A method and device for monitoring bridge deformation

Abstract

The present invention relates to a method and device for monitoring bridge deformation. The method includes obtaining deformation candidate points and spatial position information by using time-series SAR satellite images and digital elevation models; registering the deformation candidate points with three-dimensional laser point clouds, screening according to the three-dimensional laser point clouds and spatial position information, performing deformation calculation on the obtained bridge deformation points, and extracting key points according to the three-dimensional laser point clouds and deformation data to obtain bridge deformation key points; removing and vertically decomposing deformation information from the deformation data of the bridge deformation key points, and then performing deformation risk assessment to obtain a deformation monitoring risk evaluation result; the present invention performs overall deformation monitoring of the bridge through time-series SAR satellite images, combines InSAR deformation points with three-dimensional laser point clouds to make the bridge deformation points more accurate, and performs risk assessment on the bridge deformation key points, taking into account the deformation characteristics of different parts of the bridge, so that the deformation risk evaluation result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge deformation monitoring, and particularly to a bridge deformation monitoring method and device. Background Art

[0002] In the highway traffic network, bridges play a role in connecting traffic lines and are the throat of traffic, having important significance in aspects such as economy and social life. During the service period of bridges, affected by various adverse factors, such as severe overloading and external impact on bridge piers, problems such as bridge structure aging and structural damage may occur. When the damage accumulates to a certain extent, collapse may occur. Therefore, it is of great significance to monitor the deformation of in-service bridges, especially those that have been in service for a long time.

[0003] Traditional bridge deformation monitoring methods are to set up points manually and use total stations, levels, GPS, etc. for conventional geodetic surveys to obtain the three-dimensional coordinates of the monitoring points. Although traditional methods are widely used in practical applications, they also have problems such as large field workload, being restricted by weather, low efficiency, and only being able to achieve local single-point monitoring and unable to conduct large-scale planar monitoring of the entire bridge.

[0004] Therefore, there is an urgent need to propose a bridge deformation monitoring method and device to solve the technical problems in the existing bridge deformation monitoring methods, such as being affected by external factors, only being able to achieve local single-point monitoring, and unable to conduct large-scale planar monitoring of the entire bridge. Summary of the Invention

[0005] In view of this, it is necessary to provide a bridge deformation monitoring method and device to solve the technical problems in the existing bridge deformation monitoring methods, such as being affected by external factors, only being able to achieve local single-point monitoring, and unable to conduct large-scale planar monitoring of the entire bridge.

[0006] To solve the above problems, the present invention provides a bridge deformation monitoring method, including:

[0007] Obtain time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and the digital elevation models to obtain deformation candidate points and spatial position information;

[0008] Register the deformation candidate points with the three-dimensional laser point clouds to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point clouds and the spatial position information to obtain bridge deformation points;

[0009] Perform deformation calculation on the bridge deformation points to obtain the deformation data of the bridge deformation points, and extract key points based on the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points;

[0010] After performing deformation elimination and vertical decomposition on the deformation data of the bridge deformation key points, conduct deformation risk assessment to obtain the deformation monitoring risk assessment result.

[0011] In a possible implementation manner, the performing InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and the digital elevation model to obtain deformation candidate points and spatial position information includes:

[0012] Extract the bridge PS points and bridge DS points in the time-series SAR satellite images based on the amplitude deviation and FaSHP similarity test method, and determine the bridge PS points and the bridge DS points as deformation candidate points;

[0013] Perform relative height calculation on the triangular network constructed by the deformation candidate points based on the periodogram method to obtain the absolute height error of the deformation candidate points;

[0014] Add the absolute height error to the digital elevation model, and perform geocoding on the deformation candidate points through the digital elevation model to obtain spatial position information.

[0015] In a possible implementation manner, the registering the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points includes:

[0016] Construct a KD tree according to the deformation candidate points and the three-dimensional laser point cloud;

[0017] Determine the initial corresponding feature point pairs in the deformation candidate points and the three-dimensional laser points according to the fast point feature histogram;

[0018] Screen and perform matrix transformation on the initial corresponding feature point pairs according to the RANSAC algorithm to obtain the initial rigid body transformation matrix;

[0019] Perform iterative registration on the deformation candidate points and the three-dimensional laser points in the initial rigid body transformation matrix according to the KD tree to obtain the final transformation matrix, and determine the deformation candidate points in the final transformation matrix as target deformation candidate points.

[0020] In a possible implementation manner, the screening the target deformation candidate points according to the three-dimensional laser point cloud and the spatial position information to obtain bridge deformation points includes:

[0021] Determine the nearest laser point in the three-dimensional laser point cloud according to the target deformation candidate point and the spatial position information, and determine the adjacent laser points of the nearest laser point;

[0022] Determine a local plane according to the nearest laser point and the adjacent laser points, and determine the distance of the target deformation candidate point according to the local plane;

[0023] Remove the candidate points in the target deformation candidate points whose distances are greater than a preset threshold to obtain the remaining deformation candidate points;

[0024] Remove noise points from the remaining deformation candidate points based on the DBSCAN algorithm to obtain bridge deformation points.

[0025] In a possible implementation manner, the deformation calculation is performed on the bridge deformation points to obtain the deformation data of the bridge deformation points, and key points are extracted according to the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points, including:

[0026] Perform deformation speed calculation on the triangular mesh constructed by the bridge deformation points based on the periodogram method to obtain the deformation rate of the bridge deformation points;

[0027] Perform phase separation on the bridge deformation points based on the spatio-temporal filtering method to obtain a deformation time series, and obtain deformation data according to the deformation rate and the deformation time series;

[0028] Extract different types of key points according to the three-dimensional laser point cloud and all the deformation data in the bridge as a whole to obtain bridge deformation key points of each type.

[0029] In a possible implementation manner, after performing deformation rejection and vertical decomposition on the deformation data of the bridge deformation key points, perform deformation risk assessment to obtain a deformation monitoring risk evaluation result, including:

[0030] Perform periodic deformation rejection and vertical decomposition on the deformation time series of the bridge deformation key points based on empirical mode decomposition and the deformation rate to obtain bridge deformation data;

[0031] Perform overall risk assessment according to the bridge deformation data and types of the bridge deformation key points to obtain a risk level;

[0032] Determine the deformation monitoring risk evaluation result of the bridge as a whole according to the risk level.

[0033] In a possible implementation manner, the performing periodic deformation rejection and vertical decomposition on the deformation time series of the bridge deformation key points based on empirical mode decomposition and the deformation rate to obtain bridge deformation data includes:

[0034] Decompose the deformation time series of the key bridge deformation points based on the empirical mode decomposition to obtain a plurality of IMF components and a residual term;

[0035] Analyze the plurality of IMF components according to a preset temperature cycle frequency to obtain periodic IMF components;

[0036] After removing the periodic IMF components from the plurality of IMF components, reconstruct the remaining IMF components to obtain initial bridge deformation data;

[0037] Perform vertical displacement decomposition on the initial bridge deformation data according to the deformation rate to obtain a vertical deformation rate, and determine the vertical deformation rate as the bridge deformation data.

[0038] In a possible implementation manner, the overall risk assessment based on the bridge deformation data and type of the key bridge deformation points to obtain a risk level includes:

[0039] Determine the target key bridge deformation points of each type according to the type of the key bridge deformation points;

[0040] Obtain the overall average deformation rate of each type according to the target key bridge deformation points and the corresponding vertical deformation rate;

[0041] Perform an overall risk assessment on the overall average deformation rate according to a preset level division to obtain the risk level of each type.

[0042] In a possible implementation manner, before registering the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, it further includes:

[0043] Determine the incident angle and the ground distance angle according to the time-series SAR satellite image;

[0044] Perform rough correction calculation on the incident angle, the ground distance angle, and the spatial position information according to a preset rough correction model to obtain the deformed candidate points after rough correction.

[0045] On the other hand, the present invention also provides a bridge deformation monitoring device, including:

[0046] A candidate point extraction module, configured to obtain a time-series SAR satellite image, a three-dimensional laser point cloud, and a digital elevation model covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite image and the digital elevation model to obtain deformation candidate points and spatial position information;

[0047] The deformation point determination module is used to register the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point cloud and the spatial position information to obtain bridge deformation points;

[0048] The key point extraction module is used to perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extract key points according to the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points;

[0049] The result determination module is used to perform deformation risk assessment after deformation rejection and vertical decomposition of the deformation data of the bridge deformation key points to obtain a deformation monitoring risk evaluation result.

[0050] The beneficial effects of the present invention are as follows: obtaining time-series SAR satellite images, three-dimensional laser point clouds and digital elevation models covering the bridge area, performing InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and digital elevation models to obtain deformation candidate points and spatial position information; registering the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, and screening the target deformation candidate points according to the three-dimensional laser point cloud and the spatial position information to obtain bridge deformation points; performing deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extracting key points according to the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points; performing deformation risk assessment after deformation rejection and vertical decomposition of the deformation data of the bridge deformation key points to obtain a deformation monitoring risk evaluation result; the present invention can perform overall detection of the bridge through the time-series SAR satellite images of the entire bridge, and through the combination of InSAR deformation points and three-dimensional laser point clouds, make the spatial position information of the bridge deformation points more accurate, and through risk assessment of the bridge deformation key points, consider the deformation characteristics of different parts of the bridge, making the deformation monitoring and risk evaluation results more accurate, thereby achieving the purpose of large-scale monitoring of the entire bridge. Description of the Drawings

[0051] Figure 1 It is a schematic flowchart of an embodiment of the bridge deformation monitoring method provided by the present invention;

[0052] Figure 2 For the present invention Figure 1 It is a schematic flowchart of an embodiment of step S101 in the present invention;

[0053] Figure 3 For the present invention Figure 1 It is a schematic flowchart of an embodiment of step S102 in the present invention;

[0054] Figure 4 For the present invention Figure 1Another schematic flow diagram of step S102 in the present invention;

[0055] Figure 5 For the present invention Figure 1 A schematic flow diagram of an embodiment of step S103 in the present invention;

[0056] Figure 6 For the present invention Figure 1 A schematic flow diagram of an embodiment of step S104 in the present invention;

[0057] Figure 7 For the present invention Figure 6 A schematic flow diagram of an embodiment of step S601 in the present invention;

[0058] Figure 8 For the present invention Figure 6 A schematic flow diagram of an embodiment of step S602 in the present invention;

[0059] Figure 9 A schematic structural diagram of an embodiment of the bridge deformation monitoring device provided by the present invention;

[0060] Figure 10 A schematic structural diagram of an embodiment of the electronic device provided by the present invention. Detailed implementation manners

[0061] The following will specifically describe the preferred embodiments of the present invention with reference to the accompanying drawings. The accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0062] As Figure 1 shown, a specific embodiment of the present invention discloses a bridge deformation monitoring method, including:

[0063] S101. Obtain the time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and digital elevation models, and obtain deformation candidate points and spatial position information;

[0064] S102. Register the deformation candidate points with the three-dimensional laser point clouds to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point clouds and spatial position information to obtain bridge deformation points;

[0065] S103. Perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extract key points according to the three-dimensional laser point clouds and the deformation data to obtain bridge deformation key points;

[0066] S104. After performing deformation rejection and vertical decomposition on the deformation data of the key points of the bridge deformation, perform a deformation risk assessment to obtain the deformation monitoring risk evaluation result.

[0067] It should be understood that: The embodiments of the present invention can be applied to a bridge detection system. The bridge detection system can be connected to a SAR satellite, lidar, and an external DEM. DEM (Digital Elevation Model), that is, digital elevation model, is a digital simulation of the ground terrain through limited terrain elevation data. The bridge detection system can receive time-series SAR satellite images and three-dimensional lidar point clouds. The bridge detection system can be a software system running on a terminal device. The terminal device can be a server, a tablet computer, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a mobile phone, or other terminal devices. The specific type of the terminal device is not limited in the embodiments of the present application.

[0068] In a specific embodiment of the present invention, the time-series SAR satellite image is a time-series SAR satellite image covering the bridge area obtained by a SAR satellite, and the three-dimensional lidar point cloud is the point cloud data of the bridge obtained by lidar. The time-series SAR satellite image is a long-time series data of the entire bridge. A digital elevation model, that is, DEM, can also be set. Then, the amplitude deviation and the FaSHP similarity test method can be used to perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite image and the digital elevation model, respectively, to obtain deformation candidate points and spatial position information. Then, the deformation candidate points are registered with the three-dimensional lidar point cloud to obtain target deformation candidate points, and the target deformation candidate points are screened according to the three-dimensional lidar point cloud and the spatial position information to remove deformation abnormal points, and more accurate bridge deformation points can be obtained. Then, deformation calculation is performed on the bridge deformation points to obtain the deformation data of the bridge deformation points. Key point extraction is performed according to the three-dimensional lidar point cloud and the deformation data to obtain different types of bridge deformation key points. Then, weekly deformation rejection and vertical decomposition are performed on the deformation data of the bridge deformation key points, and then deformation risk assessment is performed based on the key points to obtain the deformation monitoring risk evaluation result.

[0069] Compared with the prior art, the present embodiment provides a method for obtaining time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering a bridge area, performing InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and digital elevation models to obtain deformation candidate points and spatial position information; registering the deformation candidate points with the three-dimensional laser point clouds to obtain target deformation candidate points, and screening the target deformation candidate points according to the three-dimensional laser point clouds and spatial position information to obtain bridge deformation points; performing deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extracting key deformation points according to the three-dimensional laser point clouds and deformation data; after performing deformation rejection and vertical decomposition on the deformation data of the key bridge deformation points, performing deformation risk assessment to obtain a deformation monitoring risk evaluation result; the present invention can perform overall detection of the bridge through the time-series SAR satellite images of the entire bridge, and through the combination of InSAR deformation points and three-dimensional laser point clouds, make the spatial position information of the bridge deformation points more accurate, and through risk assessment of the key bridge deformation points, considering the deformation characteristics of different parts of the bridge, make the deformation monitoring and risk evaluation results more accurate, thereby achieving the purpose of large-scale monitoring of the entire bridge.

[0070] In some embodiments of the present invention, as Figure 2 shown, step S101 includes:

[0071] S201. Extract bridge PS points and bridge DS points in the time-series SAR satellite images based on the amplitude deviation and FaSHP similarity test method, and determine the bridge PS points and bridge DS points as deformation candidate points;

[0072] S202. Perform relative height calculation on the triangular network constructed by the deformation candidate points based on the periodogram method to obtain the absolute height error of the deformation candidate points;

[0073] S203. Add the absolute height error to the digital elevation model, and perform geocoding on the deformation candidate points through the digital elevation model to obtain spatial position information.

[0074] In a specific embodiment of the present invention, the time series InSAR technology can be used to obtain the complete deformation of the bridge, and deformation points that completely cover the key positions of the bridge need to be obtained. To this end, based on the amplitude deviation and FaSHP similarity test method, PS points and DS points of the bridge are extracted, and then deformation candidate points of the bridge are obtained; combined with external DEM information, the three-dimensional spatial position information of the deformation candidate points is obtained. The specific process can be as follows: Select the master image through the time series SAR satellite image, generate the differential interferogram, and use the ratio of the amplitude variance to the amplitude mean of the same pixel in the time dimension to identify the pixels with relatively stable amplitude deviation as PS candidate points. The time coherence coefficient is used to evaluate the phase stability of the PS candidate points, and the points with high phase stability are used as the PS points of the bridge. The FaSHP similarity test can be used to identify the homogeneous pixels in the time series SAR satellite image, and then the optimal phase of the homogeneous pixels is obtained by using the maximum likelihood optimization estimation, and the time coherence index is used to evaluate the quality of the phase optimization. The homogeneous pixels with high quality are used as the DS points of the bridge. Then, the PS points and DS points of the bridge can be used as the deformation candidate points of the bridge. Delaunay triangulation can be performed on the deformation candidate points of the bridge. On this basis, the relative height of the arc segment is calculated by using the periodogram method, and the absolute height error of the deformation candidate points is obtained by using the least square method relative to the same reference point in the area. The estimated value of the absolute height error of the deformation candidate points is added to the coordinates of the external absolute height error, and geocoding is performed on each deformation candidate point by using the satellite orbit parameters and the reference sphere, so as to obtain the three-dimensional coordinates of the deformation candidate points, that is, the spatial position information.

[0075] In some embodiments of the present invention, before step S102, it further includes:

[0076] According to the time series SAR satellite image, determine the incident angle and the ground distance angle;

[0077] Perform rough correction calculation on the incident angle, the ground distance angle and the spatial position information according to the preset rough correction model to obtain the deformation candidate points after rough correction.

[0078] In a specific embodiment of the present invention, since there is a deviation between the deformation candidate points of the bridge and the spatial position of the laser point cloud, in order to further utilize the obtained high-precision three-dimensional laser point cloud data of the bridge, it is necessary to accurately match the deformation candidate points with the three-dimensional laser point cloud, so as to realize the fine correction of the spatial position of the deformation candidate points. Specifically: Geocoding can realize the projection from the radar coordinate to the WGS84 coordinate, but the elevation of the structure will cause an offset between the geocoded ground coordinates of the point target and the real position, which needs to be further corrected. If the spatial position information of the deformation candidate points of the bridge obtained through geocoding is ( lon1, lat1, h), the incident angle, the ground distance angle between the ground distance direction and the east direction can be determined through the time-series SAR satellite image, and then the incident angle, the ground distance angle and the spatial position information can be input into the preset rough correction model to obtain the deformed candidate points of the corrected bridge ( lon2, lat2, h ), and the calculation of the preset rough correction model is as shown in formulas (1) and (2):

[0079] (1)

[0080] (2)

[0081] In the formula, is the distance ratio after geocoding, is the incident angle of the SAR image, is the ground distance angle between the ground distance direction and the east direction.

[0082] In some embodiments of the present invention, as Figure 3 shown, step S102 includes:

[0083] S301. Construct a KD tree according to the deformed candidate points and the 3D laser point cloud;

[0084] S302. Determine the initial corresponding feature point pairs in the deformed candidate points and the 3D laser points according to the fast point feature histogram;

[0085] S303. Screen and perform matrix transformation on the initial corresponding feature point pairs according to the RANSAC algorithm to obtain the initial rigid body transformation matrix;

[0086] S304. Perform iterative registration on the deformed candidate points and the 3D laser points in the initial rigid body transformation matrix according to the KD tree to obtain the final transformation matrix, and determine the initial deformed candidate points in the final transformation matrix as the target deformed candidate points.

[0087] In the specific embodiments of the present invention, the density of the 3D laser point cloud is relatively high, while the density of the bridge deformation candidate points is relatively low. To ensure high-precision registration between the two, an improved ICP registration algorithm is used to achieve accurate registration of the low-density deformation candidate points and the high-density 3D laser points, so as to obtain more accurate deformation candidate points in terms of spatial position. The specific steps are as follows: Establish a KD tree: Construct a KD tree for the low-density deformation candidate points and the high-density 3D laser point cloud to improve the efficiency of nearest neighbor point search. Initial registration: Use the fast point feature histogram method to perform on the deformed candidate point cloud Among them, find the initial corresponding feature point pairs, and use the RANSAC algorithm to remove the incorrect matching point pairs; on this basis, use methods such as the least squares method to calculate the initial rigid body transformation matrix, so as to preliminarily register the deformed candidate point cloud and the 3D laser point cloud. Iterative registration: ① Using the constructed KD tree, starting from each point in the low-density deformed candidate point cloud calculate the average distance of the neighboring points of . If the average distance is large, increase the search distance of the neighboring points; if it is small, decrease the search distance, and then search for the nearest point of pi in the high-density 3D laser point cloud ; at the same time, starting from each in the 3D laser point cloud qi , search for the nearest point in , and record all the above corresponding point pairs. ② Screen the corresponding point pairs obtained by the two-way search, calculate the spatial distance between the corresponding points, and remove the corresponding point pairs whose distance exceeds the set threshold. ③ Using the obtained corresponding point pairs, calculate the transformation matrices and to make the error function , where , the weight , where , and and respectively represent the local densities of the corresponding points and . ④ Repeat the above process until is less than the set threshold or the maximum number of iterations is reached, so as to obtain the final transformation matrix, and the deformed candidate points in the final transformation matrix can be determined as the target deformed candidate points, realizing the precise registration of the low-density deformed candidate point cloud and the high-density 3D laser point cloud. In some embodiments of the present invention, as

[0088] shown, step S102 further includes: Figure 4 S401. According to the target deformed candidate points and spatial position information, determine the nearest laser point in the 3D laser point cloud, and determine the adjacent laser points of the nearest laser point;

[0089] S402. According to the nearest laser point and the adjacent laser points, determine the local plane, and determine the distance of the target deformed candidate points according to the local plane;

[0090] S403. Remove the candidate points in the target deformed candidate points whose distance is greater than the preset threshold to obtain the retained deformed candidate points;

[0091]

[0092] ​S404. Remove noise points from the remaining deformation candidate points based on the DBSCAN algorithm to obtain bridge deformation points.

[0093] In a specific embodiment of the present invention, based on the precise registration of bridge deformation candidate points and three-dimensional laser point clouds, the high-precision three-dimensional laser point cloud data is used to further screen the bridge deformation candidate points, and a screening rule for bridge deformation points is established, so as to obtain more accurate bridge deformation points to support subsequent high-precision bridge deformation monitoring and risk assessment. For each deformation point among the target deformation candidate points I , the spatial coordinates of the spatial position information are , search for the nearest laser point of this deformation point in the three-dimensional laser point cloud. By setting the search radius, the adjacent laser points of the nearest laser point can be obtained. On this basis, fit the obtained three-dimensional laser points into a local plane, and the distance from the deformation point to the laser point is calculated as shown in formula (3):

[0094] (3)

[0095] In the formula, are the parameters of the fitting equation of the local plane.

[0096] By setting appropriate preset thresholds, the target deformation candidate points that are far from the laser points on the bridge surface with distances greater than the preset thresholds are judged as non-bridge area deformation points, so as to eliminate the target deformation candidate points outside the bridge area and obtain the remaining deformation candidate points. Use the DBSCAN algorithm to perform local clustering on the above-mentioned bridge three-dimensional deformation points to further remove noise points and obtain more accurate bridge deformation points. The specific steps are as follows: According to the density, distribution, etc. of the three-dimensional laser pair cloud of the deformation candidate points, set parameters such as the minimum radius and the minimum number of points of the DBSCAN algorithm; use the selected and parameters to apply the DBSCAN algorithm to the remaining deformation candidate points, and mark the remaining deformation candidate points as core points (there are at least points within its range), border points (within the range of a certain core point, but there are not enough points within its own range), and noise points (neither core points nor border points). The remaining deformation candidate points marked as noise do not participate in subsequent processing. Calculate the standard deviation of the elevation of each clustering cluster, traverse each remaining deformation candidate point within the corresponding clustering cluster, and mark the remaining deformation candidate points with elevation values exceeding twice the standard deviation as noise points and do not participate in subsequent processing. Thus, the bridge deformation points after removing noise points are obtained.

[0097] In some embodiments of the present invention, such asFigure 5 As shown in the figure, step S103 includes:

[0098] S501. Calculate the deformation speed of the triangular network constructed for the bridge deformation points based on the periodogram method to obtain the deformation rate of the bridge deformation points;

[0099] S502. Perform phase separation on the bridge deformation points based on the spatio-temporal filtering method to obtain the deformation time series, and obtain the deformation data according to the deformation rate and the deformation time series;

[0100] S503. Extract different types of key points according to the three-dimensional laser point cloud and all the deformation data in the bridge as a whole to obtain the bridge deformation key points of each type.

[0101] In a specific embodiment of the present invention, the obtained bridge deformation points are solved to calculate the deformation data of the corresponding points; combining the high-precision three-dimensional laser point cloud data of the bridge with the overall deformation information of the bridge, the bridge deformation key points are identified; specifically: based on the obtained bridge deformation points above, re-perform Delaunay triangulation, and on this basis, use the periodogram method to solve the deformation speed of the arc segment, and relative to the same reference point within the region, use the least square method to obtain the deformation rate of the bridge deformation points; then further adopt the spatio-temporal filtering method to obtain the residual phase diagram, separate the non-linear deformation, atmospheric delay and orbit error phases, so as to obtain the deformation time series of the bridge deformation points superimposed with linear deformation and non-linear deformation, and the deformation data can include the deformation rate and the deformation time series. The factors controlling the deformation of each part of the bridge are different, and the deformation magnitude and characteristics of different bridge parts have different effects on the overall deformation risk of the bridge. Therefore, based on the three-dimensional laser point cloud data and all the deformation data of the bridge as a whole, from the obtained bridge deformation points above, extract the following several types of deformation points as the bridge deformation key points, then the bridge deformation key points of each type can be obtained, specifically as follows:

[0102] A. Pier and abutment deformation key points: reflecting the overall settlement of the bridge, use the three-dimensional laser point cloud data to obtain the range of the bridge piers and abutments, and then obtain the deformation points within the range of the bridge piers and abutments;

[0103] B. Mid-span deformation key points: reflecting the deformation of the main girder, use the three-dimensional laser point cloud data to obtain the range of the bridge mid-span, and then obtain the deformation points within the mid-span range;

[0104] C. Tower and arch deformation key points: reflecting the deformation of the main load-bearing structure of the bridge, use the three-dimensional laser point cloud data to obtain the range of the bridge tower and arch, and then obtain the deformation points within the range of the tower and arch;

[0105] D. InSAR deformation aggregation points: Based on the spatial distribution density of the deformation points, select the deformation points in the range with a higher distribution density as the key points;

[0106] E. InSAR deformation risk points: Based on the deformation magnitude of the deformation points, select the points with larger bridge deformation as the key bridge deformation points.

[0107] In some embodiments of the present invention, as Figure 6 shown, step S104 includes:

[0108] S601. Perform periodic deformation removal and vertical decomposition on the deformation time series of the bridge deformation key points based on empirical mode decomposition and deformation rate to obtain bridge deformation data;

[0109] S602. Conduct an overall risk assessment based on the bridge deformation data and types of the bridge deformation key points to obtain a risk level;

[0110] S603. Determine the deformation monitoring risk evaluation result of the overall bridge according to the risk level.

[0111] In a specific embodiment of the present invention, the bridge is often affected by seasonal temperature changes, and the bridge deformation information obtained by InSAR has periodic deformation. Therefore, based on the empirical mode decomposition (EMD) method, the deformation time series data of the bridge deformation key points obtained are processed to remove the periodic deformation information caused by factors such as temperature, obtain high-precision deformation time series data directly reflecting the bridge state, and directly decompose the obtained deformation into vertical displacement that can better reflect the bridge deformation state.

[0112] In some embodiments of the present invention, as Figure 7 shown, step S601 includes:

[0113] S701. Decompose the deformation time series of the bridge deformation key points based on empirical mode decomposition to obtain multiple IMF components and a residual term;

[0114] S702. Analyze the multiple IMF components according to the preset temperature cycle frequency to obtain periodic IMF components;

[0115] S703. Reconstruct the remaining IMF components after removing the periodic IMF components from the multiple IMF components to obtain initial bridge deformation data;

[0116] S704. Perform vertical displacement decomposition on the initial bridge deformation data according to the deformation rate to obtain a vertical deformation rate, and determine the vertical deformation rate as the bridge deformation data.

[0117] In a specific embodiment of the present invention, the empirical mode decomposition (EMD) algorithm is used to decompose the deformation time series of the key points of the bridge deformation, and multiple intrinsic mode function (IMF) components and a residual term can be obtained. Each IMF component represents the fluctuation components of different frequency scales in the deformation time series; the periodic deformation caused by temperature usually exhibits a specific periodic pattern. According to the characteristics of the local temperature change, the periodic frequency related to temperature is determined. By analyzing the time-frequency characteristics of each IMF component, the periodic IMF component related to temperature change is identified; the identified periodic IMF component related to temperature is removed from all IMF components, and the remaining IMF components are reconstructed, so as to obtain the initial bridge deformation data after removing the temperature periodic deformation. The longitudinal displacement of the bridge is often periodic deformation, which has been removed through the above steps; the lateral displacement of the bridge is generally much smaller than the vertical displacement of the bridge, and the deformation in the line-of-sight (LOS) direction generated by the same lateral displacement is also much smaller than the deformation in the LOS direction generated by the vertical displacement; therefore, in the decomposition of the vertical displacement of the bridge, the lateral displacement of the bridge can be ignored, and combined with the imaging azimuth of the SAR satellite, the bridge deformation can be further decomposed into the vertical deformation rate that can better reflect the bridge deformation condition. The vertical deformation rate can be determined as the bridge deformation data, and the calculation is shown in formula (4):

[0118] (4)

[0119] In the formula, is the deformation rate of the initial bridge deformation data, is the incident angle of the SAR satellite.

[0120] In some embodiments of the present invention, as Figure 8 shown, step S602 includes:

[0121] S801. Determine the target bridge deformation key points of each type according to the type of the bridge deformation key points;

[0122] S802. Obtain the overall average deformation rate of each type according to the target bridge deformation key points and the corresponding vertical deformation rate;

[0123] S803. Conduct an overall risk assessment on the overall average deformation rate according to the preset level division to obtain the risk level of each type.

[0124] In a specific embodiment of the present invention, based on the deformation information of the bridge deformation key points obtained above, different types of bridge deformation key points are divided into deformation risk levels from level 1 to level 10 in combination with the actual condition of the bridge, and by setting different weights, the overall risk evaluation of bridge InSAR deformation is realized. The specific process is as follows: Obtaining the overall deformation information of different types of bridge deformation key points: According to the deformation information of the processed bridge key deformation points above, obtain the number and deformation rate of different types of bridge deformation key points, and then calculate the overall average deformation rate of different types of bridge deformation key points. The calculation is as shown in formula (5):

[0125] (5)

[0126] In the formula, 、 are respectively the average rate and the number of deformation points of the i th type of bridge deformation key points, is the deformation rate of the j th point in the

[0127] th type of bridge deformation key points.

[0128] Risk level division of different types of bridge deformation key points: Combining the actual data, operation condition of the bridge and relevant standards such as "Highway Bridge Technical Condition Assessment Standard", according to the average deformation rate of each type of bridge deformation points, the preset level division can be to divide the deformation risk levels of each type of bridge key deformation points according to the risk level standard from 1 to 10. Thus, the risk assessment can be carried out on the overall average deformation rate calculated for each type according to the risk level, and then the risk level of each type can be obtained. S Furthermore, by setting different weights for different types of bridge deformation key points, the overall risk level of the bridge can be calculated by means of linear weighting

[0129] (6)

[0130] In the formula, K is the type of bridge deformation key points, are respectively the risk levels of the deformation key points of the i th type of bridge, is the corresponding weight, and . Then, the overall deformation monitoring risk evaluation result of the bridge can be determined through S

[0131] ​In the embodiment of the present invention, by combining the time-series InSAR and LiDAR technologies, the obtained bridge deformation points are more accurate and their spatial positions are more precise, improving the accuracy of bridge InSAR deformation monitoring. The constructed risk assessment model based on the key deformation points of the bridge fully considers the deformation characteristics of different parts of the bridge, improving the accuracy of bridge deformation risk assessment based on InSAR technology and having stronger practicability.

[0132] To better implement the bridge deformation monitoring method in the embodiment of the present invention, correspondingly, based on the bridge deformation monitoring method, the embodiment of the present invention also provides a bridge deformation monitoring device, as Figure 9 shown. The bridge deformation monitoring device 900 includes:

[0133] A candidate point extraction module 901, configured to obtain time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and digital elevation models, and obtain deformation candidate points and spatial position information;

[0134] A deformation point determination module 902, configured to register the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point cloud and spatial position information to obtain bridge deformation points;

[0135] A key point extraction module 903, configured to perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extract key deformation points of the bridge according to the three-dimensional laser point cloud and the deformation data;

[0136] A result determination module 904, configured to perform deformation risk assessment after deformation rejection and vertical decomposition of the deformation data of the bridge deformation key points to obtain a deformation monitoring risk assessment result.

[0137] The above-mentioned bridge deformation monitoring device 900 provided in the above embodiment can implement the technical solutions described in the above-mentioned bridge deformation monitoring method embodiment. The specific implementation principles of the above-mentioned modules or units can be referred to the corresponding content in the above-mentioned bridge deformation monitoring method embodiment, and will not be elaborated here.

[0138] As Figure 10 shown, the present invention also correspondingly provides an electronic device 1000. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Figure 10 Only some components of the electronic device 1000 are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0139] The memory 1002 can be an internal storage unit of the electronic device 1000 in some embodiments, such as the hard disk or memory of the electronic device 1000. The memory 1002 can also be an external storage device of the electronic device 1000 in other embodiments, such as a plug-in hard disk equipped on the electronic device 1000, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0140] Furthermore, the memory 1002 can include both the internal storage unit of the electronic device 1000 and the external storage device. The memory 1002 is used to store the application software installed on the electronic device 1000 and various types of data.

[0141] The processor 1001 can be a Central Processing Unit (CPU), a microprocessor or other data processing chips in some embodiments, and is used to run the program code stored in the memory 1002 or process data, such as the bridge deformation monitoring method in the present invention.

[0142] The display 1003 can be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. in some embodiments. The display 1003 is used to display the information of the electronic device 1000 and to display a visual user interface. The components 1001-1003 of the electronic device 1000 communicate with each other through a system bus.

[0143] In some embodiments of the present invention, when the processor 1001 executes the bridge deformation monitoring program in the memory 1002, the following steps can be implemented:

[0144] Obtain time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and digital elevation models to obtain deformation candidate points and spatial position information;

[0145] Register the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point cloud and spatial position information to obtain bridge deformation points;

[0146] Perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and perform key point extraction according to the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points;

[0147] After performing deformation rejection and vertical decomposition on the deformation data of the key points of the bridge deformation, deformation risk assessment is carried out to obtain the deformation monitoring risk evaluation result.

[0148] It should be understood that when the processor 1001 executes the bridge deformation monitoring program in the memory 1002, in addition to the above functions, other functions can also be realized. For specific details, reference can be made to the description of the corresponding method embodiments above.

[0149] Furthermore, the embodiments of the present invention do not specifically limit the type of the mentioned electronic device 1000. The electronic device 1000 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or other portable electronic devices. Exemplary embodiments of the portable electronic device include, but are not limited to, portable electronic devices equipped with IOS, android, microsoft, or other operating systems. The above-mentioned portable electronic devices can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1000 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (such as a touch panel).

[0150] Correspondingly, the embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium is used to store computer-readable programs or instructions. When the programs or instructions are executed by a processor, the method steps or functions of the bridge deformation monitoring method provided by the above-mentioned method embodiments can be realized.

[0151] Those skilled in the art can understand that all or part of the processes of implementing the above method embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The computer program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0152] The above has introduced in detail the bridge deformation monitoring method and device provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for monitoring bridge deformation, characterized in that, Including: Obtain time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area. Perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and the digital elevation models to obtain deformation candidate points and spatial position information. Register the deformation candidate points with the three-dimensional laser point clouds to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point clouds and the spatial position information to obtain bridge deformation points. Perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extract key points according to the three-dimensional laser point clouds and the deformation data to obtain bridge deformation key points. After performing deformation rejection and vertical decomposition on the deformation data of the bridge deformation key points, perform deformation risk assessment to obtain a deformation monitoring risk evaluation result. The performing InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and the digital elevation models to obtain deformation candidate points and spatial position information includes: Extract bridge PS points and bridge DS points in the time-series SAR satellite images based on the amplitude deviation and FaSHP similarity test method, and determine the bridge PS points and the bridge DS points as deformation candidate points. Perform relative height calculation on the triangular network constructed by the deformation candidate points based on the periodogram method to obtain the absolute height error of the deformation candidate points. Add the absolute height error to the digital elevation model, and perform geocoding on the deformation candidate points through the digital elevation model to obtain spatial position information.

2. The bridge deformation monitoring method according to claim 1, wherein The registering the deformation candidate points with the three-dimensional laser point clouds to obtain target deformation candidate points includes: Construct a KD tree according to the deformation candidate points and the three-dimensional laser point clouds. Determine the initial corresponding feature point pairs in the deformation candidate points and the three-dimensional laser points according to the fast point feature histogram. Screen and perform matrix transformation on the initial corresponding feature point pairs according to the RANSAC algorithm to obtain an initial rigid body transformation matrix. Perform iterative registration on the deformation candidate points and the three-dimensional laser points in the initial rigid body transformation matrix according to the KD tree to obtain a final transformation matrix, and determine the deformation candidate points in the final transformation matrix as target deformation candidate points.

3. The bridge deformation monitoring method according to claim 1, characterized in that, The screening the target deformation candidate points according to the three-dimensional laser point clouds and the spatial position information to obtain bridge deformation points includes: According to the target deformation candidate points and the spatial position information, determine the nearest laser point in the three-dimensional laser point clouds, and determine the adjacent laser points of the nearest laser point. Determine a local plane according to the nearest laser point and the adjacent laser points, and determine the distance of the target deformation candidate points according to the local plane. Remove the candidate points with a distance greater than a preset threshold in the target deformation candidate points to obtain remaining deformation candidate points. Remove noise points from the remaining deformation candidate points based on the DBSCAN algorithm to obtain bridge deformation points.

4. The bridge deformation monitoring method according to claim 1, characterized in that, Performing deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extracting key points based on the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points, including: Performing deformation speed calculation on the triangular network constructed by the bridge deformation points based on the periodogram method to obtain the deformation rate of the bridge deformation points; Performing phase separation on the bridge deformation points based on the spatio-temporal filtering method to obtain a deformation time series, and obtaining deformation data based on the deformation rate and the deformation time series; Performing extraction of different types of key points based on the three-dimensional laser point cloud and all deformation data in the bridge as a whole to obtain bridge deformation key points of each type.

5. The bridge deformation monitoring method according to claim 4, characterized in that, After performing deformation rejection and vertical decomposition on the deformation data of the bridge deformation key points, performing deformation risk assessment to obtain a deformation monitoring risk evaluation result, including: Performing periodic deformation rejection and vertical decomposition on the deformation time series of the bridge deformation key points based on empirical mode decomposition and the deformation rate to obtain bridge deformation data; Performing overall risk assessment based on the bridge deformation data and type of the bridge deformation key points to obtain a risk level; Determining the deformation monitoring risk evaluation result of the bridge as a whole according to the risk level.

6. The bridge deformation monitoring method according to claim 5, characterized in that, The performing periodic deformation rejection and vertical decomposition on the deformation time series of the bridge deformation key points based on empirical mode decomposition and the deformation rate to obtain bridge deformation data includes: Decomposing the deformation time series of the bridge deformation key points based on the empirical mode decomposition to obtain a plurality of IMF components and a residual term; Analyzing the plurality of IMF components according to a preset temperature period frequency to obtain periodic IMF components; Removing the periodic IMF components from the plurality of IMF components and then reconstructing the remaining IMF components to obtain initial bridge deformation data; Performing vertical displacement decomposition on the initial bridge deformation data according to the deformation rate to obtain a vertical deformation rate, and determining the vertical deformation rate as bridge deformation data.

7. The bridge deformation monitoring method according to claim 6, wherein The performing overall risk assessment based on the bridge deformation data and type of the bridge deformation key points to obtain a risk level includes: Determining target bridge deformation key points of each type according to the type of the bridge deformation key points; Obtaining the overall average deformation rate of each type according to the target bridge deformation key points and the corresponding vertical deformation rate; Performing overall risk assessment on the overall average deformation rate according to a preset level division to obtain the risk level of each type.

8. The bridge deformation monitoring method according to claim 1, characterized in that, Before registering the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, it further includes: Determining the incident angle and the ground distance angle according to the time-series SAR satellite image; Performing rough calibration calculation on the incident angle, the ground distance angle and the spatial position information according to a preset rough calibration model to obtain the deformed candidate points after rough calibration.

9. A bridge deformation monitoring device, characterized in that, Including: A candidate point extraction module, which is used to obtain time-series SAR satellite images, three-dimensional laser point clouds, and digital elevation models covering the bridge area, perform InSAR deformation detection and spatial coordinate extraction on the time-series SAR satellite images and the digital elevation models to obtain deformation candidate points and spatial position information; A deformation point determination module, which is used to register the deformation candidate points with the three-dimensional laser point cloud to obtain target deformation candidate points, and screen the target deformation candidate points according to the three-dimensional laser point cloud and the spatial position information to obtain bridge deformation points; A key point extraction module, which is used to perform deformation calculation on the bridge deformation points to obtain deformation data of the bridge deformation points, and extract key points according to the three-dimensional laser point cloud and the deformation data to obtain bridge deformation key points; A result determination module, which is used to perform deformation risk assessment after deformation rejection and vertical decomposition of the deformation data of the bridge deformation key points to obtain a deformation monitoring risk evaluation result; The candidate point extraction module is further used to extract bridge PS points and bridge DS points in the time-series SAR satellite images based on the amplitude deviation and the FaSHP similarity test method, and determine the bridge PS points and the bridge DS points as deformation candidate points; perform relative height calculation on the triangular network constructed by the deformation candidate points based on the periodogram method to obtain the absolute height error of the deformation candidate points; add the absolute height error to the digital elevation model, and perform geocoding on the deformation candidate points through the digital elevation model to obtain spatial position information.

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