Method and device for measuring line shape of main cable of suspension bridge and electronic equipment

Abstract

The invention provides a method and device for measuring the line shape of a main cable of a suspension bridge and electronic equipment. Point cloud data of the suspension bridge are obtained through three-dimensional scanning; the coordinate system of the suspension bridge point cloud is converted into a coordinate system with the longitudinal direction of the bridge floor as the main direction through K-L conversion; automatically extracting point cloud data of the main cable by using a main cable automatic extraction algorithm and taking a density threshold value as a standard; determining the circle center and the radius of each section of the main cable based on a repetitive least clipping square method RLTS; and determining the line shape of the main cable of the suspension bridge based on the circle center coordinate and the radius of each section of the main cable. In order to solve the problem of high defect rate of point cloud of the main cable generated after three-dimensional laser scanning of the suspension bridge, the point cloud data of the main cable is obtained by using the 3D laser scanning technology, the linear equation and key parameters of the main cable are obtained, and the proposed method has relatively high robustness and measurement precision and meets measurement requirements in engineering practice.

Description

Technical Field

[0001] The invention relates to the field of structural deformation detection, and in particular to a method, a device and electronic equipment for measuring the line shape of a main cable of a suspension bridge. Background Art

[0002] Traditional structural deformation detection methods mainly include: precision leveling method (such as: level, theodolite and total station, etc.) or contact measurement method (such as embedded displacement sensor method, connecting pipe measurement method and inclinometer measurement method, etc.). 3D scanning technology transforms the single-point acquisition method of the traditional method into a new method of automatically and continuously acquiring a large amount of point cloud data. It has the advantages of fast, high precision and comprehensiveness, and provides a new idea for the measurement of structures. Therefore, 3D scanning technology establishes a high-precision 3D point cloud model by acquiring the actual spatial position information data of the target structure such as points, lines, surfaces, bodies, and spaces. At present, 3D scanning technology plays an increasingly important role in global change, smart cities, resource surveys, environmental monitoring, basic surveying and mapping, etc. 3D laser scanning technology plays an important role in the field of bridge detection.

[0003] A suspension bridge is a type of bridge structure that relies on the main cable to bear the main load of the bridge. The main cable plays a vital role in the entire suspension bridge. As the core load-bearing component of the suspension bridge, the stability and safety of the main cable are directly related to the safety and service life of the entire bridge structure. Therefore, the linear monitoring of the main cable of the suspension bridge is particularly important.

[0004] At present, the reconstruction of the plane line shape of the main cable of the suspension bridge has the following defects: the spatial state of the main cable cannot be specifically displayed; in the actual scanning and registration process, the point cloud of the main cable part often has problems such as incompleteness and high defect rate such as many clustered drift points. Summary of the invention

[0005] In response to the problems in the prior art, an embodiment of the present invention provides a method, device and electronic equipment for measuring the linear shape of the main cable of a suspension bridge using three-dimensional laser scanning, so as to solve the problems in the prior art that the point cloud of the main cable of a suspension bridge obtained by ground three-dimensional laser scanning has many outlier clustering points and high incompleteness.

[0006] Illustratively, the embodiments of the present invention provide the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for measuring the line shape of a main cable of a suspension bridge, comprising:

[0008] Obtain point cloud data of the suspension bridge through 3D scanning;

[0009] Converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction;

[0010] Determining the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion;

[0011] Slice and reduce the dimension of the point cloud data of each section of the main cable respectively, and determine the center and radius of each piece of point cloud data after slicing and dimensionality reduction;

[0012] The main cable line shape of the suspension bridge is obtained according to the center and radius of each piece of point cloud data of each section of the main cable.

[0013] In a possible implementation, converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction includes:

[0014] Using KL transformation, the coordinate system of the point cloud data is converted into a coordinate system whose X-axis is consistent with the longitudinal direction of the bridge deck;

[0015] Among them, the Y-axis is the direction perpendicular to the longitudinal direction of the bridge deck and within the plane of the bridge deck, and the Z-axis is the direction perpendicular to the bridge deck and pointing upward.

[0016] In a possible implementation, determining the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion includes:

[0017] The point cloud data after the coordinate system conversion is projected, and the point cloud data of each section of the main cable of the suspension bridge is determined based on the projected data and a preset density threshold.

[0018] In a possible implementation, projecting the point cloud data after the coordinate system conversion, and determining the point cloud data of each section of the main cable of the suspension bridge based on the projected data and a preset density threshold, includes:

[0019] Project the point cloud data after coordinate system conversion onto the YOZ plane to obtain the point cloud density curve;

[0020] According to the point cloud density curve and the preset density threshold, the point cloud data of the main beam of the suspension bridge is stripped;

[0021] Density statistics are performed on the XOY plane for the remaining point cloud data except the point cloud data of the main beam in the point cloud data after the coordinate system conversion, and the point cloud data of a single main cable of the suspension bridge is separated;

[0022] The point cloud data of a single main cable is projected onto the YOZ plane to determine the point cloud data of each section of the main cable of the suspension bridge.

[0023] In a possible implementation, slicing and reducing the dimension of the point cloud data of each section of the main cable respectively includes:

[0024] For each section of the main cable, the point cloud data of the main cable is sliced ​​at equal intervals; wherein the slicing distance is not less than the distance of the main cable diameter;

[0025] For each slice of each main cable segment, determine the normal vector of each point in the point cloud data of the slice;

[0026] Using a clustering algorithm, the normal vectors of each point in the point cloud data of each slice are clustered to obtain the point cloud axis vector of each slice;

[0027] Based on the point cloud axis vector of each slice of each main cable segment, the 3D point cloud is converted to a 2D plane.

[0028] In a possible implementation, determining the center and radius of each piece of point cloud data after slicing and dimensionality reduction includes:

[0029] Based on the repetitive least squares method, the center and radius of each piece of point cloud data after slicing and dimensionality reduction are determined.

[0030] In a possible implementation, the determining of the center and radius of each piece of point cloud data after slicing and dimensionality reduction based on the repetitive least trimmed squares method includes:

[0031] For each piece of point cloud data after slicing and dimensionality reduction, randomly select a first preset number of point cloud data, and determine an initial circle center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle;

[0032] Based on the initial circle center and radius, calculate the residual from each point of the point cloud data to the preliminary fitting circle;

[0033] Selecting a second preset number of point cloud data corresponding to the smallest residual from the residuals; wherein the second preset number is less than or equal to the first preset number;

[0034] The second preset number of point cloud data is used as the new first preset number of point cloud data, and the steps of determining the initial center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle are re-executed until the stopping condition is reached to obtain the center and radius of the final fitting circle.

[0035] In a possible implementation, obtaining the main cable line shape of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable includes:

[0036] According to the center and radius of each piece of point cloud data of each section of the main cable, the coordinates of the center point of each section of the main cable are obtained;

[0037] Perform quadratic fitting on the coordinates of the center point of each section of the main cable to obtain the center line of each section of the main cable;

[0038] Projecting the center lines of two adjacent main cables in the XOZ plane respectively, and determining the intersection coordinates of the main cables of the suspension bridge according to the projection results;

[0039] Based on the coordinates of the intersection of the center line of each section of the main cable and the main cable of the suspension bridge, the main cable line shape of the suspension bridge is obtained.

[0040] In a second aspect, an embodiment of the present invention provides a device for measuring the linear shape of a main cable of a suspension bridge, comprising:

[0041] Bridge scanning module, used to obtain point cloud data of suspension bridges through 3D scanning;

[0042] A coordinate system conversion module, used to convert the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction;

[0043] A main cable determination module, used to determine the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion;

[0044] A processing and fitting module is used to slice and reduce the dimension of the point cloud data of each section of the main cable, and determine the center and radius of each piece of point cloud data after slicing and dimensionality reduction;

[0045] The linear acquisition module is used to obtain the main cable linear shape of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable.

[0046] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and wherein the processor implements any of the above-mentioned methods for measuring the main cable shape of a suspension bridge when executing the computer program.

[0047] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for measuring the main cable shape of a suspension bridge as described above is implemented.

[0048] The method, device and electronic device for measuring the linear shape of the main cable of a suspension bridge provided in the embodiment of the present invention obtain point cloud data of the suspension bridge through three-dimensional scanning, and convert the coordinate system of the point cloud of the suspension bridge into a coordinate system with the longitudinal direction of the bridge deck as the main direction, and then determine the point cloud data of each section of the main cable of the suspension bridge, slice and reduce the dimension of the point cloud data of each section of the main cable, and determine the center and radius of each piece of point cloud data after slicing and dimension reduction; and determine the linear shape of the main cable of the suspension bridge based on the center coordinates and radius of each section of the main cable. Among them, the embodiment of the present application aims at the problem of high defect rate of the main cable point cloud generated after three-dimensional scanning of the suspension bridge, and obtains the point cloud data of each section of the main cable of the suspension bridge by using 3D scanning technology, and obtains the linear shape of the main cable, which has high robustness and measurement accuracy, meets the measurement requirements in engineering practice, and provides a technical reference for the use of 3D laser scanning to obtain the deformation and linear shape of the main cable of the suspension bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A flowchart of an exemplary method for measuring the main cable shape of a suspension bridge provided by an embodiment of the present invention;

[0051] Figure 2 A flowchart of an exemplary method for measuring the main cable shape of a suspension bridge provided by another embodiment of the present invention;

[0052] Figure 3 A schematic diagram of an exemplary main cable centerline forming process provided by an embodiment of the present invention;

[0053] Figure 4 A block diagram of a device for measuring the main cable shape of a suspension bridge provided by an embodiment of the present invention; and

[0054] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 are within the scope of protection of the present invention.

[0056] In the related technologies, Xu Jinjun et al. first introduced the center of gravity calculation method and point cloud fitting method into the application of 3D scanning technology in bridge deflection measurement, and obtained the deflection change results of the bridge under various load conditions. At the same time, the deflection of the bridge was measured by traditional deflection measurement methods. Finally, the two results were compared and analyzed, and the feasibility of ground laser scanners in bridge deflection detection was obtained. Then, based on the principle of pulse ranging, A. Nurunnabi et al. used 3D laser scanners to obtain point cloud data of models under different working conditions, processed the point cloud data from qualitative and quantitative aspects, and obtained the deformation information of key components of arch bridges. A bridge deformation detection method based on target point cloud is proposed, which provides a new idea for measuring the deformation of arch bridges. Wang Bei et al. conducted long-term monitoring of a Yangtze River Bridge in Wuhan City, processed the scanning data of each period, and compared and analyzed the deformation monitoring of the bridge arch and the vertical monitoring of the bridge piers. By analyzing the state parameters such as deflection and 3D offset, the coordinates of the bridge arch section points were extracted and the best vertical cylinder fitting was performed on the bridge pier slice point cloud, enriching the expression of bridge monitoring data.

[0057] For the deformation detection of suspension bridges, Liang Dong et al. conducted an overall configuration detection on a pedestrian suspension bridge in operation. The results showed that 3D laser scanning technology can accurately obtain the overall spatial configuration at a certain moment and can be used to evaluate the health status of the bridge. H. Zhang et al. used the ICP (iterative closest point) algorithm based on feature points, which required fewer registration points and provided more accurate values ​​in the identification of spatial deformation of bridge towers.

[0058] How to effectively handle these incomplete and abnormal data and improve the accuracy and reliability of the linear fitting of the main cable of the suspension bridge based on three-dimensional laser scanning has become a key issue that needs to be urgently solved in the current research field. In response to the above problems, the embodiment of the present application proposes a robust point cloud linear reconstruction method for the main cable of the suspension bridge, which obtains the point cloud data of the suspension bridge through three-dimensional scanning, and converts the coordinate system of the point cloud data to a coordinate system with the longitudinal direction of the bridge deck as the main direction, and then determines the point cloud data of each section of the main cable of the suspension bridge. The point cloud data of each section of the main cable is sliced ​​and reduced in dimension, and the center and radius of each piece of point cloud data after slicing and dimensionality reduction are determined, so that the main cable linear shape of the suspension bridge is obtained according to the center and radius of each piece of point cloud data of each section of the main cable.

[0059] First, refer to Figure 1 , which shows a method for measuring the main cable shape of a suspension bridge provided by an exemplary embodiment of the present invention, comprising:

[0060] Step 100), obtaining point cloud data of the suspension bridge through three-dimensional scanning.

[0061] In step 100), in one example, obtaining point cloud data of a suspension bridge through three-dimensional scanning includes: station arrangement, point cloud data denoising, and point cloud data compression.

[0062] Station arrangement: In this embodiment, a scanner may be arranged at a preset distance from the suspension bridge in the middle of the bridge span, so that the scanner can perform a comprehensive scan of the bridge. The preset distance may be determined based on the position of the scanner that can perform a comprehensive scan of the bridge.

[0063] Point cloud data denoising: In this implementation, bilateral filtering can be used for denoising.

[0064] Point cloud data compression: This implementation can use a spatial distance compression algorithm. First, the sampling distance threshold is determined. If the spatial distance between a data point and the next data point in its sorting direction is greater than the threshold, the point is retained, otherwise it is deleted.

[0065] In order to determine the sampling distance threshold in the point cloud data, this embodiment can set a reasonable threshold based on the geometric characteristics of the main cable of the suspension bridge and the density of the point cloud. The distance threshold should remove redundant points and retain the characteristics of the main cable line shape as much as possible while ensuring the fitting accuracy. An effective distance threshold formula can consider the following factors: (1) the spatial distribution density of the point cloud (i.e., the distance between adjacent points); (2) the geometric shape characteristics of the main cable (such as curvature, length, etc.); (3) the accuracy and sampling interval of the laser scanner.

[0066] Step 200), converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction.

[0067] In step 200), in one example, the coordinate system of the point cloud data is converted into a coordinate system with the longitudinal direction of the bridge deck as the main direction, including: using KL transformation to convert the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction.

[0068] In one example, the KL transformation is used to transform the coordinate system of the point cloud data into a coordinate system in which the X-axis is consistent with the longitudinal direction of the bridge deck; wherein the Y-axis is perpendicular to the longitudinal direction and in the plane of the bridge deck, and the Z-axis is perpendicular to the upward direction of the bridge deck.

[0069] In this paper, KL transformation is used to transform the point cloud of the suspension bridge into a coordinate system whose X-axis is consistent with the main direction of the bridge. In this new coordinate system, the variance of the data is maximized, and the covariance between different coordinate axes is zero, that is, the linear correlation between the data is eliminated, which is conducive to the automatic extraction and linear fitting of the subsequent main cables, avoiding the redundancy of the coordinates after projection.

[0070] Of course, those skilled in the art will appreciate that the coordinate system of the point cloud data can be converted to a coordinate system in which the Y-axis or Z-axis is consistent with the longitudinal direction of the bridge deck; wherein, when the coordinate system of the point cloud data is converted to a coordinate system in which the Y-axis is consistent with the longitudinal direction of the bridge deck, the X-axis of the coordinate system is perpendicular to the longitudinal direction and in the plane of the bridge deck, and the Z-axis is perpendicular to the upward direction of the bridge deck; when the coordinate system of the point cloud data is converted to a coordinate system in which the Z-axis is consistent with the longitudinal direction of the bridge deck, the Y-axis of the coordinate system is perpendicular to the longitudinal direction and in the plane of the bridge deck, and the X-axis is perpendicular to the upward direction of the bridge deck.

[0071] Step 300), based on the point cloud data after the coordinate system conversion, determine the point cloud data of each section of the main cable of the suspension bridge.

[0072] In step 300), in one example, based on the point cloud data after coordinate system conversion, point cloud data of each section of the main cable of the suspension bridge is determined, including: projecting the point cloud data after coordinate system conversion, and determining the point cloud data of each section of the main cable of the suspension bridge based on the projected data and a preset density threshold.

[0073] In one example, the point cloud data after the coordinate system conversion is projected, and the point cloud data of each section of the main cable of the suspension bridge is determined based on the projected data and a preset density threshold, including:

[0074] Project the point cloud data after coordinate system conversion to the YOZ plane (the X-axis is consistent with the longitudinal direction of the bridge deck) to obtain the point cloud density curve;

[0075] According to the point cloud density curve and the preset density threshold, the point cloud data of the main beam of the suspension bridge is stripped;

[0076] Density statistics are performed on the XOY plane for the remaining point cloud data except the point cloud data of the main beam in the point cloud data after the coordinate system conversion, and the point cloud data of a single main cable of the suspension bridge is separated;

[0077] The point cloud data of a single main cable is projected onto the YOZ plane to determine the point cloud data of each section of the main cable of the suspension bridge.

[0078] Project the entire point cloud to the YOZ plane, find the mutation position according to the density curve, and peel off the point cloud of the main beam. Then, perform density statistics on the remaining point cloud on the XOY plane to separate a single main cable. Finally, project it to the YOZ plane to separate each section of the main cable. After this process, the point clouds of the mid-span main cable, side-span main cable, main tower and suspender can be accurately and quickly segmented.

[0079] Step 400), slicing and reducing the dimension of the point cloud data of each section of the main cable, and determining the center and radius of each piece of point cloud data after slicing and reducing the dimension.

[0080] In step 400), in one example, the point cloud data of each section of the main cable is sliced ​​and dimensionally reduced, including:

[0081] For each section of the main cable, the point cloud data of the main cable is sliced ​​at equal intervals; wherein the slicing distance is not less than the distance of the main cable diameter;

[0082] For each slice of each main cable segment, determine the normal vector of each point in the point cloud data of the slice;

[0083] Using a clustering algorithm, the normal vectors of each point in the point cloud data of each slice are clustered to obtain the point cloud axis vector of each slice;

[0084] Based on the point cloud axis vector of each slice of each main cable segment, the 3D point cloud is converted to a 2D plane.

[0085] In one example, determining the center and radius of each piece of point cloud data after slicing and dimensionality reduction includes:

[0086] Based on the repetitive least squares method, the center and radius of each piece of point cloud data after slicing and dimensionality reduction are determined.

[0087] In one example, based on the repetitive least clipping squares method, the center and radius of each piece of point cloud data after slicing and dimensionality reduction are determined, including:

[0088] For each piece of point cloud data after slicing and dimensionality reduction, randomly select a first preset number of point cloud data, and determine an initial circle center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle;

[0089] Based on the initial center and radius, calculate the residual from each point of each point cloud data to the initial fitting circle;

[0090] Selecting a second preset number of point cloud data corresponding to the minimum residual from the residuals; wherein the second preset number is less than or equal to the first preset number;

[0091] The second preset number of point cloud data is used as the new first preset number of point cloud data, and the steps of determining the initial center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle are re-executed until the stopping condition is reached to obtain the center and radius of the final fitting circle.

[0092] Through the slicing step, each point cloud can be cut into multiple slices, and the axis of each small slice can be approximately regarded as a straight line for subsequent processing; in this way, the clustering results can reveal the main direction or multiple directions of the point cloud as a whole. Through the dimensionality reduction step, the axis vector of each small slice point cloud can be extracted, and the 3D point cloud can be converted to a 2D plane, preparing for the next step of fitting a plane circle.

[0093] Step 500), the main cable shape of the suspension bridge is obtained according to the center and radius of each piece of point cloud data of each section of the main cable.

[0094] In step 500), in one example, the main cable shape of the suspension bridge is obtained according to the center and radius of each piece of point cloud data of each section of the main cable, including:

[0095] According to the center and radius of each piece of point cloud data of each section of the main cable, the coordinates of the center point of each section of the main cable are obtained;

[0096] Perform quadratic fitting on the coordinates of the center point of each section of the main cable to obtain the center line of each section of the main cable;

[0097] Project the center lines of two adjacent main cables in the XOZ plane respectively, and determine the intersection coordinates of the main cables of the suspension bridge according to the projection results;

[0098] The main cable shape of the suspension bridge is obtained based on the coordinates of the intersection of the center line of each main cable segment and the main cable of the suspension bridge.

[0099] In this embodiment, the present invention proposes a method for reconstructing the linear shape of the main cable of a suspension bridge with robustness, which obtains the point cloud data of the suspension bridge through three-dimensional scanning; converts the coordinate system of the point cloud data to a coordinate system with the longitudinal direction of the bridge deck as the main direction; determines the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion; slices and reduces the dimension of the point cloud data of each section of the main cable, and determines the center and radius of each piece of point cloud data after slicing and dimensionality reduction; obtains the linear shape of the main cable of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable. This application aims to overcome the problems of incomplete data and clustering of abnormal points in the prior art through improved data processing (KL coordinate system conversion, automatic extraction of main cables) and fitting algorithms (Repeatable Least Trimming Squares RLTS), so as to provide more accurate and reliable technical support for structural monitoring and health assessment of suspension bridges.

[0100] The following is another exemplary embodiment of the present application, referring to Figure 2 The following embodiment of the present invention provides a method for measuring the main cable shape of a suspension bridge, comprising:

[0101] Step a), obtaining point cloud data of the suspension bridge.

[0102] Step b), KL coordinate system conversion.

[0103] Step c), automatic extraction of the main cable.

[0104] Step d), point cloud slicing and dimensionality reduction.

[0105] Step e), RLTS fitting.

[0106] Step f), main cable linear acquisition.

[0107] In step a), acquisition of suspension bridge point cloud data

[0108] In one example, a Leica ScanStation P40 3D laser scanner is used to scan a suspension bridge to obtain point cloud data of the suspension bridge.

[0109] Survey station arrangement, point cloud data denoising and point cloud data compression, wherein the specific steps may refer to the description in the above embodiment.

[0110] Set the distance threshold D th The formula can refer to the following expression:

[0111]

[0112] Where: D th is the sampling distance threshold, that is, the maximum allowable distance between adjacent points, in meters or millimeters; L is the total length of the main cable of the suspension bridge, in meters; N is the total number of point clouds collected in the scanning area; α is a constant coefficient used to adjust the ratio of the threshold, α = a value between 0.1 and 1; higher α values ​​(such as 0.7 to 1): increase D th , allowing the maximum distance between adjacent points to be larger, thus generating a sparser point cloud with less data but possible loss of detail. Lower α values ​​(such as 0.1 to 0.3): Reduce D th , limiting the maximum distance between adjacent points, retaining more details but increasing the amount of data and processing time.

[0113] Among them, factors to be considered when setting α are: Detail requirements: If high-precision modeling or detailed inspection is required, choose a lower α; if it is only used for general evaluation, choose a higher α.

[0114] Data noise level: If there is a lot of noise in the point cloud, a higher α can help filter out the noise by relaxing the distance between points.

[0115] Computing resources: If computing resources are limited, you can choose a higher α to reduce the amount of data; if resources are sufficient, you can choose a lower α to retain more details.

[0116] Measurement accuracy: Devices with higher accuracy are suitable for lower α values ​​to preserve measurement accuracy. For low-precision devices, higher α values ​​can avoid over-reliance on data points that may have errors.

[0117] β is an exponential coefficient used to adjust the effect of point cloud density on the threshold. Usually, the value of β can range from 0.5 to 1. Smaller values ​​reduce the effect of point density changes on D. th, providing a more stable threshold in areas with large density changes. Larger values ​​can make it more sensitive to changes in point density. This is useful when you need to dynamically adjust the threshold based on the amount of data.

[0118] Combined with the point cloud of the 100m pedestrian suspension bridge, the total length of the main cable is L = 100m, the number of collected point clouds is N = 1000000, and α = 0.2 is selected based on the high detail requirements, precise scanning equipment, and sufficient computing resources; β = 0.7 is selected based on the requirements of moderate point cloud density change and enhanced filtering consistency; From the formula, we can get

[0119]

[0120] In step b), KL coordinate system conversion

[0121] The coordinate system of the point cloud data of the suspension bridge usually obtained by three-dimensional scanning is generally an engineering coordinate system or a geodetic coordinate system. In these coordinate systems, the direction of the coordinate axis is not necessarily parallel to the main direction of the suspension bridge. In order to facilitate the automatic extraction and linear fitting of the subsequent main cable and avoid the redundancy of the coordinates after projection, the embodiment of the present application uses KL transformation to convert the suspension bridge point cloud into a coordinate system in which the X axis is consistent with the main direction of the bridge.

[0122] The core idea of ​​KL transformation (Karhunen-Loeve Transform) is to project the original data into a new coordinate system so that the variance of the data is maximized in this new coordinate system, and the covariance between different coordinate axes is zero, that is, the linear correlation between the data is eliminated. The specific steps are as follows:

[0123] First, the point cloud data (x, y, z) of the suspension bridge obtained by 3D scanning is obtained, and then data centering is performed. The data centering includes calculating the mean vector; centering the data; calculating the covariance matrix to form a data matrix; calculating the eigenvalues ​​and eigenvectors; constructing a new coordinate system; projecting the data to the new coordinate system, so as to realize the conversion of the coordinate system of the point cloud data of the suspension bridge obtained by 3D scanning to the coordinate system with the longitudinal direction of the bridge deck as the main direction, so as to facilitate the automatic extraction and linear fitting of the subsequent main cables and avoid the redundancy of the coordinates after projection.

[0124] Optionally, the coordinate system conversion specifically includes: (1) data preparation: point cloud data (x, y, z) of the suspension bridge.

[0125] (2) Data centering: ① Calculate the mean vector: Calculate the average value of all points in the x, y, and z directions.

[0126]

[0127] ②Centered data: Subtract the mean vector from the coordinates of each point to move the center of the data to the origin.

[0128]

[0129] ③ Calculate the covariance matrix and form a data matrix: organize the centralized data into an N×3 matrix X.

[0130] Calculate the covariance matrix

[0131] ④ Calculate eigenvalues ​​and eigenvectors: Perform eigendecomposition on the covariance matrix C to obtain the eigenvalue λ 1 >λ 2 >λ 3 and the corresponding eigenvector V 1 ,V 2 ,V 3 , where Cv i =λ i v i .

[0132] ⑤Construct a new coordinate system: the first principal component V 1 : corresponds to the largest eigenvalue, indicating the direction with the largest data variance, which is the longitudinal direction of the bridge; the second principal component V 2 : corresponds to the transverse direction of the bridge; the third principal component V 3 : Perpendicular to the bridge deck direction.

[0133] Establish the transformation matrix P: P = [v 1 、v 2 、v 3 ]

[0134] ⑥ Project the data to the new coordinate system:

[0135]

[0136] (x″ i ,y″ i ,z″ i ) is the point cloud data with the vertical and horizontal directions of the bridge as coordinate axes.

[0137] Through this step, the x-axis of the suspension bridge point cloud coordinates can be converted to the longitudinal direction of the bridge, and the y-axis can be converted to the transverse direction of the bridge.

[0138] In step c), the automatic extraction algorithm of the main cable

[0139] This implementation can automatically extract the main cable based on projection density filtering. Here, considering that the main cable segments and hangers of the suspension bridge are large in number, if they are separated manually, the efficiency is low and the time is long. Therefore, it is particularly necessary to develop an automated point cloud separation program. Compared with other buildings, bridge structures have the characteristics of independent components, no obstruction, and obvious geometric features.

[0140] The point density of the unit plane is:

[0141]

[0142] Where ΔA is the unit area and N is the number of points within ΔA.

[0143] ΔA=m×n

[0144] m is the absolute value of the length of the point cloud in the y direction, and all point clouds should be accommodated in it:

[0145] m=|max(Ky)-min(Ky)|

[0146] Among them, Ky is the value of the y direction of all point clouds.

[0147] In this embodiment, when automatically extracting the main cable of a suspension bridge, the X-axis of the converted coordinate system is consistent with the longitudinal direction of the bridge deck, the Y-axis is perpendicular to the longitudinal direction of the bridge deck and in the plane of the bridge deck, and the Z-axis is perpendicular to the upward direction of the bridge deck. First, the overall point cloud is projected to the YOZ plane, and the mutation position is found according to the density curve, and the point cloud of the main beam part is stripped. Then, the density of the remaining point cloud is counted on the XOY plane to separate a single main cable. Finally, it is projected to the YOZ plane to separate each section of the main cable.

[0148] After this process, the point clouds of the mid-span main cables, side-span main cables, main towers and hangers can be segmented accurately and quickly.

[0149] In step d), the point cloud is sliced ​​and dimensionally reduced.

[0150] In this embodiment, considering that the main cable of the suspension bridge has a curved surface or a cylinder, it is difficult to analyze the whole when studying its deformation. Therefore, the point cloud data obtained by scanning is first sliced; then the normal vectors are clustered with the cut point cloud slices to find the axial vector of each slice and convert the spatial point cloud slices to a 2D plane; the center and radius of each slice of the main cable slice point cloud in the 2D plane are obtained by the proposed repeated least truncated squares method; finally, the three-dimensional coordinates of the feature points are connected to draw a curve graph to find the line shape of the main cable.

[0151] Exemplarily, when performing point cloud slicing: In this embodiment, cloudcompare (three-dimensional point cloud processing software) can be used to perform equidistant slicing on the main cable point cloud, with a slicing distance not less than the main cable diameter.

[0152] Through this step, each point cloud can be cut into multiple slices, and the axis of each small slice can be approximately regarded as a straight line for subsequent processing.

[0153] Optionally, after slicing the point cloud, this embodiment can perform dimensionality reduction based on slicing of normal vector clustering. For example, for each point in the point cloud, first determine its neighborhood, usually defined by K nearest neighbors or spatial range. The size of the neighborhood definition or the number of points affects the locality and globality of the normal vector calculation. Within this neighborhood, the eigendecomposition of the covariance matrix is ​​used to calculate the normal vector of the neighborhood. This normal vector is usually obtained by the eigenvector corresponding to the minimum eigenvalue in the covariance matrix, representing the direction of the local plane or surface in the neighborhood.

[0154] When the normal vectors of all points are calculated, it is found that some normal vectors point in similar directions, which reflects the consistency of local geometric structures, such as local planes or axially consistent structures of the point cloud. This embodiment can use a clustering algorithm (such as K-means, DBSCAN, etc.) to cluster all normal vectors and classify those normal vectors with similar directions into the same category. In this way, the clustering results can reveal the main direction or multiple directions of the entire point cloud.

[0155] After clustering, the normal vector direction with the largest cluster is selected, or the main axis of the point cloud is determined by the average direction of all normal vectors. This main direction can usually be used as a basis for aligning or rotating the point cloud. For example, the point cloud can be rotated to align this main axis with a fixed axis of the coordinate system (such as the x-axis).

[0156] This step can be used to extract the axis vector of each small slice point cloud and convert the 3D point cloud to a 2D plane, preparing for the next step of fitting a plane circle.

[0157] In step e), RLTS is fitted.

[0158] In this embodiment, in order to solve the problem of outliers and incomplete data, a repetitive least trimmed squares (RLTS) regression is used. RLTS only considers the h subset of most points (n>h>n / 2), which is based on an assumption that more concentrated points have a greater chance of being on the circle itself. Determining h depends on the knowledge of the percentage of outliers in the data. The inventors of the present application have observed that the outliers of laser scanning point clouds usually do not exceed 50%. Therefore, in order to avoid extreme effects on the results, the parameter h is limited to 0.5n.

[0159] The new algorithm (RLTS) follows an iterative process to obtain a subset free of outliers on which the LTS algorithm is performed. To save time, the algorithm starts with the smallest h 0 Instead of fitting a circle from h points, we start with a subset of points, where, in one example, h 0 is the minimum number of three points that can determine the center and radius of the fitted circle. In order to obtain a subset without outliers, a Monte Carlo type probabilistic method is used. n , using the following formula:

[0160]

[0161] in To get h 0 The probability of at least one outlier-free subset among the points is, ∈ is the percentage of outliers. This embodiment is accomplished by setting ∈=0.50 and pr=0.999 based on actual scanning data experience.

[0162] The iterations are repeated to obtain a robust and best fitting circle using most of the h-points. The LTS circle fitting step is evaluated and repeated ten times.

[0163] Exemplarily, this embodiment may include the following when performing RLTS fitting:

[0164] Step a: Based on the minimum h 0 A subset of points, fit a circle, calculate the center C 0 (a 0 ,b 0 ), radius R 0 and the residual e i (i=1,2,…,n), e i It is defined by the following formula and figure:

[0165]

[0166] Step b: Sort the points in ascending order of residual squares (i.e. ), select h points with the smallest residual square;

[0167] Step c: Fit a circle based on point h in step b and calculate the center C h (a h , b h ) and radius R h The squared residuals are then sorted and the smallest h-squared residuals are summed.

[0168] Until the residual sum of squares (SS e) the smallest subset of h, stops iteration, and estimates the circle parameters of the final fitted circle based on the subset of h: the center C(a, b) and the radius R. This algorithm is called the Repeatable Least Trimmed Squares (RLTS) algorithm in this article.

[0169] In an example, the RLTS fitting pseudo code is as follows:

[0170] Among them, input:

[0171] P: Point cloud data, that is, the collection of all data points.

[0172] h 0 : Minimum sample size (number of initial points, used to fit the initial circle, usually 3).

[0173] h: cropping sample size (the number of inliers retained in each iteration, used to correct the circle fit).

[0174] I n : Maximum number of iterations: 50.

[0175] Among them, the output is:

[0176] C(a,b): center coordinates of the fitted circle.

[0177] R: The radius of the fitted circle.

[0178] 1. Initialization: Set the residual sum of squares SSE 0 =∞, set it to a large initial value to store the residual sum of squares of the best fit. n = length(P): the number of points in the point cloud data.

[0179] 2. Main loop: When I n > 0 to start the iteration (i.e., the number of iterations is specified).

[0180] 3. Multiple sampling cycles:

[0181] For each iteration, h is randomly selected from the data points 0 points as the initial sampling point set.

[0182] 4. Supplementary point set:

[0183] If the rank of the selected point set is less than m (i.e., not enough to fit a circle), continue to add random points until the rank meets the requirement.

[0184] 5. Preliminary fitting circle:

[0185] Use the Hyper method in this h 0 Fit a circle on the points and estimate the initial center C(a 0 ,b 0 ) and radius R 0.

[0186] 6. Calculate the residual:

[0187] For each point (x j ,y j ) Calculate the residual e from the initial fitting circle j The formula is:

[0188]

[0189] 7. Sort residuals and select interior points:

[0190] Square the residual Sorting is performed and h points with the smallest residuals (i.e., the points closest to the initial fitting circle) are selected to form a new point set for further fitting the circle.

[0191] 8. Accurately fit the circle:

[0192] Use these selected h points to fit the circle again using the Hyper method to estimate the new center C(a h ,b h ) and radius R h

[0193] 9. Calculate the new residual:

[0194] Calculate the residual e from all points to the exact fitting circle j .

[0195] 10. Calculate the residual sum of squares after clipping:

[0196] Square the above residual Sort from small to large, take the first h residual squares and sum them up to get the trimmed residual sum of squares SSE i .

[0197] 11. Update best fit:

[0198] Compare the current residual sum of squares (SSE) i and the best residual sum of squares SSE before i-1 If SSE i <SSE i-1 Then update the parameters C(a, b) and R of the fitted circle. Otherwise, keep the previous circle parameters as the best fitting result.

[0199] 12. Continue iteration or terminate:

[0200] If the maximum number of iterations has not been reached, continue to repeat the above process.

[0201] 13. When the number of iterations is not specified:

[0202] If the number of iterations is not specified, the final circle is fit using the Hyper method on all data points.

[0203] 14. Output results:

[0204] Finally, the center C(a, b) and radius R of the best fitting circle are output.

[0205] In step f), the main cable is linearly acquired.

[0206] The main cable linear acquisition includes: the formation of the main cable centerline and the acquisition of the intersection point.

[0207] Formation of the centerline of the main cable: Repeat the above method to perform circle fitting on each segment of the main cable, and obtain the coordinates of the center point of each segment of the main cable according to the fitted circle. Figure 3 , these center points are fitted with quadratic terms to obtain the high-precision center line of the main cable segment. This embodiment takes into account the influence of point cloud noise, circle center fitting, etc., and the center point cannot avoid errors. If a high-order polynomial is used, there will be overfitting, and the sag of the main cable segment is usually small. Therefore, quadratic polynomial fitting is used to solve the above problem.

[0208] Obtaining the intersection point: After the center line of each main cable segment is obtained, due to the existence of circle fitting errors, the center lines of two adjacent main cable segments may not intersect after extension. In this embodiment, the X-axis of the coordinate system after the above conversion is consistent with the longitudinal direction of the bridge deck, the Y-axis is perpendicular to the longitudinal direction of the bridge deck and in the plane of the bridge deck, and the Z-axis is perpendicular to the upward direction of the bridge deck. The two center lines are projected into the XOZ plane. Taking center line 1 and center line 2 as examples, according to the projection results, the elevation points of center line 1 and center line 2 at the hanger position xs are determined, that is, the intersection of the main cable and the hanger. Here, this embodiment takes into account the existence of fitting errors and uses the average value of the two elevation points as the z coordinate value of the intersection.

[0209] In order to solve the problem of how to effectively process these incomplete and abnormal data and improve the accuracy and reliability of the linear fitting of the main cable of the suspension bridge based on three-dimensional laser scanning, an embodiment of the present invention proposes a robust point cloud linear reconstruction method for the main cable of the suspension bridge. This application aims to overcome the problems of incomplete data and abnormal point clustering in the prior art through improved data processing (KL coordinate system conversion, automatic extraction of main cables) and fitting algorithm (Repeatable Least Trimmed Squares method RLTS), so as to provide more accurate and reliable technical support for the structural monitoring and health assessment of suspension bridges.

[0210] The present invention also provides a device 400 for measuring the main cable shape of a suspension bridge, referring to Figure 4 ,include:

[0211] The bridge scanning module 410 is used to obtain point cloud data of the suspension bridge through three-dimensional scanning;

[0212] A coordinate system conversion module 420 is used to convert the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction;

[0213] The main cable determination module 430 is used to determine the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion;

[0214] The processing and fitting module 440 is used to slice and reduce the dimension of the point cloud data of each section of the main cable, and determine the center and radius of each piece of point cloud data after slicing and dimensionality reduction;

[0215] The linear acquisition module 450 is used to obtain the main cable linear shape of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable.

[0216] Figure 5 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 50 and a memory 51. The memory 51 is used to store a computer program 52, and the processor 50 is used to call and run the computer program 52 stored in the memory 51 to perform the steps in the above-mentioned foreign body identification method embodiments, such as Figure 1 Alternatively, the processor 50 is used to call and run the computer program 52 stored in the memory 51 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 4 Functionality of modules / units 410 to 450 is shown.

[0217] Exemplarily, the computer program 52 may be divided into one or more modules / units, one or more modules / units are stored in the memory 51 and executed by the processor 50 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 may be divided into Figure 3 Modules / units 301 to 304 are shown.

[0218] The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0219] The processor 50 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. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0220] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store computer programs and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0221] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0222] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0223] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0224] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0225] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0226] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0227] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. 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 computer program code, recording medium, U disk, mobile hard disk, 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, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0228] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for measuring the main cable shape of a suspension bridge, characterized in that: include: Obtain point cloud data of the suspension bridge through 3D scanning; Converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction; Determining the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion; Slice and reduce the dimension of the point cloud data of each section of the main cable respectively, and determine the center and radius of each piece of point cloud data after slicing and dimensionality reduction; The main cable line shape of the suspension bridge is obtained according to the center and radius of each piece of point cloud data of each section of the main cable.

2. The method for measuring the main cable shape of a suspension bridge according to claim 1, characterized in that: The step of converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction comprises: Using KL transformation, the coordinate system of the point cloud data is converted into a coordinate system whose X-axis is consistent with the longitudinal direction of the bridge deck; Among them, the Y-axis is the direction perpendicular to the longitudinal direction of the bridge deck and within the plane of the bridge deck, and the Z-axis is the direction perpendicular to the bridge deck and pointing upward.

3. The method for measuring the main cable shape of a suspension bridge according to claim 2, characterized in that: The step of determining the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion includes: The point cloud data after the coordinate system conversion is projected, and the point cloud data of each section of the main cable of the suspension bridge is determined based on the projected data and a preset density threshold.

4. The method for measuring the main cable shape of a suspension bridge according to claim 3, characterized in that: The step of projecting the point cloud data after the coordinate system conversion and determining the point cloud data of each section of the main cable of the suspension bridge based on the projected data and a preset density threshold comprises: Project the point cloud data after coordinate system conversion onto the YOZ plane to obtain the point cloud density curve; According to the point cloud density curve and the preset density threshold, the point cloud data of the main beam of the suspension bridge is stripped; Density statistics are performed on the XOY plane for the remaining point cloud data except the point cloud data of the main beam in the point cloud data after the coordinate system conversion, and the point cloud data of a single main cable of the suspension bridge is separated; The point cloud data of a single main cable is projected onto the YOZ plane to determine the point cloud data of each section of the main cable of the suspension bridge.

5. The method for measuring the main cable shape of a suspension bridge according to any one of claims 1 to 4, characterized in that: The point cloud data of each section of the main cable are sliced ​​and dimensionally reduced, including: For each section of the main cable, the point cloud data of the main cable is sliced ​​at equal intervals; wherein the slicing distance is not less than the distance of the main cable diameter; For each slice of each main cable segment, determine the normal vector of each point in the point cloud data of the slice; Using a clustering algorithm, the normal vectors of each point in the point cloud data of each slice are clustered to obtain the point cloud axis vector of each slice; Based on the point cloud axis vector of each slice of each main cable segment, the 3D point cloud is converted to a 2D plane.

6. The method for measuring the main cable shape of a suspension bridge according to any one of claims 1 to 4, characterized in that: The step of determining the center and radius of each piece of point cloud data after slicing and dimensionality reduction includes: Based on the repetitive least squares method, the center and radius of each piece of point cloud data after slicing and dimensionality reduction are determined.

7. The method for measuring the main cable shape of a suspension bridge according to claim 6, characterized in that: The method of determining the center and radius of each piece of point cloud data after slicing and dimensionality reduction based on the repetitive least clipping squares method includes: For each piece of point cloud data after slicing and dimensionality reduction, randomly select a first preset number of point cloud data, and determine an initial circle center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle; Based on the initial circle center and radius, calculate the residual from each point of the point cloud data to the preliminary fitting circle; Selecting a second preset number of point cloud data corresponding to the smallest residual from the residuals; wherein the second preset number is less than or equal to the first preset number; The second preset number of point cloud data is used as the new first preset number of point cloud data, and the steps of determining the initial center and radius based on the first preset number of point cloud data to obtain a preliminary fitting circle are re-executed until the stopping condition is reached to obtain the center and radius of the final fitting circle.

8. The method for measuring the main cable shape of a suspension bridge according to any one of claims 2 to 4, characterized in that: The method of obtaining the main cable line shape of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable comprises: According to the center and radius of each piece of point cloud data of each section of the main cable, the coordinates of the center point of each section of the main cable are obtained; Perform quadratic fitting on the coordinates of the center point of each section of the main cable to obtain the center line of each section of the main cable; Projecting the center lines of two adjacent main cables in the XOZ plane respectively, and determining the intersection coordinates of the main cables of the suspension bridge according to the projection results; Based on the coordinates of the intersection of the center line of each section of the main cable and the main cable of the suspension bridge, the main cable line shape of the suspension bridge is obtained.

9. A device for measuring the main cable shape of a suspension bridge, characterized in that: include: Bridge scanning module, used to obtain point cloud data of suspension bridges through 3D scanning; A coordinate system conversion module, used for converting the coordinate system of the point cloud data into a coordinate system with the longitudinal direction of the bridge deck as the main direction; A main cable determination module, used to determine the point cloud data of each section of the main cable of the suspension bridge based on the point cloud data after the coordinate system conversion; A processing and fitting module is used to slice and reduce the dimension of the point cloud data of each section of the main cable, and determine the center and radius of each piece of point cloud data after slicing and dimensionality reduction; The linear acquisition module is used to obtain the main cable linear shape of the suspension bridge according to the center and radius of each piece of point cloud data of each section of the main cable.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method for measuring the main cable shape of a suspension bridge according to any one of claims 1 to 8 is implemented.

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