevation data after splicing is obtained through the Z coordinate of the elevation control point, the difference between the actual elevation and the design elevation of each elevation control point is calculated, and systematic detection of the height of the joint is realized. The actual elevation of the elevation control point is obtained by field measurement, and 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 identified and adjusted in a timely manner to ensure the height consistency of the beam segments.

[0097] Taking a prefabricated beam as an example, referring to Figure 2 During the production of the prefabricated beam, 4 elevation control measurement points BG1, BG2, BG3 and BG4 are embedded on the top surface of the prefabricated beam using bolts, and 4 alignment control points DZ1, DZ2, DZ3 and DZ4 are embedded on the side surface of the prefabricated beam. These control points are embedded on the surface of the prefabricated beam and have no accuracy requirements for position, mainly serving as a reference for relative position.

[0098] Referring to Figure 3 , the point cloud data of the prefabricated beam is obtained by using a three-dimensional laser scanner. When laying, slide rails are arranged on both sides of the prefabricated beam. A first trolley 7 is slidably arranged on one side of the slide rail. A first lifting rod 5 is arranged on the first trolley 7. A first laser instrument 1 and a first positioning target 3 are arranged on the first lifting rod 5. A second trolley 8 is arranged on the other side of the slide rail. A second lifting rod 6 is arranged on the second trolley 8. A second laser instrument 2 and a second positioning target 4 are arranged on the second lifting rod 6. The first trolley 7 and the second trolley 8 each include a bottom plate, a power supply, a controller, four wheels and four stepping motors. The four wheels are rotatably mounted on both sides of the front and rear ends of the bottom plate. The four stepping motors are fixed to the bottom of the bottom plate and are arranged one by one corresponding to the four wheels. Each stepping motor drives one of the wheels to move. The power supply and the controller are installed at corresponding positions on the bottom plate. The power supply is connected to the four stepping motors through a power supply circuit, and the controller is connected to the four stepping motors through a control circuit. The first laser instrument 1 and the second laser instrument 2 are three-dimensional laser scanners. The first lifting rod 5 and the second lifting rod 6 each include three telescopic aluminum alloy columns, two lifting motors, a power supply and a controller.

[0099] When measuring, the first trolley 7 and the second trolley 8 are arranged at a distance from the two sides of the precast beam, and the first laser instrument 1 and the second laser instrument 2 are arranged in a distance range in which the best scanning data can be obtained when scanning the precast beam; the initial height of the first lifting rod 5 and the second lifting rod 6 is adjusted, and the working parameters of the first laser instrument 1 and the second laser instrument 2 are set, including the angle range of scanning, the frame rate, the pixel and the image quality, wherein the scanning angle range of the laser instrument is set to be able to collect the panoramic view of the bottom surface of the precast beam when the laser instrument moves with the trolley; the first laser instrument 1 and the second laser instrument 2 are respectively placed on the lifting rod 5 and the lifting rod 6, and the laser instruments are fixed; the first trolley 7 and the second trolley 8 are respectively placed on the track, the two laser instruments are turned on, the two trolleys are simultaneously turned on through the controller, and the moving speed and the distance of the two trolleys are set, and the speed and the distance are the same, the moving speed of the trolley is matched with the best moving scanning speed of the corresponding laser instrument, and the trolley is controlled to move along the track. The positioning target is located on the laser instrument, can send position information to the positioning base station in real time, and obtains the position coordinates of the point cloud data in cooperation with the point cloud data obtained by the laser instrument scanning. The laser instrument moves with the trolley, and the panoramic point cloud data of the bottom surface of the precast beam is obtained. The two trolleys are returned to the starting point through the controller; the height of the lifting rod is changed, and the working parameters of the laser instrument are set, including the angle range of scanning, the frame rate, the pixel and the image quality, wherein the scanning angle range of the laser instrument is set to be able to collect the panoramic view of the side surface and the top surface of the precast beam when the laser instrument moves with the trolley; the panoramic point cloud data of the side surface and the top surface of the precast beam is obtained. The point cloud data of the beam section and the control point coordinates are obtained by scanning, and the beam section diaphragm is extracted from the scanned point cloud data.

[0100] After obtaining the precast beam point cloud data by the above method, the noise points in the precast beam point cloud data are screened out by using an outlier elimination algorithm, it is assumed that each point in the initial point cloud data contains at least a certain number of neighborhood points in a specified radius neighborhood; it is assumed that the specified search radius is r, and the number of points in the neighborhood of the point is calculated

[0101]

[0102] wherein, is a query point, is a neighborhood point, , =1, 2, 3, ···;

[0103] Then, a neighborhood point number threshold value is specified If , the point will be regarded as a noise point and deleted, otherwise, it will be retained, and the preprocessing of the precast beam point cloud data is completed.

[0104] ​The BIM model is converted into BIM model reference point cloud data, the BIM model reference point cloud data and the preprocessed prefabricated beam point cloud data are registered by using an iterative closest point (ICP) algorithm, through continuous iteration, a best rotation matrix R and a translation vector T between the two sets of point cloud data are found, and the deviation between the preprocessed prefabricated beam point cloud data and the BIM model reference point cloud data is calculated by taking the BIM model reference point cloud data as a reference and using a K-nearest neighbor (KNN) algorithm. The deviation between the actual structure size and the theoretical design value can be analyzed through the deviation between the prefabricated beam point cloud data and the BIM model reference point cloud data. The smaller the deviation is, the closer the actual structure size and the design size are, and the smaller the structure manufacturing error and deformation error are. Whether the structure size meets the construction standard is judged. If it meets the construction standard, the point cloud data of the butt joint section of the prefabricated beam is obtained. If it does not meet the construction standard, the prefabricated beam is replaced or the prefabricated beam is recast.

[0105] When the point cloud data of the butt joint section of the prefabricated beam is obtained, the preprocessed prefabricated beam point cloud data is segmented, and the point cloud data of the butt joint section can be directly obtained.

[0106] The point cloud data of the butt joint section is reduced to two-dimensional point cloud data of the butt joint section.

[0107] The points on the outer boundary of the two-dimensional point cloud data of the butt joint section are extracted, and a two-dimensional curve of the points on the outer boundary is generated. Here, a convex hull or Alpha shape method can be used to achieve this.

[0108] Two points on the two-dimensional curve that are farthest apart are selected as the head and tail points, respectively.

[0109] The head and tail points are connected to form a straight line.

[0110] The distances of all points on the two-dimensional curve to the straight line are calculated.

[0111] If the maximum distance of all points on the two-dimensional curve to the straight line is less than 5mm, the straight line is retained.

[0112] If the maximum distance of all points on the two-dimensional curve to the straight line is greater than or equal to the threshold value, the point with the maximum distance to the straight line is selected as a new end point, and the head and tail points are connected to form two straight lines. The distances of all points on the two-dimensional curve to the straight line are recalculated, and the iteration is performed until no new end point is generated, and the rough corner point is obtained. For the case of a hollow section, the inner and outer boundary closed curves need to be processed respectively, and the above method is performed on each curve. Two points are selected as the head and tail to subdivide until the rough corner points of the inner and outer boundaries are extracted.

[0113] Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary points, and perform linear fitting to obtain the prefabricated beam butt joint section corner point. Optionally, the KNN algorithm can be used to obtain the two-dimensional point cloud data near the rough corner point. The KNN algorithm is used to find the point set around the point and divide it into 8 regions at an angle of 45°. When the number of points in at least 4 regions is 0, the point is identified as a boundary point. Finally, the random sample consensus algorithm is used to perform linear fitting on the boundary point cloud. The intersection of the two lines is the accurate prefabricated beam butt joint section corner point.

[0114] After obtaining the accurate prefabricated beam butt joint section corner point, a rotation and translation matrix is used to perform prefabricated beam butt joint section corner point butt joint, and the butt joint precision is tested by the average detection error The specific steps are as follows:

[0115] The rotation and translation matrix :

[0116]

[0117] The prefabricated beam butt joint section corner point butt joint is realized, and the reduced dimension point cloud data is fitted by the random sample consensus algorithm to obtain the deviation of the butt joint relative to the other butt joint, providing more accurate data basis for field splicing, and thus performing splicing, wherein is the rotation matrix, is the 3D translation vector

[0118]

[0119] The deviation index of the butt joint relative to the other butt joint can be evaluated by the average detection error between the prefabricated beam butt joint section corners, and the specific formula is as follows:

[0120]

[0121] wherein, is the number of detected corner points, is the position of the th detected corner point, is the actual position of the point (position in the real data or design model), is the average detection error.

[0122] If the average detection error is less than 1mm, it is considered that the corner point detection accuracy meets the requirements. When the detection error of the corner point is greater than 1mm, the error point area is rescaned, and the scanning parameters such as scanning angle and neighborhood size are optimized to ensure that the error is reduced to the allowable range, and the RANSAC fitting model is further optimized by high-precision algorithm to fine-tune the position of the corner point, ensuring that the accuracy of the corner point detection meets the requirements.​

[0123] After the butt joint cross-section corner points of the precast beams are aligned, the optimal splicing pose of the precast beams 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, and accurate technical basis is provided for subsequent construction. This precise alignment technology ensures that each beam segment can meet the design specifications during splicing, thereby reducing safety hazards and improving the controllability and scientificity of the construction process. In addition, the elevation data of the spliced structure is obtained through the Z coordinate of the elevation control points, the difference between the actual elevation and the design elevation of each elevation control point is calculated, and systematic detection of the height of the joint is realized. The actual elevation of the elevation control point is obtained by field measurement, and 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 identified and adjusted in a timely manner to ensure the height consistency of the beam segments.

[0124] Referring to Figure 4 , the application further provides a precast beam size matching method system based on a three-dimensional laser scanner, comprising:

[0125] A data preprocessing module is configured to obtain precast beam point cloud data and pre-process the precast beam point cloud data.

[0126] A size deviation obtaining module is configured to register the pre-processed precast beam point cloud data with a BIM model and obtain the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data.

[0127] A size deviation judgment and butt joint surface point cloud data obtaining module is configured to judge whether the size deviation meets the standard, and if the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data meets the standard, obtain the point cloud data of the precast beam butt joint cross-section.

[0128] A precast beam butt joint cross-section corner point obtaining module is configured to obtain the precast beam butt joint cross-section corner point according to the point cloud data of the butt joint cross-section.

[0129] A virtual assembly module is configured to align the precast beam butt joint cross-section corner points and perform virtual assembly, and if the corner point alignment meets the accuracy requirement, 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 on the top surface of the precast beam; the alignment control point is a point embedded on the side surface of the precast beam; the precast elevation point and the alignment control point are used for reference of relative position.

[0130] A precast beam size matching adjustment module is configured to guide subsequent construction according to the position information of the precast elevation point and the alignment control point.

[0131] The application provides a terminal device including a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor implements the steps in each of the method embodiments above when executing the computer program. Alternatively, the processor implements the functions of each module / unit in each of the device embodiments above when executing the computer program.

[0132] The computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the application.

[0133] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The terminal device can include, but is not limited to, a processor and a memory.

[0134] The processor can be a central processing unit (CPU), and can also be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like.

[0135] The memory can be used to store the computer program and / or modules. The processor implements various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory.

[0136] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can 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), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium can include or exclude contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0137] In summary, the prefabricated beam size matching method and system based on the three-dimensional laser scanner acquires the point cloud data of the prefabricated beam, compares the point cloud data of the prefabricated beam with the reference model, realizes the automatic detection of the structure size, then extracts the beam segment diaphragm from the scanned point cloud data by using the point cloud data of the prefabricated beam section and the control point coordinates, finally aligns the prefabricated beam butt joint section corner points of adjacent prefabricated beam segments, performs virtual pre-assembly, determines the assembly precision, calculates the coordinates of the preset elevation control measurement points and alignment control points by using such alignment, and predicts the bridge alignment. That is, by using the BIM reverse modeling technology, the prefabricated beam digital pre-splicing technology is used to simulate the field splicing process, a prefabricated beam high-precision splicing scheme based on control point butt joint is provided, and the precise matching of the prefabricated beam is realized. The method is simple in operation, high in efficiency, and more accurate in matching, can greatly shorten the construction period, and increase the service life and structural reliability of the bridge structure.

[0138] The above only describes the preferred embodiments of the present application and does not limit the technical solutions of the present application in any way. Those skilled in the art should understand that the technical solutions can be easily modified and replaced without departing from the spirit and principles of the present application, and these modifications and replacements also fall within the protection scope of the claims.

Claims

1. A method for matching the dimensions of precast beams based on a three-dimensional laser scanner, characterized in that, include: Acquire point cloud data of precast beams and preprocess the point cloud data of precast beams; The pre-processed precast beam point cloud data is registered with the BIM model to obtain the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data. If the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data meets the standard, then the point cloud data of the precast beam joint section is obtained. Based on the point cloud data of the butt joint section, the corner points of the precast beam butt joint section are obtained, specifically: The point cloud data of the docking section is reduced to two-dimensional point cloud data of the docking section; Extract the points of the outer boundary of the point cloud data of the two-dimensional docking section, and generate a two-dimensional curve of the points of the outer boundary; Select the two points on the two-dimensional curve that are farthest apart as the beginning and end points, respectively; Connect the first and last points with a straight line; Calculate the distance from all points on the two-dimensional curve to the aforementioned straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than a threshold, then 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, then the point with the maximum distance to the straight line is used as a new endpoint and connected to the first and last points respectively to form two straight lines. The distance from all points on the two-dimensional curve to the above straight lines is recalculated and iterated until no new endpoints are generated, thus obtaining a rough corner point. Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary points, and perform line fitting to obtain the corner point of the precast beam butt joint section; Align the corner points of the precast beam's 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 based on the virtual assembly status information. 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 a reference for relative position. Based on the location information of the precast elevation points and alignment control points, subsequent construction guidance will be provided.

2. The method for matching the dimensions of precast beams based on a three-dimensional laser scanner according to claim 1, characterized in that, The method for acquiring point cloud data of precast beams and preprocessing the point cloud data of precast beams is as follows: Acquire point cloud data of precast beams; the point cloud data of precast beams is the point cloud data of the outer surface of the precast beams. Remove noise from the precast beam point cloud data to complete the preprocessing of the precast beam point cloud data.

3. The method for matching the dimensions of precast beams based on a three-dimensional laser scanner according to claim 2, characterized in that, The method for removing noise from the point cloud data of precast beams is as follows: Assume that each point in the initial point cloud data contains at least a certain number of neighboring points within a specified radius; assume the specified search radius is r, and calculate the number of points within the neighboring area of ​​each point. : in, For query point, For neighboring points, , =1, 2, 3...; Then specify the threshold for the number of neighboring points. ,if Then point It will be considered a noise point and deleted; otherwise, it will be kept.

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

5. The method for matching the dimensions of precast beams based on a three-dimensional laser scanner according to claim 1, characterized in that, The method for finding the point cloud data of the two-dimensional butt joint section near the rough corner point, determining the boundary points, and performing line fitting to obtain the corner point of the precast beam butt joint section is as follows: The KNN algorithm is used to find the point cloud data of the two-dimensional docking section near the coarse corner point; The point cloud data of the two-dimensional docking section near the rough corner point is divided into 8 regions at 45°. When at least 4 regions have 0 points, the point is identified as a boundary point. The boundary point cloud is fitted with a straight line using a random sampling consensus algorithm, and the intersection of the two straight lines is the corner point of the precast beam's butt joint section.

6. The method for matching the dimensions of precast beams based on a three-dimensional laser scanner according to claim 1, characterized in that, The method for aligning the corner points of the precast beam's butt joint section and performing virtual assembly, and if the corner point alignment meets the accuracy requirements, then obtaining the position information of the precast elevation point and the alignment control point based on the virtual assembly status information is as follows: A rotation and translation matrix is ​​used to align the corner points of the precast beam's butt joint section for virtual assembly; The point cloud data of the docking section is reduced to two-dimensional point cloud data of the docking section; By fitting the point cloud data of the two-dimensional docking section, the deviation of one interface relative to another pair of interfaces is obtained; Based on the deviation of one pair of interfaces relative to another pair of interfaces, determine whether the corner alignment meets the accuracy requirements; If the corner alignment accuracy error is greater than the set threshold, the corner position will be fine-tuned until the corner alignment accuracy error is less than or equal to the set threshold. If the corner 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.

7. A precast beam size matching method system based on a three-dimensional laser scanner, characterized in that, include: Data preprocessing module: used to acquire point cloud data of precast beams and preprocess the point cloud data of precast beams; Dimensional Deviation Acquisition Module: Used to register the pre-processed precast beam point cloud data with the BIM model and acquire the deviation between the pre-processed precast beam point cloud data and the BIM model reference point cloud data; Dimensional deviation judgment and docking surface point cloud data acquisition module: used to determine whether the dimensional deviation meets the standard. If the deviation between the pre-processed 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 corner point acquisition module: Used to acquire the corner points of the precast beam butt joint section based on the point cloud data of the butt joint section, specifically: The point cloud data of the docking section is reduced to two-dimensional point cloud data of the docking section; Extract the points of the outer boundary of the point cloud data of the two-dimensional docking section, and generate a two-dimensional curve of the points of the outer boundary; Select the two points on the two-dimensional curve that are farthest apart as the beginning and end points, respectively; Connect the first and last points with a straight line; Calculate the distance from all points on the two-dimensional curve to the aforementioned straight line; If the maximum distance from all points on the two-dimensional curve to the straight line is less than a threshold, then 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, then the point with the maximum distance to the straight line is used as a new endpoint and connected to the first and last points respectively to form two straight lines. The distance from all points on the two-dimensional curve to the above straight lines is recalculated and iterated until no new endpoints are generated, thus obtaining a rough corner point. Find the point cloud data of the two-dimensional butt joint section near the rough corner point, determine the boundary points, and perform line fitting to obtain the corner point of the precast beam butt joint section; Virtual assembly module: Used to align the corner points of the precast beam's butt joint section for virtual assembly. If the corner point alignment meets the accuracy requirements, the position information of the precast elevation point and alignment control point is obtained based on the virtual assembly status information. The precast elevation point is a point pre-embedded on the top surface of the precast beam; the alignment control point is a point pre-embedded on the side surface of the precast beam; the precast elevation point and alignment control point are used for relative position reference. Precast beam size matching and adjustment module: used to provide subsequent construction guidance based on the position information of the precast elevation point and the alignment control point.

8. 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, it implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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