Slope deformation monitoring method and system based on slope radar and three-dimensional scanning

Abstract

The landslide deformation cooperative monitoring method and system based on a slope radar and three-dimensional scanning disclosed by the application obtain the deformation variable of the line-of-sight direction through the slope radar; the three-dimensional laser scanner is used for regularly scanning the monitoring area to obtain three-dimensional point cloud data at different time nodes; and then the deformation variable of the line-of-sight direction and the three-dimensional point cloud data are combined, so that the accurate and reliable acquisition of the three-dimensional deformation field of the landslide is realized, and the accuracy of the monitoring is improved.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring technology, and more specifically, to a method and system for coordinated monitoring of landslide deformation based on slope radar and three-dimensional scanning. Background Technology

[0002] Landslides are severe geological hazards, and high-precision, all-weather deformation monitoring is crucial for early warning and disaster mitigation. Currently, the main monitoring technologies include slope radar and 3D laser scanning; however, slope radar acquires one-dimensional deformation along the line of sight and is easily affected by atmospheric effects; 3D laser scanning obtains high-density 3D point cloud data of the monitored object's surface through laser ranging, directly providing 3D spatial coordinates, but it is greatly affected by weather conditions such as rain and fog. Summary of the Invention

[0003] In view of the above problems, the purpose of this invention is to provide a method and system for coordinated monitoring of landslide deformation based on slope radar and three-dimensional scanning, which combines slope radar and three-dimensional scanning to achieve high-precision prediction of landslide bodies from one-dimensional to three-dimensional.

[0004] The first aspect of this invention provides a method for collaborative monitoring of landslide deformation based on slope radar and three-dimensional scanning, comprising:

[0005] Marker points were set up within the landslide monitoring area, and the three-dimensional coordinates of the marker points were measured.

[0006] At a preset first time period, the monitoring area is monitored by a preset slope radar to obtain radar data at different time nodes, and the line-of-sight deformation of the monitoring area is obtained by processing.

[0007] At a preset second time interval, the monitoring area is periodically scanned by a preset 3D laser scanner to obtain 3D point cloud data at different time points;

[0008] Using the three-dimensional coordinates of the marker points as spatial reference points and the line-of-sight deformation as a constraint, the three-dimensional point cloud data at different time points are fused to obtain the three-dimensional deformation field V of the landslide body.

[0009] The average deformation of the entire landslide monitoring area is obtained based on the three-dimensional deformation field V of the landslide body.

[0010] If the average deformation exceeds the preset deformation threshold in the corresponding direction, an alert message will be triggered and displayed to the preset management terminal.

[0011] In this scheme, the marker point is a device with a corner reflector installed at the top and a target ball installed at the bottom.

[0012] In this solution, after obtaining the 3D point cloud data at different time points, the method further includes:

[0013] Convert the three-dimensional point cloud data into a three-dimensional real scene model based on preset software, and export a front projection image to obtain a landslide image;

[0014] Divide the landslide image into a plurality of sub-landslide images according to a preset grid size;

[0015] Extract elements in the sub-landslide image and corresponding element features;

[0016] Compare the element features in the sub-landslide image with preset element features to obtain an element feature similarity value;

[0017] If the element feature similarity value is greater than a preset first similarity threshold, the corresponding element is retained; otherwise, the corresponding element is deleted to obtain a deleted element;

[0018] Based on the deleted element, determine the corresponding three-dimensional point in the three-dimensional point cloud data, and delete the corresponding three-dimensional point.

[0019] In this scheme, it also includes:

[0020] Extract the area of the retained element in the sub-landslide image;

[0021] If the area of the retained element in the sub-landslide image is greater than a preset area threshold, subtract the preset area threshold from the area of the retained element in the sub-landslide image to obtain a first area difference value;

[0022] Extract the boundary line of the corresponding retained element in the corresponding sub-landslide image;

[0023] Take the boundary line as a reference, and inwardly contract the first area difference value to obtain a region corresponding to the inwardly contracted first area difference value;

[0024] According to the region corresponding to the inwardly contracted first area difference value, determine the three-dimensional point corresponding to the region corresponding to the inwardly contracted first area difference value, and delete the corresponding three-dimensional point.

[0025] In this scheme, the step of taking the line-of-sight deformation variable as a constraint condition and fusing the three-dimensional point cloud data at different time nodes to obtain the three-dimensional deformation field V of the landslide body, specifically includes:

[0026] Split the line-of-sight deformation variable, and its formula is , wherein represents the line-of-sight deformation variable, represents the unit vector of the radar line-of-sight direction;

[0027] Take the three-dimensional coordinates of the landmark point as a spatial reference point, and convert the point coordinates in the three-dimensional point cloud data at different time nodes into three-dimensional coordinate points under the spatial reference point ;

[0028] The three-dimensional coordinate points under the reference time node For reference, each subsequent time node is registered with the three-dimensional coordinate points under the reference time node, and the formula is:

[0029] Where A satisfies ; Where represents the vector of the three-dimensional coordinate points corresponding to the i-th time node , represents the vector of the three-dimensional coordinate points corresponding to the reference time node , R represents the rotation matrix between the scans corresponding to two different time nodes, M represents the translation vector between the scans corresponding to two different time nodes, ; A represents the real deformation vector, ; represents the observation error vector; T represents the transpose of the vector;

[0030] Take the three-dimensional coordinate points under the same time node as the reference, construct a multivariate linear equation system of the three-dimensional coordinate points under the same time node, and obtain the set of R, M, and A of the three-dimensional scan under the corresponding time node;

[0031] Based on the preset algorithm, the three-dimensional deformation of the landslide body at the corresponding time node is determined according to the set of R, M, and A of the three-dimensional scan under the corresponding time node.

[0032] Iterate through all time nodes to form a three-dimensional deformation field V of the landslide body by the three-dimensional deformation of the landslide body at different time nodes.

[0033] In the scheme, the unit vector of the radar line-of-sight direction is determined by the radar beam's pitch angle and azimuth angle , and the formula is:

[0034] .

[0035] In the scheme, the observation error vector is obtained by:

[0036] Extracting the environmental data information under the i-th time node;

[0037] Obtaining historical three-dimensional point cloud data and corresponding historical environmental data information;

[0038] Comparing and analyzing the environmental data information under the i-th time node with the historical environmental data information to obtain an environmental data similarity value;

[0039] When the environment data similarity value is greater than a preset second similarity threshold value, historical three-dimensional point cloud data corresponding to the historical environment data information is saved;

[0040] The saved historical three-dimensional point cloud data is extracted, and a historical observation error vector in the saved historical three-dimensional point cloud data is extracted;

[0041] The historical observation error vector in the saved historical three-dimensional point cloud data is subjected to mean value calculation, to obtain an observation error vector at a current i th time node.

[0042] The second aspect of the present application provides a landslide deformation collaborative monitoring system based on slope radar and three-dimensional scanning, comprising a memory and a processor, the memory stores a landslide deformation collaborative monitoring method program based on slope radar and three-dimensional scanning, and the landslide deformation collaborative monitoring method program based on slope radar and three-dimensional scanning is executed by the processor to realize the following steps:

[0043] Setting a marker point in the landslide monitoring area, and measuring the three-dimensional coordinates of the marker point;

[0044] Intervals of a preset first time period, monitoring the monitoring area by a preset slope radar, obtaining radar data at different time nodes, and processing to obtain the line-of-sight deformation of the monitoring area;

[0045] Intervals of a preset second time period, periodically scanning the monitoring area by a preset three-dimensional laser scanner, obtaining three-dimensional point cloud data at different time nodes;

[0046] Taking the three-dimensional coordinates of the marker point as a spatial reference point, the line-of-sight deformation as a constraint condition, and the three-dimensional point cloud data at different time nodes are fused and processed to obtain a three-dimensional deformation field V of the landslide body;

[0047] According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained;

[0048] If the average deformation is greater than a preset deformation threshold value in the corresponding direction, an alarm information is triggered and a preset management terminal is prompted.

[0049] In the present scheme, the marker point is a device with an angle reflector installed on the top and a target ball installed on the bottom.

[0050] In the present scheme, after obtaining the three-dimensional point cloud data at different time nodes, it further comprises:

[0051] Based on a preset software, the three-dimensional point cloud data is converted into a three-dimensional real scene model, and a front projection image is exported to obtain a landslide image;

[0052] The landslide image is divided into a plurality of sub-landslide images according to a preset grid size;

[0053] extracting elements in the sub-landslide image and corresponding element features;

[0054] comparing the element features in the sub-landslide image with preset element features to obtain element feature similarity values;

[0055] if the element feature similarity value is greater than a preset first similarity threshold, the corresponding element is retained, otherwise, the corresponding element is deleted to obtain a deleted element;

[0056] based on the deleted element, a corresponding three-dimensional point in the three-dimensional point cloud data is determined, and the corresponding three-dimensional point is deleted.

[0057] The one or more technical solutions provided in the application have at least the following technical effects:

[0058] The flag point is set in the monitoring area, and an angle reflector and a target ball are installed on the top of the flag point, so that the radar data and the three-dimensional point cloud data are unified in the spatial coordinate system; the three-dimensional point cloud data is preprocessed to reduce the three-dimensional points that are easily disturbed by the outside world and are unstable to improve the data accuracy and efficiency, such as deleting the three-dimensional points corresponding to the non-stem and high-growing vegetation elements; the combination of the slope radar data and the three-dimensional point cloud data realizes the accurate and reliable acquisition of the landslide three-dimensional deformation field. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 a flowchart of a landslide deformation cooperative monitoring method based on slope radar and three-dimensional scanning is shown;

[0060] Figure 2 a block diagram of a landslide deformation cooperative monitoring system based on slope radar and three-dimensional scanning is shown. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can also be implemented in other ways different from those described herein, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0063] Figure 1 a flowchart of a landslide deformation cooperative monitoring method based on slope radar and three-dimensional scanning is shown;

[0064] As Figure 1The application discloses a landslide deformation cooperative monitoring method based on a slope radar and three-dimensional scanning, which comprises the following steps:

[0065] S101, setting a mark point in a landslide monitoring area and measuring three-dimensional coordinates of the mark point;

[0066] S102, monitoring the monitoring area by a preset slope radar at intervals of a preset first time period, obtaining radar data at different time nodes, and processing to obtain a line-of-sight deformation variable of the monitoring area;

[0067] S103, periodically scanning the monitoring area by a preset three-dimensional laser scanner at intervals of a preset second time period, obtaining three-dimensional point cloud data at different time nodes;

[0068] S104, taking the three-dimensional coordinates of the mark point as a spatial reference point, fusing the three-dimensional point cloud data at different time nodes with the line-of-sight deformation variable as a constraint condition, and obtaining a three-dimensional deformation field V of the landslide body;

[0069] S105, obtaining an average deformation variable of the entire landslide monitoring area according to the three-dimensional deformation field V of the landslide body;

[0070] S106, if the average deformation variable is greater than a preset deformation variable threshold in a corresponding direction, triggering an alarm information and prompting a preset management end.

[0071] According to the embodiment of the application, the slope radar device is erected in a set area, the landslide area is continuously observed, the radar complex data on the time sequence is obtained with a preset first time period as a sampling interval, the sampling interval can be minute or hour level, the three-dimensional laser scanner is used to periodically scan the landslide body in the intermittent period or the critical period of the radar monitoring, the preset second time period can be the same as or different from the preset first time period, for example, the preset first time period is 60 minutes, and the preset second time period is 100 minutes, the time sequence complex image obtained by the slope radar is subjected to D-lnSAR processing, the line-of-sight deformation variable of each pixel point is extracted, and the three-dimensional deformation field V of the landslide body includes the deformation variables A of a plurality of pixel points, wherein represents the deformation variable of the point in the east direction, the positive value represents eastward movement, and the negative value represents westward movement, wherein represents the deformation variable of the point in the north direction, the positive value represents northward movement, and the negative value represents southward movement, wherein This represents the deformation at a point in the vertical direction; a positive value indicates upward lifting, and a negative value indicates downward subsidence. The average deformation of the three-dimensional deformation field V of the landslide body in the east direction is calculated by averaging the deformation of all points in the east direction, and then the average deformation in the vertical direction and the average deformation in the north direction are obtained. Corresponding deformation thresholds are set for different directions, such as a deformation threshold of 5 mm in the vertical direction.

[0072] Furthermore, based on the three-dimensional deformation field V of the landslide body, the total displacement of each three-dimensional point is determined; after traversing all three-dimensional points, the average displacement of the current time node is obtained; based on the average displacement and the displacement at the reference time, the average deformation rate from the current time node to the reference time node is obtained; based on the preset range in which the average deformation rate falls, the warning level of the current landslide monitoring area is determined.

[0073] It should be noted that the total displacement of each three-dimensional point is set as Its formula is: The average displacement of the current time node is obtained by averaging the total displacement of all three-dimensional points. Set the deformation rate to Its formula is: ,in Indicates time node The average displacement, Indicates the reference time node The average displacement, Indicates time node To the base time node The time difference; the preset range is set according to the actual situation, for example, it can be divided into: less than 2 mm / year, greater than or equal to 2 mm / year and less than 20 mm / year, greater than 20 mm / year and greater than 1 mm / day. When the average deformation rate is less than 2 mm / year, the warning level of the current landslide monitoring area is zero, that is, the corresponding landslide monitoring area is a stable area; when the average deformation rate is greater than or equal to 2 mm / year and less than 20 mm / year, the first-level warning information of the current landslide monitoring area is triggered, and the corresponding landslide monitoring area is set as a slight deformation area; when the average deformation rate is greater than 20 mm / year, the second-level warning information of the current landslide monitoring area is triggered, and the corresponding landslide monitoring area is set as a significant deformation area; when the average deformation rate is greater than 1 mm / day, the third-level warning information of the current landslide monitoring area is triggered, and the corresponding landslide monitoring area is set as a severe deformation area.

[0074] According to an embodiment of the present invention, it further includes:

[0075] Obtain soil moisture values ​​within the landslide monitoring area;

[0076] According to the soil humidity value range into which the soil humidity value in the landslide monitoring area falls, a period optimization coefficient is obtained;

[0077] The preset first time period is set as , and the preset second time period is set as , and the formula is , wherein n=1 or 2; represents the preset first time period or the preset second time period after adjustment; represents the preset first time period or the preset second time period, represents an optimization coefficient.

[0078] It should be noted that the soil humidity value is displayed in percentage, for example, the soil humidity value is 40%, which can be obtained by testing by a soil humidity speed tester; for example, the humidity value range is divided into less than 40%, greater than or equal to 40% and less than 70%, and greater than 70%, wherein when the soil humidity value is less than 40%, the corresponding period optimization coefficient is zero; when the soil humidity value is greater than or equal to 40% and less than 70% and higher than 70%, the corresponding period optimization coefficient is set as ; when the soil humidity value is greater than or equal to 70%, the corresponding period optimization coefficient is set as , wherein .

[0079] According to the embodiment of the present application, the mark point is a device with an angle reflector mounted on the top and a target ball mounted on the bottom.

[0080] It should be noted that the angle reflector is a device composed of three mutually perpendicular metal plates, which has a special geometric collision and can reflect the incident radar wave along a completely parallel path back to the radar receiver, which makes it become a two-point with extremely high brightness, namely a permanent scatterer, on the radar image; the surface of the target ball is covered with high-efficiency micro-prism material, which can almost completely reflect the laser beam back to the scanner along the original path, and in the three-dimensional scanning point cloud, the target ball will present a sphere with extremely high intensity, regular shape and easy to be automatically recognized; therefore, the strict unification of the two kinds of data in the spatial coordinate system and the time sequence can be realized through the mark point.

[0081] According to the embodiment of the present application, after the three-dimensional point cloud data at different time nodes are obtained, the method further comprises:

[0082] Based on the preset software, the three-dimensional point cloud data are converted into a three-dimensional real scene model, and a front projection image is exported, to obtain a landslide image;

[0083] The landslide image is divided into a plurality of sub-landslide images according to a preset grid size;

[0084] Elements in the sub-landslide image and corresponding element features are extracted;

[0085] Comparing and analyzing the element features in the sub-landslide image with the preset element features, an element feature similarity value is obtained;

[0086] If the element feature similarity value is greater than a preset first similarity threshold, the corresponding element is retained; if not, the corresponding element is deleted, and a deleted element is obtained;

[0087] Based on the deleted element, a corresponding three-dimensional point in the three-dimensional point cloud data is determined, and the corresponding three-dimensional point is deleted.

[0088] It should be noted that the exported orthographic image is a projection image in the radar line-of-sight direction; the elements in the sub-landslide image include buildings, stones, vegetation, etc.; the element features are features such as the color, height, and size of the corresponding elements, such as the size of the stone and the height of the vegetation; and the preset element features include building element features and stone element features, which are less affected by the natural environment.

[0089] According to the embodiment of the application, the method further comprises:

[0090] The area of the retained element in the sub-landslide image is extracted;

[0091] If the area of the retained element in the sub-landslide image is greater than a preset area threshold, the area of the retained element in the sub-landslide image is subtracted by the preset area threshold, and a first area difference value is obtained;

[0092] The boundary line of the corresponding retained element in the corresponding sub-landslide image is extracted;

[0093] The first area difference value is inwardly contracted based on the boundary line, and a region corresponding to the inwardly contracted first area difference value is obtained;

[0094] According to the region corresponding to the inwardly contracted first area difference value, a three-dimensional point corresponding to the region is determined, and the three-dimensional point is deleted.

[0095] It should be noted that the area of the retained element in each sub-landslide image is limited by the preset area threshold, which further reduces the calculation amount of the entire data, for example, the preset area threshold is one-third of the area of the corresponding sub-landslide image; by selecting the points inside the retained element, the accuracy of the corresponding three-dimensional point is further improved.

[0096] According to the embodiment of the application, the method further comprises:

[0097] When the area of the retained element in the sub-landslide image is zero, the vegetation contour in the sub-landslide image is extracted;

[0098] Based on the vegetation contour in the sub-landslide image, the features and corresponding feature values of the corresponding vegetation are determined.

[0099] The vegetation feature values ​​are normalized to obtain normalized feature values;

[0100] Multiply the normalized eigenvalue by the corresponding feature weight coefficient to obtain the corresponding feature priority index;

[0101] The priority index of the corresponding vegetation is obtained by summing up all the feature priority indices.

[0102] Arrange the vegetation priority indices in descending order, save the 3D points within the outline of the vegetation corresponding to the highest priority index, and delete the other 3D points.

[0103] It should be noted that the characteristics of the vegetation include the size of the leaves, the thickness of the stems, and the height of the vegetation. The smaller and flatter the leaves, the larger the normalized characteristic value; the thicker the stems, the larger the normalized characteristic value; and the shorter the height, the larger the normalized characteristic value. The smaller and flatter the leaves, the thicker the stems, and the shorter the height of the vegetation, the less the vegetation is affected by the environment, and the more stable and accurate the 3D point cloud data obtained by scanning with a 3D laser scanner is.

[0104] According to an embodiment of the present invention, the step of fusing the line-of-sight deformation as a constraint with three-dimensional point cloud data at different time points to obtain the three-dimensional deformation field V of the landslide body specifically includes:

[0105] The formula for decomposing the line of sight into shape variables is as follows: ,in Indicates the line-of-sight deformation. A unit vector representing the direction of the radar line of sight;

[0106] Using the three-dimensional coordinates of the marker points as the spatial reference points, the point coordinates in the three-dimensional point cloud data at different time points are converted into three-dimensional coordinate points under the spatial reference points. ;

[0107] Three-dimensional coordinates at the reference time node For reference, the 3D coordinate points at each subsequent time point are registered with the reference time point using the following formula:

[0108] Where A satisfies ;in A vector representing the three-dimensional coordinate point corresponding to the i-th time node. , A vector representing the three-dimensional coordinate points corresponding to the reference time node. R represents the rotation matrix between two scans at different time points, and M represents the translation vector between two scans at different time points. ; A represents a real deformation vector, ; represents an observation error vector; T represents the transpose of a vector;

[0109] Taking the three-dimensional coordinate points at the same time node as a reference, a multivariate linear equation group of the three-dimensional coordinate points at the same time node is constructed, and the R, M and A of the three-dimensional scanning at the corresponding time node are obtained;

[0110] Based on a preset algorithm, the three-dimensional deformation of the landslide body at the corresponding time node is determined according to the R, M and A of the three-dimensional scanning at the corresponding time node;

[0111] All time nodes are traversed, and the three-dimensional deformation of the landslide body at different time nodes is composed into a three-dimensional deformation field V of the landslide body.

[0112] It should be noted that three three-dimensional coordinate points at the same time node are randomly selected and matched with the three-dimensional coordinate points at the reference time node to obtain the multivariate linear equation group of the three-dimensional coordinate points, and the rotation matrix R, the translation vector M and the three-dimensional deformation vector A of each point are jointly solved through a least square adjustment or Kalman filtering algorithm; through the constraint of the rotation matrix R, the translation vector M and the three-dimensional deformation vector A of each point, the uncertainty of the solution is further reduced.

[0113] According to the embodiment of the present application, the unit vector of the radar line-of-sight direction is determined by the elevation angle and the azimuth angle , and the formula is:

[0114] .

[0115] It should be noted that according to , further, the .

[0116] According to the embodiment of the present application, the acquisition step of the observation error vector specifically includes:

[0117] Extracting the environmental data information at the i th time node;

[0118] Obtaining historical three-dimensional point cloud data and corresponding historical environmental data information;

[0119] Comparing and analyzing the environmental data information at the i th time node and the historical environmental data information to obtain an environmental data similarity value;

[0120] When the environmental data similarity value is greater than a preset second similarity value threshold, the historical three-dimensional point cloud data corresponding to the historical environmental data information is saved;

[0121] ​extract the saved historical three-dimensional point cloud data, and extract a historical observation error vector in the saved historical three-dimensional point cloud data;

[0122] The historical observation error vector in the saved historical three-dimensional point cloud data is subjected to mean calculation to obtain an observation error vector at a current i th time node.

[0123] It should be noted that the historical three-dimensional point cloud data is calibration data before a three-dimensional scanner measures a landslide monitoring area, and includes actual coordinate points of calibration points in different historical environments and corresponding three-dimensional scanner measurement coordinate points. The historical observation error vector of the corresponding calibration point is determined .

[0124] The second aspect of the present application provides a landslide deformation cooperative monitoring system based on a slope radar and three-dimensional scanning, which comprises a memory and a processor, the memory stores a landslide deformation cooperative monitoring method program based on a slope radar and three-dimensional scanning, and the landslide deformation cooperative monitoring method program based on a slope radar and three-dimensional scanning is executed by the processor to realize the following steps:

[0125] Setting a marker point in a landslide monitoring area and measuring the three-dimensional coordinates of the marker point;

[0126] Intervals of a preset first time period, monitoring the monitoring area by a preset slope radar, obtaining radar data at different time nodes, and processing to obtain the line-of-sight deformation of the monitoring area;

[0127] Intervals of a preset second time period, periodically scanning the monitoring area by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes;

[0128] Taking the three-dimensional coordinates of the marker point as a spatial reference point, taking the line-of-sight deformation as a constraint condition, and fusing the three-dimensional point cloud data at different time nodes to obtain a three-dimensional deformation field V of the landslide body;

[0129] According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained;

[0130] If the average deformation is greater than the preset deformation threshold in the corresponding direction, an alarm information is triggered and a preset management terminal is prompted.

[0131] In the present scheme, the marker point is a device with an angle reflector installed on the top and a target ball installed on the bottom.

[0132] In the present scheme, after obtaining the three-dimensional point cloud data at different time nodes, it further comprises:

[0133] Based on the preset software, the three-dimensional point cloud data is converted into a three-dimensional real scene model, and a front projection image is exported to obtain a landslide image;

[0134] The landslide image is divided into a plurality of sub-landslide images according to a preset grid size;

[0135] Elements in the sub-landslide image and corresponding element features are extracted;

[0136] The element features in the sub-landslide image are compared with preset element features to obtain an element feature similarity value;

[0137] If the element feature similarity value is greater than a preset first similarity threshold, the corresponding element is retained; otherwise, the corresponding element is deleted to obtain a deleted element;

[0138] Based on the deleted element, a corresponding three-dimensional point in the three-dimensional point cloud data is determined, and the corresponding three-dimensional point is deleted.

[0139] The landslide deformation collaborative monitoring method and system based on the slope radar and three-dimensional scanning disclosed in the application obtain the deformation amount in the line-of-sight direction through the slope radar; the three-dimensional point cloud data at different time nodes is obtained by periodically scanning the monitoring area through the three-dimensional laser scanner; and then the deformation amount in the line-of-sight direction and the three-dimensional point cloud data are combined to realize accurate and reliable acquisition of the three-dimensional deformation field of the landslide, thereby improving the accuracy of the monitoring.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and actual implementation can have another division mode. For example, multiple units or components can be combined, or can be 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 various components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0141] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment scheme.

[0142] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be separately taken as one unit, 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 hardware plus software function unit.

[0143] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps including the above-mentioned method embodiments when executed; and the foregoing storage medium includes a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disc or an optical disc, and various storage medium capable of storing program codes.

[0144] Alternatively, when the integrated unit of the present application is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes a mobile storage device, a ROM, a RAM, a magnetic disc or an optical disc, and various storage medium capable of storing program codes.

Claims

1. A method for coordinated monitoring of landslide deformation based on slope radar and 3D scanning, characterized in that, include: Marker points were set up within the landslide monitoring area, and the three-dimensional coordinates of the marker points were measured. At a preset first time period, the monitoring area is monitored by a preset slope radar to obtain radar data at different time nodes, and the line-of-sight deformation of the monitoring area is obtained by processing. At a preset second time interval, the monitoring area is periodically scanned by a preset 3D laser scanner to obtain 3D point cloud data at different time points; Using the three-dimensional coordinates of the marker points as spatial reference points and the line-of-sight deformation as a constraint, the three-dimensional point cloud data at different time points are fused to obtain the three-dimensional deformation field V of the landslide body. The average deformation of the entire landslide monitoring area is obtained based on the three-dimensional deformation field V of the landslide body. If the average deformation exceeds the preset deformation threshold in the corresponding direction, an alarm message will be triggered and the preset management terminal will be notified. After obtaining the 3D point cloud data at different time points, the process also includes: Based on the pre-set software, the 3D point cloud data is converted into a 3D real scene model and the orthographic projection image is exported to obtain the landslide image. The landslide image is divided into multiple sub-landslide images according to a preset grid size; Extract elements and corresponding element features from sub-landslide images; The element features in the sub-landslide image are compared and analyzed with the preset element features to obtain the element feature similarity value; If the similarity value of an element's features is greater than a preset first similarity threshold, the corresponding element will be retained; otherwise, the corresponding element will be deleted, resulting in the deleted element. Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are identified, and the corresponding 3D points are deleted. Also includes: Extract the area of ​​elements retained in the sub-landslide image; If the area of ​​the element retained in the sub-landslide image is greater than the preset area threshold, then the area of ​​the element retained in the sub-landslide image is subtracted from the preset area threshold to obtain the first area difference. Extract the boundary lines of the corresponding retained elements in the corresponding sub-landslide image; Using the boundary line as a reference, the area is contracted inward by the first area difference to obtain the region corresponding to the first area difference contracted inward. Based on the region corresponding to the first area difference of inward contraction, determine the corresponding three-dimensional points in the region corresponding to the first area difference of inward contraction, and delete the corresponding three-dimensional points.

2. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 1, characterized in that, The marker point is a device with a corner reflector mounted on top and a target ball mounted on the bottom.

3. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 1, characterized in that, The step of fusing the line-of-sight deformation as a constraint with three-dimensional point cloud data from different time points to obtain the three-dimensional deformation field V of the landslide body specifically includes: The formula for decomposing the line of sight into shape variables is as follows: ,in Indicates the line-of-sight deformation. A unit vector representing the direction of the radar line of sight; Using the three-dimensional coordinates of the marker points as the spatial reference points, the point coordinates in the three-dimensional point cloud data at different time points are converted into three-dimensional coordinate points under the spatial reference points. ; Three-dimensional coordinates at the reference time node For reference, the 3D coordinate points at each subsequent time point are registered with the reference time point using the following formula: Where A satisfies ;in A vector representing the three-dimensional coordinate point corresponding to the i-th time node. , A vector representing the three-dimensional coordinate points corresponding to the reference time node. R represents the rotation matrix between two scans at different time points, and M represents the translation vector between two scans at different time points. A represents the true deformation vector. ; represents the observation error vector; T represents the transpose of the vector; Using the three-dimensional coordinate points at the same time node as the reference, a system of multiple linear equations for the three-dimensional coordinate points at the same time node is constructed to obtain the set of R, M, and A for the three-dimensional scan at the corresponding time node; Based on a preset algorithm, the three-dimensional deformation of the landslide body at the corresponding time node is determined according to the set of R, M, and A of the three-dimensional scan at the corresponding time node. By iterating through all time points, the three-dimensional deformation of the landslide body at different time points is used to form the three-dimensional deformation field V of the landslide body.

4. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 3, characterized in that, The unit vector in the radar line-of-sight direction passes through the elevation angle of the radar beam. and azimuth To determine this, the formula is: 。 5. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 3, characterized in that, The steps for obtaining the observation error vector specifically include: Extract environmental data information at the i-th time node; Acquire historical 3D point cloud data and corresponding historical environmental data information; By comparing and analyzing the environmental data at the i-th time point with historical environmental data, the environmental data similarity value is obtained. When the environmental data similarity value is greater than the preset second similarity value threshold, the historical 3D point cloud data corresponding to the historical environmental data information will be saved. Extract the saved historical 3D point cloud data, and extract the historical observation error vector from the saved historical 3D point cloud data; The average value of the historical observation error vectors in the saved historical 3D point cloud data is calculated to obtain the observation error vector at the current i-th time node.

6. A landslide deformation collaborative monitoring system based on slope radar and 3D scanning, characterized in that, The system includes a memory and a processor. The memory stores a program for a landslide deformation collaborative monitoring method based on slope radar and 3D scanning. When the processor executes the program, the landslide deformation collaborative monitoring method based on slope radar and 3D scanning performs the following steps: Marker points were set up within the landslide monitoring area, and the three-dimensional coordinates of the marker points were measured. At a preset first time period, the monitoring area is monitored by a preset slope radar to obtain radar data at different time nodes, and the line-of-sight deformation of the monitoring area is obtained by processing. At a preset second time interval, the monitoring area is periodically scanned by a preset 3D laser scanner to obtain 3D point cloud data at different time points; Using the three-dimensional coordinates of the marker points as spatial reference points and the line-of-sight deformation as a constraint, the three-dimensional point cloud data at different time points are fused to obtain the three-dimensional deformation field V of the landslide body. The average deformation of the entire landslide monitoring area is obtained based on the three-dimensional deformation field V of the landslide body. If the average deformation exceeds the preset deformation threshold in the corresponding direction, an alarm message will be triggered and the preset management terminal will be notified. After obtaining the 3D point cloud data at different time points, the process also includes: Based on the pre-set software, the 3D point cloud data is converted into a 3D real scene model and the orthographic projection image is exported to obtain the landslide image. The landslide image is divided into multiple sub-landslide images according to a preset grid size; Extract elements and corresponding element features from sub-landslide images; The element features in the sub-landslide image are compared and analyzed with the preset element features to obtain the element feature similarity value; If the similarity value of an element's features is greater than a preset first similarity threshold, the corresponding element will be retained; otherwise, the corresponding element will be deleted, resulting in the deleted element. Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are identified, and the corresponding 3D points are deleted. Also includes: Extract the area of ​​elements retained in the sub-landslide image; If the area of ​​the element retained in the sub-landslide image is greater than the preset area threshold, then the area of ​​the element retained in the sub-landslide image is subtracted from the preset area threshold to obtain the first area difference. Extract the boundary lines of the corresponding retained elements in the corresponding sub-landslide image; Using the boundary line as a reference, the area is contracted inward by the first area difference to obtain the region corresponding to the first area difference contracted inward. Based on the region corresponding to the first area difference of inward contraction, determine the corresponding three-dimensional points in the region corresponding to the first area difference of inward contraction, and delete the corresponding three-dimensional points.

7. The landslide deformation collaborative monitoring system based on slope radar and three-dimensional scanning according to claim 6, characterized in that, The marker point is a device with a corner reflector mounted on top and a target ball mounted on the bottom.

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