Bridge main girder structure space overall deformation detection method, terminal and storage medium

Abstract

The application provides a bridge main girder structure space overall deformation detection method, a terminal and a storage medium, and relates to the technical field of bridge main girder deformation detection. The method comprises the following steps: acquiring a first main girder bottom surface point cloud and a second main girder bottom surface point cloud of a bridge to be measured; a plurality of first point cloud blocks and a plurality of second point cloud blocks are divided from the first main girder bottom surface point cloud and the second main girder bottom surface point cloud respectively by using a sliding window; a plane fitting is performed on each point cloud block by using a maximum consistent minimum distance algorithm; a center point of each fitted plane is taken as a measurement point; a plurality of first measurement points and a plurality of second measurement points are acquired; the plurality of first measurement points and the plurality of second measurement points are used to construct a first curved surface and a second curved surface respectively; Z-axis data is extracted from the first curved surface and the second curved surface respectively based on the center position of each sliding window, and the Z-axis data is subtracted to obtain a periodic deformation of the bridge to be measured. The application can improve the precision of deformation detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge girder deformation detection, and in particular to a bridge girder structure spatial overall deformation detection method, a terminal and a storage medium. BACKGROUND

[0002] Bridge deformation is an important indicator for evaluating the safety of the structure in bridge structure health monitoring, and reflects the response of the structure to degradation or even damage. A large amount of evidence shows that by regularly monitoring the deformation amplitude of key points, the degree of attenuation of the structural stiffness can be evaluated, and the finite element model can be updated. Based on the high-precision deformation mode, the structure damage can be further located and quantified. Therefore, efficiently and accurately identifying the deformation mode of the structure, i.e., identifying the deformation amplitude and deformation form at any position, has become an important goal of bridge structure health monitoring.

[0003] Traditional bridge girder alignment monitoring methods usually rely on ground measurement, photogrammetry and unmanned aerial vehicle technology, which have certain limitations. Especially in the high places or complex environments of long-span suspension bridges, it is difficult to obtain high-precision and high-resolution data. At the same time, with the rapid development of three-dimensional laser scanning technology, obtaining bridge girder point cloud data based on laser scanners has become a new research and application trend. Through the three-dimensional laser scanner, high-precision and full-coverage scanning of the main girder structure form can be realized, thereby providing a large amount of three-dimensional data for the spatial deformation of the main girder.

[0004] However, the current bridge girder overall deformation obtained by the three-dimensional laser scanning technology still faces certain challenges and problems. First, there is no systematic and automatic extraction scheme for obtaining the point cloud of the main girder structure bottom surface. Second, due to the interference of environmental factors (such as wind, rain, light, etc.) or the precision limitation of the equipment in the data collection process of the three-dimensional laser scanner, there are often clustering abnormal points in the obtained point cloud data, which may be caused by reflection, noise and other factors, further affecting the accurate restoration of the main cable alignment. Finally, the existing researches usually only stay at the stage of planar alignment deformation, and there is no method to express the spatial overall deformation. SUMMARY

[0005] The present application provides a bridge girder structure spatial overall deformation detection method, a terminal and a storage medium to solve the problem of low accuracy of bridge deformation detection in the prior art.

[0006] In a first aspect, the present application provides a bridge girder structure spatial overall deformation detection method, comprising:

[0007] obtaining a first main girder bottom surface point cloud and a second main girder bottom surface point cloud of a bridge to be measured, wherein the acquisition time of the first main girder bottom surface point cloud is later than the acquisition time of the second main girder bottom surface point cloud;

[0008] A plurality of first point cloud blocks and a plurality of second point cloud blocks are divided from the first girder bottom surface point cloud and the second girder bottom surface point cloud respectively by using a sliding window, and a plane fitting is performed on each point cloud block by using a maximum consistent minimum distance algorithm, a center point of each fitted plane is taken as a measurement point, and a plurality of first measurement points and a plurality of second measurement points are obtained, wherein each first measurement point corresponds to a first point cloud block, and each second measurement point corresponds to a second point cloud block;

[0009] A first curved surface and a second curved surface are constructed respectively by using the plurality of first measurement points and the plurality of second measurement points, Z-axis data is extracted from the first curved surface and the second curved surface respectively based on a center position of each sliding window, and a difference is made to obtain a periodic deformation of the bridge to be measured.

[0010] In a second aspect, a terminal is provided, which includes 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 method in the first aspect or any possible implementation manner of the first aspect when executing the computer program.

[0011] In a third aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the method in the first aspect or any possible implementation manner of the first aspect when executed by a processor.

[0012] The application provides a bridge main girder structure space overall deformation detection method, a terminal and a storage medium. The first main girder bottom surface point cloud and the second main girder bottom surface point cloud of a bridge to be measured are acquired, wherein the acquisition time of the first main girder bottom surface point cloud is later than the acquisition time of the second main girder bottom surface point cloud; a plurality of first point cloud blocks and a plurality of second point cloud blocks are divided from the first main girder bottom surface point cloud and the second main girder bottom surface point cloud respectively by using a sliding window, and plane fitting is performed on each point cloud block respectively by using a maximum consistent minimum distance algorithm; the center point of each fitting plane is taken as a measurement point, a plurality of first measurement points and a plurality of second measurement points are acquired, wherein each first measurement point corresponds to a first point cloud block, and each second measurement point corresponds to a second point cloud block; the plurality of first measurement points and the plurality of second measurement points are constructed into a first curved surface and a second curved surface respectively, and Z-axis data is extracted from the first curved surface and the second curved surface respectively based on the center position of each sliding window, and the difference is obtained, thereby obtaining the periodic deformation of the bridge to be measured. According to the application, the main girder bottom surface point cloud data at different times is acquired, plane fitting is performed by using a sliding window and a maximum consistent minimum distance algorithm, the feature points of the main girder bottom surface can be accurately extracted, the influence of noise and abnormal values on the measurement result can be effectively reduced, and the deformation detection precision is improved; and according to the application, the first curved surface and the second curved surface are constructed, and the Z-axis data is extracted based on the center position of the sliding window and the difference is obtained, thereby comprehensively reflecting the overall deformation of the main girder in space, helping to identify the deformation difference of the main girder at different positions, and providing an important basis for the safety evaluation and maintenance of the bridge. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 It is the implementation flowchart of the bridge main girder structure space overall deformation detection method provided by the embodiments of the present application.

[0015] Figure 2 It is a bridge point cloud up-down flipping schematic diagram provided by the embodiments of the present application.

[0016] Figure 3 It is a main girder bottom surface seed point set schematic diagram provided by the embodiments of the present application.

[0017] Figure 4 It is a seed point feature schematic diagram provided by the embodiments of the present application.

[0018] Figure 5 It is a region normal vector feature schematic diagram provided by the embodiments of the present application.

[0019] Figure 6 is a sliding window movement schematic diagram provided by an embodiment of the present application;

[0020] Figure 7 is a sliding window parameter setting schematic diagram provided by an embodiment of the present application;

[0021] Figure 8 is a vertical distance schematic diagram provided by an embodiment of the present application;

[0022] Figure 9 is a measurement point schematic diagram provided by an embodiment of the present application;

[0023] Figure 10 is a plane fitting flow schematic diagram provided by an embodiment of the present application;

[0024] Figure 11 is an iteration reserved point selection schematic diagram provided by an embodiment of the present application;

[0025] Figure 12 is an orthogonal distance schematic diagram provided by an embodiment of the present application;

[0026] Figure 13 is a seed point selection schematic diagram of h orthogonal distances provided by an embodiment of the present application;

[0027] Figure 14 is a structure schematic diagram of a bridge girder structure space overall deformation detection device provided by an embodiment of the present application;

[0028] Figure 15 is a schematic diagram of a terminal provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following description, specific details are set forth in order to provide a thorough understanding of the present application. However, persons of ordinary skill in the art will readily recognize that the application can be practiced without these specific details, or with an equivalent arrangement and / or method. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0030] In order to make the purpose, technical scheme and advantages of the present application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0031] Currently, the bridge girder deformation detection method based on three-dimensional laser scanning mostly stays in the simple linear fitting stage, and usually assumes that the data is complete and noise-free point cloud. However, in actual scanning, there are usually a large number of clustered noise points and sparse data, which will cause errors in the deformation detection of the traditional method. In view of this problem, the application provides a bridge girder structure spatial overall deformation detection method, which provides more accurate and reliable technical support for bridge girder structure monitoring and health evaluation. First, an automatic extraction method of the main girder bottom surface point cloud is proposed, which realizes the acquisition of the main girder bottom surface seed point through two times of cloth simulation filtering algorithm. After obtaining the seed point, the neighborhood curvature, neighborhood maximum distance and neighborhood normal vector of the neighborhood point cloud are used to further extract the complete main girder bottom surface point cloud by using the region growing algorithm. Secondly, a main girder structure spatial continuous deformation detection mode is proposed, which combines the sliding window and the center point acquisition technology based on robust plane fitting, and realizes the spatial deformation detection of the main girder deflection. Finally, a robust plane fitting algorithm is proposed. For the plane fitting process in each sliding window, considering the influence of a large number of abnormal noise points and sparse data, a plane fitting method based on repeated clipping maximum consistent minimum distance is proposed.

[0032] Through comparison with the traditional algorithm, the plane fitting accuracy of the application is higher in the case of high noise points. Through the spatial form measurement of the PVC panel in the laboratory, the data obtained by the total station is compared with the application, which verifies that the application has high robustness and measurement accuracy, and meets the deformation measurement requirements in engineering practice.

[0033] Figure 1 The implementation flowchart of the bridge girder structure spatial overall deformation detection method provided by the embodiment of the application is described as follows:

[0034] In step 101, the first main girder bottom surface point cloud and the second main girder bottom surface point cloud of the bridge to be measured are obtained, wherein the acquisition time of the first main girder bottom surface point cloud is later than that of the second main girder bottom surface point cloud.

[0035] In the embodiment of the application, the TLS scanner is used to obtain the first main girder bottom surface point cloud and the second main girder bottom surface point cloud of the bridge to be measured. The scanning time of the first main girder bottom surface point cloud is later than that of the second main girder bottom surface point cloud, which aims to monitor the deformation of the bridge in this time interval.

[0036] In a possible implementation manner, obtaining the first main girder bottom surface point cloud and the second main girder bottom surface point cloud of the bridge to be measured can include:

[0037] The first bridge point cloud and the second bridge point cloud of the bridge to be measured are obtained, wherein the acquisition time of the first bridge point cloud is later than that of the second bridge point cloud;

[0038] A cloth simulation filtering algorithm is used to obtain the seed point set of the main beam bottom surface of the first bridge point cloud and the second bridge point cloud. The seed point set of the main beam bottom surface corresponding to the first bridge point cloud is used as the first seed point set, and the seed point set of the main beam bottom surface corresponding to the second bridge point cloud is used as the second seed point set.

[0039] Calculate the neighborhood curvature, maximum neighborhood distance, and neighborhood normal vector of the first seed point set to determine the point cloud of the bottom surface of the first main beam; and calculate the neighborhood curvature, maximum neighborhood distance, and neighborhood normal vector of the second seed point set to determine the point cloud of the bottom surface of the second main beam.

[0040] Cloth Simulation Filter (CSF) is a term in computer graphics. The core idea of ​​this method is to use a rigid cloth to cover an inverted point cloud surface to generate a surface that approximates the ground, thereby extracting ground points according to a set threshold.

[0041] Since the bottom surface of the bridge main girder is located above the bottom surface of the bridge, this embodiment uses a cloth simulation filtering algorithm to obtain the point cloud of the bottom surface of the main girder of the bridge under test. The main process is as follows:

[0042] Bridge point cloud acquisition: The first and second bridge point clouds of the bridge under test are acquired using a TLS scanner, and the acquisition time of the first bridge point cloud is later than that of the second bridge point cloud.

[0043] For each bridge point cloud, perform the following steps:

[0044] Preprocessing: Flip the bridge point cloud vertically, see details below. Figure 2 As shown, Figure 2 The bridge point cloud below can be obtained by flipping it upwards. Figure 2 The bridge above is dotted with clouds.

[0045] Cloth Mesh Initialization: Construct a uniformly distributed 2D mesh based on the preset mesh resolution S. All mesh nodes are initially placed at a uniform height, which must be higher than the flipped point cloud data. (Refer to...) Figure 2 The topmost curve represents the initial mesh. At this point, all mesh nodes are marked as "movable nodes" for subsequent participation in dynamic simulations.

[0046] Limit Height Determination: For each grid node, search for the nearest point cloud data point in the horizontal direction, and use the elevation of that point as the minimum allowable height (i.e., limit height) during the current grid node's descent. This operation ensures that the cloth does not penetrate the point cloud surface but rather adheres closely to its contours.

[0047] Simulate cloth sinking: under the action of gravity, let the height of all "movable nodes" decrease by a fixed amount H at each simulation time d If a node's height decreases to or below the corresponding limit height during the sinking process, lock its height at the limit value and update its state to "fixed node", indicating that the position has been fitted to the point cloud data.

[0048] Simulate cloth rebound: considering the rebound phenomenon that may occur after the cloth collides, for nodes still in the "movable" state, increase their height by a value H according to the preset cloth hardness parameter u This process can make the cloth have certain recovery, helping it better adapt to the ups and downs of the terrain.

[0049] Iterative optimization: repeat the "sinking" and "rebound" steps until the termination condition is met: for example, reach a predetermined number of iterations M or the maximum difference in height change of all nodes in two consecutive iterations is below a certain threshold. This iteration process makes the cloth gradually stabilize and closely fit the approximate ground surface formed by the inverted point cloud.

[0050] Ground point filtering: compare the elevation difference between each point in the original girder point cloud and the corresponding grid node. When the height difference between a point and the cloth surface is less than a predetermined threshold H c , the point can be classified as a ground point and filtered out.

[0051] Secondary CSF acquisition: after filtering the ground points, perform the above steps again. At this time, the girder bottom seed point set of the girder bottom surface will be obtained. For details, refer to Figure 3 , Figure 3 The virtual cloth in the above steps is the girder bottom seed point set.

[0052] After two CSF filters, the obtained points are the surface points of the girder bottom surface. In addition, there are various noise points in the scene (such as billboards, roadside facilities, tree and vegetation noise points, and a small amount of point cloud of bridge piers, etc.). In order to remove the noise points and complete the girder bottom point cloud, the embodiment of the application also develops a region growing method based on neighborhood features, that is, the neighborhood curvature, neighborhood maximum distance and neighborhood normal vector of the first seed point set and the second seed point set are calculated respectively to determine the corresponding first girder bottom point cloud and the second girder bottom point cloud.

[0053] In one possible implementation, calculating the neighborhood curvature, neighborhood maximum distance and neighborhood normal vector of the first seed point set to determine the first girder bottom point cloud can include:

[0054] Calculate the normal vector and curvature of each seed point in the first seed point set, and construct a candidate seed point set with all seed points whose curvatures are less than a curvature threshold. For each candidate seed point in the candidate seed point set, search the number of points belonging to the candidate seed point set within the K-neighborhood point set of the candidate seed point, and determine whether the number of points is not less than a first threshold. If the number of points is not less than the first threshold, the candidate seed point is added to the first main beam bottom surface point cloud.

[0055] Calculate the neighborhood Euclidean distance of each seed point in the first seed point set to all neighborhood points in its K-neighborhood, and for each seed point, determine whether the neighborhood maximum distance in all neighborhood Euclidean distances corresponding to the seed point is less than the maximum neighborhood radius of the bridge bottom surface seed point of the bridge to be measured. If the neighborhood maximum distance in all neighborhood Euclidean distances corresponding to the seed point is less than the maximum neighborhood radius of the bridge bottom surface seed point of the bridge to be measured, the seed point is added to the first main beam bottom surface point cloud.

[0056] For each seed point in the first seed point set, calculate the normal vector of the seed point, and search n points within a preset search radius of the seed point through the k-dimensional tree, and calculate the distance and normal vector angle between the n points and the normal plane of the seed point, respectively. Add the points with a distance less than a distance threshold and a normal vector angle less than a normal vector angle threshold to the first main beam bottom surface point cloud, and n is a positive integer.

[0057] In the embodiments of the present application, the calculation methods of the neighborhood curvature, the neighborhood maximum distance, and the neighborhood normal vector of the first seed point set and the neighborhood curvature, the neighborhood maximum distance, and the neighborhood normal vector of the second seed point set are the same. Taking the calculation process of the first seed point set as an example:

[0058] (1) Neighborhood curvature calculation

[0059] 1) Point cloud curvature value calculation:

[0060] For a seed point p, let its k-neighborhood point set be . By analyzing the eigenvalues and eigenvectors of the neighborhood covariance matrix, the normal vector and the curvature of the seed point p are determined, and the formulas are as follows:

[0061]

[0062]

[0063] wherein , , are the eigenvalues of the covariance matrix, is the eigenvector corresponding to the eigenvalue .

[0064] 2) Plane point screening:

[0065] First, traverse the curvature of all seed points in the first seed point set, retain the seed points with curvature lower than the curvature threshold to form a candidate seed point set .

[0066] Then, for each candidate seed point in the candidate seed point set , search its k-neighborhood point set , and count the number n of points in the k-neighborhood point set that belong to the candidate seed point set .

[0067] If the number n is not less than the first threshold, add to the first girder bottom surface point cloud.

[0068] Where the first threshold is , where is the plane coefficient, usually 0.5~1.0.

[0069] (2) Neighbor maximum distance calculation

[0070] 1) Neighbor Euclidean distance calculation:

[0071] For seed point p, define its k-neighborhood point set as , and the neighbor Euclidean distance from seed point p to neighborhood point is denoted by , that is:

[0072]

[0073] Where is the coordinate value of neighborhood point , and is the coordinate value of seed point p.

[0074] The neighbor Euclidean distance calculation is performed on all neighborhood points to obtain the neighborhood point distance set .

[0075] 2) Maximum distance constraint:

[0076] Sort the elements in the neighborhood point distance set to obtain the neighborhood maximum distance . If , the seed point p satisfies the seed point condition, and the seed point p is added to the first girder bottom surface point cloud. Where is the maximum neighborhood radius of the bridge bottom surface seed point of the bridge to be tested, which is related to the neighborhood search point number and the point cloud data density. At the same time, in order to avoid the misjudgment phenomenon of narrow and long auxiliary facilities, The value must be significantly greater than the maximum neighborhood radius R of the bridge bottom surface.

[0077] (3) Calculation of neighborhood normal vector

[0078] After calculating the neighborhood curvature and maximum neighborhood distance, only the bottom surface of the main beam remains as the seed point. However, referring to... Figure 4 The seed points obtained by CSF only cover the surface layer, leaving a large portion of the bottom point cloud uncovered. Therefore, this embodiment requires grouping points with similar attributes into the same object to achieve complete extraction of the bottom point cloud of the main beam. The bottom point cloud of the main beam is flat and smooth, meaning the change in the normal vector is uniform. At the bottom boundary, the normal vector changes. Based on these characteristics, a distance threshold from a point to the normal vector is introduced. Threshold of the angle between the normal vector and the normal vector The specific process is as follows:

[0079] 1) Search for the n nearest neighbors of each seed point in the first seed point set using k-dimensional search, and calculate the normal vector of each point.

[0080] 2) Within the search radius R, calculate the distance h between each of the n points and the normal plane of the corresponding seed point, as well as the angle between the normal vectors. ,like Figure 5 As shown. If the distance h is less than the distance threshold. And the angle between the normal vectors Less than the threshold of the angle between the normal vectors Then the seed point is added to the point cloud on the bottom surface of the first main beam.

[0081] 3) Select points in the seed point sequence as the current seed point in turn, and return to step 2) for processing until all points in the seed point sequence have been processed.

[0082] 4) Mark all points in the initial seed point and seed point sequence as classified and grouped into the same object, clear the points in the seed point sequence, and complete the extraction of the region growth point cloud.

[0083] In step 102, a sliding window is used to divide the point cloud of the bottom surface of the first main beam and the point cloud of the bottom surface of the second main beam into multiple first point cloud blocks and multiple second point cloud blocks respectively. The maximum consistent minimum distance algorithm is used to perform plane fitting on each point cloud block. The center point of each fitted plane is used as the measurement point to obtain multiple first measurement points and multiple second measurement points. Each first measurement point corresponds to a first point cloud block and each second measurement point corresponds to a second point cloud block.

[0084] Generally, the axial length of a bridge beam is much larger than its cross-sectional dimension, so the deformation mode of the bridge is usually a spatial curve. The embodiments of the present application define the X-axis along the span direction of the bridge, the Y-axis along the horizontal direction perpendicular to the span direction, and the Z-axis along the vertical upward deformation direction. Under the action of gravity, the bridge deformation is a two-dimensional curve in the XZ plane; and when considering lateral foundation slip or uneven solar radiation factors, the deformation mode will evolve into a three-dimensional curve. However, the mode can still be decomposed into two-dimensional curves in the XY plane and the XZ plane. By calculating the deformation curves in the XY and XZ planes from the point cloud, the spatial deformation mode of the bridge can be completely characterized. The XY plane deformation curve is extracted from the point cloud of the bottom or top surface of the beam, and the XZ plane deformation curve is calculated from the point cloud of the side surface of the beam.

[0085] Specifically, the embodiments of the present application use a sliding window to divide the first girder bottom surface point cloud and the second girder bottom surface point cloud into point cloud blocks with the same boundary, i.e., a plurality of first point cloud blocks and a plurality of second point cloud blocks, and then fit each point cloud block by the maximum consistent minimum distance algorithm to suppress point cloud noise. The center point of each fitted plane is taken as a measurement point, and a plurality of first measurement points and a plurality of second measurement points are obtained by window sliding.

[0086] In a possible implementation, the plurality of first point cloud blocks and the plurality of second point cloud blocks are divided from the first girder bottom surface point cloud and the second girder bottom surface point cloud by using a sliding window, and each point cloud block is fitted by a maximum consistent minimum distance algorithm to obtain a plurality of first measurement points and a plurality of second measurement points, which can include:

[0087] The first girder bottom surface point cloud and the second girder bottom surface point cloud are registered to the same coordinate system, and a plurality of first point cloud blocks and a plurality of second point cloud blocks are obtained by sliding along the span direction and the lateral direction of the bridge to be measured with a preset sliding window size and a preset step size;

[0088] The center coordinates of each point cloud block are calculated, wherein the boundary of each first point cloud block is the same as the boundary of the corresponding second point cloud block;

[0089] Each first point cloud block and each second point cloud block are fitted by a maximum consistent minimum distance algorithm to obtain a plurality of first fitted planes and a plurality of second fitted planes;

[0090] A first measurement point of each first fitted plane is obtained based on the center coordinates of each first point cloud block, and a second measurement point of each second fitted plane is obtained based on the center coordinates of each second point cloud block.

[0091] The specific execution process of the embodiments is as follows:

[0092] (1) Using a TLS scanner to scan the bridge to be measured at different times to obtain a first main girder bottom surface point cloud and a second main girder bottom surface point cloud. The scanning time of the first main girder bottom surface cloud is later than the scanning time of the second main girder bottom surface point cloud.

[0093] (2) Register the first main girder bottom surface point cloud and the second main girder bottom surface point cloud to the same coordinate system, and obtain a plurality of point cloud blocks from the same target region of the two registered main girder bottom surface point clouds, as shown in Figure 6 For example, the main girder bottom surface, the actual region needs to be manually selected according to the type of the bridge.

[0094] (3) Sliding window division rule: refer to Figure 7 , define the preset sliding window size (length l w , width h w ) and the preset step size s, slide along the span direction (X axis) and the transverse direction (Y axis) of the bridge to be measured.

[0095] Among them, the sliding window overlap strategy: if the preset step size s<l w (or s<h w ), the adjacent windows partially overlap, which can improve the density and continuity of the measurement points.

[0096] Sliding window boundary constraint: the preset sliding window size needs to meet the local plane assumption, that is, the point cloud curvature radius R>5l w .

[0097] (4) Calculate the center coordinates x i of the sliding window (i.e., the point cloud block) i as the X coordinate of the deformation measurement point i.

[0098] (5) Use the maximum consistent minimum distance algorithm to fit the point cloud blocks before and after deformation in the sliding window i respectively to obtain the fitting plane 1 (i.e., the first fitting plane) and the fitting plane 2 (i.e., the second fitting plane). Calculate the coordinates of the fitting plane 1 at x i , and calculate the vertical distance from the fitting plane 2 , then is a measurement point, and the vertical distance is as shown in . Figure 8

[0099] (6) Move the window to the next position (such as ), repeat steps (4) and (5) to obtain the value of is the measurement point of the bridge bottom surface deformation surface under the next sliding window, and all measurement points are as shown in Figure 9 .

[0100] ​In a possible implementation, the plane fitting is performed on each first point cloud block and each second point cloud block respectively by using the maximum consistent minimum distance algorithm to obtain a plurality of first fitting planes and a plurality of second fitting planes, which can include:

[0101] For each point cloud block, the following steps are performed:

[0102] m seed points in the point cloud block are obtained;

[0103] The principal component analysis method is used to preliminarily fit a plane to the m seed points, and after the preliminary fitting plane is obtained, the orthogonal distances of the remaining seed points in the point cloud block to the preliminary fitting plane are calculated respectively;

[0104] All the orthogonal distances are sorted in ascending order of distance, the seed points corresponding to the first h orthogonal distances are selected, and the principal component analysis method is used to fit a plane to the seed points corresponding to the first h orthogonal distances, and after the plane fitting, the eigenvalues of the seed points corresponding to the first h orthogonal distances are calculated respectively, and the minimum value of the h eigenvalues is added to the eigenvalue set;

[0105] It is determined whether the current iteration number reaches a preset iteration number;

[0106] If the current iteration number reaches the preset iteration number, a plane corresponding to the minimum eigenvalue in the eigenvalue set is selected, the standardized distances of all seed points in the point cloud block to the plane corresponding to the minimum eigenvalue are calculated, all seed points whose standardized distances satisfy a preset condition are selected as inliers, and the principal component analysis method is used to fit a plane to all the inliers to obtain a fitting plane corresponding to the point cloud block;

[0107] If the current iteration number does not reach the preset iteration number, the current iteration number is incremented by 1, and the step of obtaining m seed points in the point cloud block is returned to continue execution.

[0108] In a bridge three-dimensional scanning project, point cloud data is inevitably affected by multiple factors such as physical limitations of sensors, discontinuity of spatial feature boundaries, environmental occlusion, multiple reflection effects, and point cloud registration errors, and thus uniform noise distribution and outlier interference are inevitable. Although the existing mainstream plane fitting algorithm has a certain universality, its robustness is significantly insufficient for a high proportion of noise (especially abnormal point clusters with spatial aggregation characteristics). Therefore, for incomplete plane point cloud data containing high abnormal values, the maximum consistent minimum distance algorithm is used for plane fitting, which has important research value for improving the accuracy of bridge structure feature extraction.

[0109] The core of the maximum consistent minimum distance algorithm is to first find the maximum consistent normal vector of the plane point cloud, and then determine the best fitting plane by limiting the minimum distance of other points to the plane under the constraint of the normal vector.

[0110] Referring to Figure 10 As shown in the figure, the plane fitting process is as follows:

[0111] (1) In each point cloud block, m seed points are randomly selected to fit the plane corresponding to the point cloud block.

[0112] Wherein, referring to Figure 11 For a point cloud block, it is considered that the best plane should be the plane fitting of h seed points formed by the most high-reliability and best-sameness majority of high-quality points in the local neighborhood. Generally, the embodiment sets h to be between (0.5~1)k, k is the total number of seed points in the point cloud block, that is, the embodiment judges that at least half of the seed points are close to the correct plane. In order to obtain the best h seed points, m seed points need to be randomly selected from all seed points first, and m seed points are the minimum number of points required for plane fitting, that is, m=3. Using m seed points to find h seed points can greatly reduce the iteration time, because m seed points are much less than h seed points.

[0113] (2) For the above m seed points, the principal component analysis method is used to preliminarily fit the plane of the m seed points, and after obtaining the preliminary fitting plane, the orthogonal distance OD of the remaining seed points in the point cloud block to the preliminary fitting plane is calculated, referring to Figure 12 As shown in the figure, and all orthogonal distances are sorted in ascending order: .

[0114] (3) Referring to Figure 13 , the first h orthogonal distances OD corresponding to the seed points are selected, the principal component analysis method is used to fit the plane of the h seed points, and after the plane fitting, the eigenvalues of the h seed points are calculated respectively, and the minimum value is added to the feature set .

[0115] (4) Determine whether the preset iteration number is reached, if yes, go to (5); otherwise, , return to step (1).

[0116] (5) Find the plane corresponding to the minimum eigenvalue from the feature set , calculate the standardized distance of all seed points in the point cloud block to the plane, and select all seed points whose standardized distance meets the preset condition as inliers. Then, the principal component analysis method is used to fit the plane of all inliers to obtain the fitting plane corresponding to the point cloud block.

[0117] In one possible implementation, after obtaining the m seed points in the point cloud block, the method can further include:

[0118] The point set composed of m seed points is taken as a first point set, the rank of the first point set is calculated, and it is determined whether the rank of the first point set is less than m;

[0119] If the rank of the first point set is less than m, a preset number of seed points are randomly added to the first point set, and the step of acquiring m seed points in the point cloud block is continued to execute;

[0120] If the rank of the first point set is equal to m, the step of performing a preliminary plane fitting on the m seed points by using a principal component analysis method is executed.

[0121] Optionally, before the m seed points are selected for preliminary plane fitting, it is further determined whether the rank of the first point set composed of the m seed points is less than m. If yes, more seed points are randomly added to the first point set, and the determination is re-performed. If no, the preliminary plane fitting is performed on the m seed points.

[0122] In a possible implementation, the calculation process of the preset iteration number is as follows:

[0123] The preset iteration number is calculated by using Monte Carlo probability simulation.

[0124] Optionally, the m seed points are randomly selected and iterated continuously, and the preset iteration number is The Monte Carlo probability simulation is introduced, and the calculation formula is as follows:

[0125]

[0126] wherein, is an outlier rate of the point cloud block, which can be set by an engineer; is at least one seed point without outliers from data containing outliers; is an expected probability of finding the seed point without outliers from the data containing outliers; . .

[0127] For example, in the embodiment of the present application, m = 3 and . It should be noted that a higher value will increase the iteration number, but can improve the probability of the subset without outliers, and thus the iteration number is essentially a trade-off between accuracy and efficiency. For the actual outlier rate , it is unknown, and underestimation will be affected by the masking effect, and overestimation will lead to the drowning of real points. Through data experience, most points (> 50%) in the local neighborhood are inner points, and thus the actual data is assumed to be . Through the above setting, when m = 3, , , the iteration number is is 68 times.

[0128] In a possible implementation, the preset condition is that the normalized distance is not greater than the second threshold value, and all seed points satisfying the preset condition can be regarded as inliers.

[0129] For each seed point, it is determined whether the normalized distance corresponding to the seed point is not greater than the second threshold value. If the normalized distance corresponding to the seed point is not greater than the second threshold value, the seed point is determined as an inlier. If the normalized distance corresponding to the seed point is greater than the second threshold value, the seed point is determined as an outlier, and is deleted from all seed points in the point cloud block.

[0130] For the maximum consistent minimum distance algorithm, the mean and standard deviation are used to calculate the normalized distance of the point to the fitted plane in the inlier screening stage in the past algorithm, but the mean and standard deviation are very sensitive to outliers. Therefore, the median (med) and median absolute deviation (MAD) are introduced in this step, so that even if there are a large number of outliers in the data, the median will not be biased, and the inlier screening is more reliable.

[0131] After each iteration, h seed points are screened based on the minimum orthogonal distance OD of the m seed points to the fitted plane, and the eigenvalues of the h seed points are calculated. The orthogonal distance OD is calculated by the following formula:

[0132]

[0133] wherein, is a seed point, is a projection point of the seed point to the preliminary fitted plane, is a unit normal vector of the preliminary fitted plane.

[0134] In theory, the smallest eigenvalue corresponding to the plane has the maximum surface consistency (i.e., the minimum variation along the normal direction), but there are still some deviation points in the spatial plane. The present embodiment first takes this plane as a preliminary plane model, i.e., the best candidate consistent subset (MCS), and then calculates the orthogonal distance OD of all points using the plane model. The median med and the median absolute deviation MAD of the orthogonal distance OD are introduced, and the median absolute deviation MAD is:

[0135]

[0136] wherein, is a correction coefficient, which can be used to estimate the consistency of the quantity, and the value can be 1.4826.

[0137] The normalized distance of each seed point is calculated as:

[0138]

[0139] wherein, To prevent the extremely small constant of zero division, the The threshold value, i.e. the second threshold value, is set to 2.5, and The points less than the second threshold value are determined as the inner points, and the inner point data after screening is fitted again by the principal component analysis method to obtain the final fitting plane.

[0140] In step 103, the first curve surface and the second curve surface are constructed respectively based on the plurality of first measurement points and the plurality of second measurement points, and the Z-axis data at the center position of each sliding window is extracted from the first curve surface and the second curve surface respectively and then subtracted to obtain the periodic deformation of the bridge to be measured.

[0141] In the embodiment of the present application, the first curve surface is constructed based on the plurality of first measurement points, and the second curve surface is constructed based on the plurality of second measurement points. Then, the Z-axis data at the center position of the sliding window with the same boundary is extracted from the first curve surface and the second curve surface respectively by the sliding window and then subtracted to determine the deformation of the bridge to be measured.

[0142] It should be noted that when the deformation is the normal vector distance of two planes, it is suitable for beams and tower members with equal cross sections; for variable cross section members, the normal distance is converted into the vertical deformation i.e.

[0143]

[0144] wherein, is the angle between the normal of the curve surface at the measurement point and the deformation direction.

[0145] The application provides a bridge main girder structure space overall deformation detection method, first main girder bottom surface point cloud and second main girder bottom surface point cloud of a bridge to be detected are acquired, wherein the acquisition time of the first main girder bottom surface point cloud is later than the acquisition time of the second main girder bottom surface point cloud; a plurality of first point cloud blocks and a plurality of second point cloud blocks are divided from the first main girder bottom surface point cloud and the second main girder bottom surface point cloud respectively by using a sliding window, and plane fitting is performed on each point cloud block respectively by using a maximum consistent minimum distance algorithm, a center point of each fitting plane is taken as a measurement point, a plurality of first measurement points and a plurality of second measurement points are acquired, wherein each first measurement point corresponds to a first point cloud block, and each second measurement point corresponds to a second point cloud block; the plurality of first measurement points and the plurality of second measurement points are constructed into a first curved surface and a second curved surface respectively, and Z-axis data is extracted from the first curved surface and the second curved surface respectively based on the center position of each sliding window, and the difference is made, to obtain the periodic deformation of the bridge to be detected. The application can accurately extract the feature points of the main girder bottom surface by acquiring the main girder bottom surface point cloud data at different times and performing plane fitting by using the sliding window and the maximum consistent minimum distance algorithm, can effectively reduce the influence of noise and abnormal values on the measurement result, and improves the deformation detection precision; and the application can more comprehensively reflect the overall deformation of the main girder in space by constructing the first curved surface and the second curved surface and extracting the Z-axis data based on the center position of the sliding window, and can help to identify the deformation difference of the main girder at different positions, and provides an important basis for the safety evaluation and maintenance of the bridge.

[0146] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0147] The following is the device embodiment of the application, and for the details not described in detail, reference can be made to the corresponding method embodiments described above.

[0148] Figure 14 The structure schematic diagram of the bridge main girder structure space overall deformation detection device provided by the embodiments of the application is shown, only the parts related to the embodiments of the application are shown for the convenience of description, and the details are as follows:

[0149] As Figure 14 shown, the bridge main girder structure space overall deformation detection device 14 comprises:

[0150] The acquisition module 141 is configured to acquire first main girder bottom surface point cloud and second main girder bottom surface point cloud of a bridge to be detected, wherein the acquisition time of the first main girder bottom surface point cloud is later than the acquisition time of the second main girder bottom surface point cloud;

[0151] The division and fitting module 142 is configured to divide a plurality of first point cloud blocks and a plurality of second point cloud blocks from the first main beam bottom surface point cloud and the second main beam bottom surface point cloud respectively by using a sliding window, and perform plane fitting on each point cloud block by using a maximum consistent minimum distance algorithm, take a center point of each fitted plane as a measurement point, and obtain a plurality of first measurement points and a plurality of second measurement points, wherein each first measurement point corresponds to a first point cloud block, and each second measurement point corresponds to a second point cloud block.

[0152] The deformation amount determination module 143 is configured to construct a first curved surface and a second curved surface from the plurality of first measurement points and the plurality of second measurement points respectively, and extract Z-axis data from the first curved surface and the second curved surface respectively based on a center position of each sliding window, and obtain a periodic deformation amount of the bridge to be measured by performing a difference operation.

[0153] The application provides a bridge main beam structure space overall deformation detection device. The device obtains a first main beam bottom surface point cloud and a second main beam bottom surface point cloud of a bridge to be measured, wherein the first main beam bottom surface point cloud is obtained at a time later than the second main beam bottom surface point cloud. The device divides a plurality of first point cloud blocks and a plurality of second point cloud blocks from the first main beam bottom surface point cloud and the second main beam bottom surface point cloud respectively by using a sliding window, and performs plane fitting on each point cloud block by using a maximum consistent minimum distance algorithm, takes a center point of each fitted plane as a measurement point, and obtains a plurality of first measurement points and a plurality of second measurement points, wherein each first measurement point corresponds to a first point cloud block, and each second measurement point corresponds to a second point cloud block. The device constructs a first curved surface and a second curved surface from the plurality of first measurement points and the plurality of second measurement points respectively, and extracts Z-axis data from the first curved surface and the second curved surface respectively based on a center position of each sliding window, and obtains a periodic deformation amount of the bridge to be measured by performing a difference operation. The application can accurately extract feature points of the main beam bottom surface by obtaining main beam bottom surface point cloud data at different times and performing plane fitting by using a sliding window and a maximum consistent minimum distance algorithm, can effectively reduce the influence of noise and abnormal values on measurement results, and improves the accuracy of deformation detection. The application can more comprehensively reflect the overall deformation of the main beam in space by constructing the first curved surface and the second curved surface and extracting Z-axis data based on the center position of the sliding window, and is helpful to identify the deformation difference of the main beam at different positions, and provides an important basis for safety evaluation and maintenance of the bridge.

[0154] In a possible implementation, the obtaining module can be specifically configured to:

[0155] obtain a first bridge point cloud and a second bridge point cloud of a bridge to be measured, wherein the first bridge point cloud is obtained at a time later than the second bridge point cloud;

[0156] The cloth simulation filtering algorithm is used to obtain a main beam bottom surface seed point set of the first bridge point cloud and a main beam bottom surface seed point set of the second bridge point cloud, the main beam bottom surface seed point set corresponding to the first bridge point cloud is taken as the first seed point set, and the main beam bottom surface seed point set corresponding to the second bridge point cloud is taken as the second seed point set.

[0157] The neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set are calculated, the first main beam bottom surface point cloud is determined, and the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set are calculated, and the second main beam bottom surface point cloud is determined.

[0158] In a possible implementation, the obtaining module can be configured to:

[0159] The normal vector and the curvature of each seed point in the first seed point set are calculated, all seed points with a curvature less than a curvature threshold value are taken as a candidate seed point set, and for each candidate seed point in the candidate seed point set, the number of points belonging to the candidate seed point set in the K-neighborhood point set of the candidate seed point is searched, it is judged whether the number of points is not less than a first threshold value, and if the number of points is not less than the first threshold value, the candidate seed point is added to the first main beam bottom surface point cloud;

[0160] The neighborhood Euclidean distance of each seed point in the first seed point set to all neighborhood points in the K-neighborhood of the seed point is calculated respectively, and for each seed point, it is judged whether the neighborhood maximum distance in all neighborhood Euclidean distances corresponding to the seed point is less than the maximum neighborhood radius of the bridge bottom surface seed point of the bridge to be measured, and if the neighborhood maximum distance in all neighborhood Euclidean distances corresponding to the seed point is less than the maximum neighborhood radius of the bridge bottom surface seed point of the bridge to be measured, the seed point is added to the first main beam bottom surface point cloud;

[0161] For each seed point in the first seed point set, the normal vector of the seed point is calculated, and the k-dimensional tree is searched to obtain n points within a preset search radius of the seed point, the distance and the normal vector angle between the n points and the normal plane of the seed point are calculated respectively, and the points with a distance less than a distance threshold value and a normal vector angle less than a normal vector angle threshold value are added to the first main beam bottom surface point cloud, and n is a positive integer.

[0162] In a possible implementation, the dividing and fitting module can be specifically configured to:

[0163] The first main beam bottom surface point cloud and the second main beam bottom surface point cloud are registered to the same coordinate system, and a plurality of first point cloud blocks and a plurality of second point cloud blocks are obtained by sliding along the span direction and the transverse direction of the bridge to be measured with a preset sliding window size and a preset step size;

[0164] The center coordinates of each point cloud block are calculated, wherein the boundary of each first point cloud block is the same as the boundary of the corresponding second point cloud block;

[0165] fitting planes are obtained by using a maximum consistent minimum distance algorithm to fit a plane to each first point cloud block and each second point cloud block, respectively;

[0166] A first measurement point of a corresponding first fitting plane is obtained based on a center coordinate of each first point cloud block, and a second measurement point of a corresponding second fitting plane is obtained based on a center coordinate of each second point cloud block.

[0167] In a possible implementation, the dividing and fitting module can be configured to:

[0168] For each point cloud block, the following steps are performed:

[0169] m seed points in the point cloud block are obtained;

[0170] A principal component analysis method is used to preliminarily fit a plane to the m seed points, and after the preliminary fitting plane is obtained, an orthogonal distance of each remaining seed point in the point cloud block to the preliminary fitting plane is calculated.

[0171] All the orthogonal distances are sorted in ascending order of distance, h seed points corresponding to the first h orthogonal distances are selected, a principal component analysis method is used to fit a plane to the h seed points, and after the plane fitting, eigenvalues of the h seed points are calculated, and a minimum value of the h eigenvalues is added to the eigenvalue set.

[0172] It is determined whether the current iteration number reaches a preset iteration number.

[0173] If the current iteration number reaches the preset iteration number, a plane corresponding to a minimum eigenvalue in the eigenvalue set is selected, a normalized distance of each seed point in the point cloud block to the plane corresponding to the minimum eigenvalue is calculated, all seed points whose normalized distances satisfy a preset condition are regarded as inliers, a principal component analysis method is used to fit a plane to all the inliers, and a fitting plane corresponding to the point cloud block is obtained.

[0174] If the current iteration number does not reach the preset iteration number, the current iteration number is incremented by 1, and the step of obtaining m seed points in the point cloud block is performed again.

[0175] In a possible implementation, the apparatus can further include a point rank determination module, which can be configured to:

[0176] A point set composed of the m seed points is regarded as a first point set, a rank of the first point set is calculated, and it is determined whether the rank of the first point set is less than m.

[0177] If the rank of the first point set is less than m, a preset number of seed points are randomly added to the first point set, and the step of obtaining m seed points in the point cloud block is performed again.

[0178] If the rank of the first point set is equal to m, a step of performing a plane preliminary fitting on the m seed points using a principal component analysis method is performed.

[0179] In a possible implementation, the preset condition is that the distance is not greater than a second threshold value, and the dividing and fitting module can be configured to:

[0180] For each seed point, it is determined whether the normalized distance corresponding to the seed point is not greater than the second threshold value. If the normalized distance corresponding to the seed point is not greater than the second threshold value, the seed point is determined as an inlier. If the normalized distance corresponding to the seed point is greater than the second threshold value, the seed point is determined as an outlier, and is deleted from all seed points in the point cloud block.

[0181] In a possible implementation, the calculation process of the preset iteration number is as follows:

[0182] The preset iteration number is calculated using a Monte Carlo probability simulation.

[0183] Figure 15 is a schematic diagram of a terminal provided by an embodiment of the present application. As shown in the figure, the terminal 15 of this embodiment includes a processor 150, a memory 151, and a computer program 152 stored in the memory 151 and executable on the processor 150. The processor 150 implements the steps in each of the bridge girder structure spatial overall deformation detection method embodiments described above when executing the computer program 152, such as steps 101 to 103 shown in the figure. Alternatively, the processor 150 implements the functions of each module / unit in each of the device embodiments described above when executing the computer program 152, such as the functions of each module shown in the figure. Figure 15 Figure 1 Figure 14

[0184] Illustratively, the computer program 152 can be divided into one or more modules / units, which are stored in the memory 151 and executed by the processor 150 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 152 in the terminal 15. For example, the computer program 152 can be divided into the modules shown in the figure. Figure 14

[0185] The terminal 15 can be a desktop computer, a notebook, a palm computer, and a cloud server, etc. The terminal 15 can include, but is not limited to, the processor 150 and the memory 151. Those skilled in the art can understand that the terminal 15 can further include other components, which are not shown in the figure. Figure 15 ​​​​The terminal 15 is merely an example and does not limit the terminal 15, which can include more or fewer components than shown, or have components in different configurations and / or have different components, such as input / output devices, network access devices, buses, etc.

[0186] The processor 150 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0187] The memory 151 can be an internal storage unit of the terminal 15, such as a hard disk or a memory of the terminal 15. The memory 151 can also be an external storage device of the terminal 15, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 151 can include both the internal storage unit and the external storage device of the terminal 15. The memory 151 is used to store the computer program and other programs and data required by the terminal. The memory 151 can also be used to temporarily store data that has been output or will be output.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0189] In the above embodiments, the description of each embodiment is focused on, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0190] Those skilled in the art can appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0191] In the embodiments provided in the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other ways. For example, the apparatus / terminal embodiments described above are only schematic, and the division of the modules or units is only a logical function division, and there can be another division in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0192] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0193] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0194] The integrated module / unit, if implemented in the form of a software function unit and sold or used as an independent product, 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, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various bridge girder structure spatial overall deformation detection method embodiments can be implemented. 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), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0195] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting the overall deformation of a bridge girder structure space, characterized by, The method comprises the following steps: obtaining first and second main girder bottom surface point clouds of a bridge to be measured, the first main girder bottom surface point cloud being obtained later than the second main girder bottom surface point cloud; aligning the first and second main girder bottom surface point clouds to the same coordinate system, and sliding along the span direction and the transverse direction of the bridge to be measured with a preset sliding window size and a preset step size to obtain a plurality of first point cloud blocks and a plurality of second point cloud blocks respectively; calculating the center coordinates of each point cloud block, wherein the boundaries of each first point cloud block are the same as the boundaries of the corresponding second point cloud block; performing plane fitting on each first point cloud block and each second point cloud block respectively using the maximum consistent minimum distance algorithm to obtain a plurality of first fitting planes and a plurality of second fitting planes, and for each point cloud block, the following steps are performed: obtaining m seed points in the point cloud block; performing preliminary plane fitting on the m seed points using principal component analysis, then calculating the orthogonal distances of the remaining seed points in the point cloud block to the preliminary fitting plane respectively; sorting all orthogonal distances in ascending order of distance, selecting the seed points corresponding to the first h orthogonal distances, performing plane fitting on the seed points corresponding to the first h orthogonal distances using the principal component analysis, then calculating the eigenvalues of the seed points corresponding to the first h orthogonal distances respectively, and adding the minimum value of the h eigenvalues to the eigenvalue set; if the current iteration number reaches the preset iteration number, selecting the plane corresponding to the minimum eigenvalue in the eigenvalue set, calculating the standardized distances of all seed points in the point cloud block to the plane corresponding to the minimum eigenvalue, and selecting all seed points with standardized distances satisfying a preset condition as inliers, and performing plane fitting on all inliers using principal component analysis to obtain the fitting plane corresponding to the point cloud block; if the current iteration number does not reach the preset iteration number, increasing the current iteration number by 1 and returning to the step of obtaining m seed points in the point cloud block for further execution; obtaining first measurement points of the corresponding first fitting plane based on the center coordinates of each first point cloud block, and obtaining second measurement points of the corresponding second fitting plane based on the center coordinates of each second point cloud block; constructing a first curve and a second curve from the plurality of first measurement points and the plurality of second measurement points respectively, and extracting Z-axis data from the first curve and the second curve respectively based on the center position of each sliding window and then performing a difference operation to obtain the periodic deformation of the bridge to be measured.

2. The bridge girder structure space integral deformation detection method according to claim 1, characterized by, The method comprises the following steps: obtaining first and second bridge point clouds of the bridge to be measured, wherein the first bridge point cloud is obtained later than the second bridge point cloud; obtaining main girder bottom surface seed point sets of the first bridge point cloud and the second bridge point cloud using cloth simulation filtering algorithm, and taking the main girder bottom surface seed point set corresponding to the first bridge point cloud as a first seed point set and taking the main girder bottom surface seed point set corresponding to the second bridge point cloud as a second seed point set; The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud.

3. The bridge girder structure space integral deformation detection method according to claim 2, characterized by, The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud.

4. The bridge girder structure space integral deformation detection method according to claim 1, characterized by, The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud.

5. The method of claim 1, wherein The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud.

6. The bridge girder structure space integral deformation detection method according to claim 1, characterized by, The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, and determining the second main beam bottom surface point cloud. The method comprises the following steps: calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the first seed point set, determining the first main beam bottom surface point cloud, and calculating the neighborhood curvature, the neighborhood maximum distance and the neighborhood normal vector of the second seed point set, 7. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the bridge girder structure space overall deformation detection method according to any one of claims 1 to 6 when the computer program is executed.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the steps of the bridge girder structure space overall deformation detection method according to any one of claims 1 to 6.

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