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

Abstract

The invention discloses a slope radar and three-dimensional scanning-based landslide deformation cooperative monitoring method and system. The method comprises the following steps of: acquiring deformation quantity in a sight line direction through a slope radar; periodically scanning a monitoring area through a three-dimensional laser scanner to obtain three-dimensional point cloud data of different time nodes; and the deformation quantity in the sight line direction is combined with the three-dimensional point cloud data, so that the accurate and reliable acquisition of the landslide three-dimensional deformation field is realized, and the monitoring accuracy is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and more specifically, to a landslide deformation collaborative monitoring method and system based on slope radar and three-dimensional scanning. Background Art

[0002] Landslides are serious landslide hazards, and high-precision, all-weather deformation monitoring is crucial for early warning and disaster reduction. Currently, the main monitoring technologies include slope radar and 3D laser scanning. However, slope radar only captures one-dimensional deformation in the line of sight and is susceptible to atmospheric effects. 3D laser scanning uses the principle of laser ranging to obtain high-density 3D point cloud data on the surface of the monitored object, directly deriving 3D spatial coordinates. However, this is significantly affected by weather conditions, such as rain and fog. Summary of the Invention

[0003] In view of the above problems, the purpose of the present invention is to provide a landslide deformation collaborative monitoring method and system based on slope radar and three-dimensional scanning, combining slope radar with three-dimensional scanning to achieve high-precision prediction of landslide bodies from one dimension to three dimensions.

[0004] The first aspect of the present invention provides a landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning, comprising: Setting marker points in the landslide monitoring area and measuring the three-dimensional coordinates of the marker points; At intervals of a preset first time period, the monitoring area is monitored by a preset slope radar, radar data at different time nodes are obtained, and the line-of-sight deformation of the monitoring area is obtained by processing; A second time period is preset at intervals, and the monitoring area is regularly scanned by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; The three-dimensional coordinates of the marker point are used as the spatial reference point, the sight line deformation is used as the constraint condition, and the three-dimensional point cloud data at different time nodes are fused to obtain the three-dimensional deformation field V of the landslide body; According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained; If the average deformation is greater than the preset deformation threshold in the corresponding direction, a warning message is triggered and the preset management terminal is prompted.

[0005] In this solution, the marking point is a device with a corner reflector installed on the top and a target ball installed on the bottom.

[0006] In this solution, after obtaining the three-dimensional point cloud data at different time nodes, the following steps are further included: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

[0007] This plan also includes: Extract the element area retained in the sub-landslide image; If the area of ​​the element retained in the sub-landslide image is greater than a preset area threshold, the preset area threshold is subtracted from the area of ​​the element retained in the sub-landslide image to obtain a first area difference; Extracting the boundary lines of the corresponding retained elements in the corresponding sub-landslide image; Taking the boundary line as a reference, shrinking the first area difference inwards to obtain an area corresponding to the inwardly shrunk first area difference; According to the area corresponding to the first inwardly contracted area difference, three-dimensional points corresponding to the area corresponding to the first inwardly contracted area difference are determined, and the corresponding three-dimensional points are deleted.

[0008] In this solution, the step of taking the sight line deformation as a constraint condition and fusing it with the three-dimensional point cloud data at different time nodes to obtain the three-dimensional deformation field V of the landslide body specifically includes: Split the sight line into shape variables, and the formula is: ,in represents the sight line deformation, The unit vector representing the radar line of sight direction; Taking the three-dimensional coordinates of the marker point as the spatial reference point, the point coordinates in the three-dimensional point cloud data at different time nodes are converted into three-dimensional coordinate points under the spatial reference point. ; The three-dimensional coordinate point under the reference time node For reference, each subsequent time node is aligned with the three-dimensional coordinate point under the reference time node, and the formula is: , where A satisfies ;in Represents the vector of the three-dimensional coordinate point corresponding to the i-th time node , The vector representing the three-dimensional coordinate point corresponding to the benchmark time node , R represents the rotation matrix between the scans at two different time nodes, and M represents the translation vector between the scans at two different time nodes. ; A represents the true deformation vector, ; represents the observation error vector; T represents the transpose of the vector; Taking the three-dimensional coordinate points at the same time node as the benchmark, a multivariate linear equation system of the three-dimensional coordinate points at the same time node is constructed to obtain the set of R, M, and A of the three-dimensional scan at the corresponding time node; Based on the preset algorithm, the three-dimensional deformation of the corresponding 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; Traverse all time nodes and combine the three-dimensional deformation variables of the landslide body at different time nodes into the three-dimensional deformation field V of the landslide body.

[0009] In this solution, the unit vector of the radar line of sight passes through the pitch angle of the radar beam. and azimuth To determine, the formula is: .

[0010] In this solution, the step of obtaining the observation error vector specifically includes: Extract environmental data information at the i-th time node; Obtain historical 3D point cloud data and corresponding historical environmental data information; Compare and analyze the environmental data information at the i-th time node with the historical environmental data information to obtain the environmental data similarity value; When the similarity value of the environmental data 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; Extracting the saved historical three-dimensional point cloud data and extracting the historical observation error vector in the saved historical three-dimensional point cloud data; The historical observation error vectors in the saved historical 3D point cloud data are averaged to obtain the observation error vector at the current i-th time node.

[0011] A second aspect of the present invention 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 program for a landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning. When the program is executed by the processor, the following steps are implemented: Setting marker points in the landslide monitoring area and measuring the three-dimensional coordinates of the marker points; At intervals of a preset first time period, the monitoring area is monitored by a preset slope radar, radar data at different time nodes are obtained, and the line-of-sight deformation of the monitoring area is obtained by processing; A second time period is preset at intervals, and the monitoring area is regularly scanned by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; The three-dimensional coordinates of the marker point are used as the spatial reference point, the sight line deformation is used as the constraint condition, and the three-dimensional point cloud data at different time nodes are fused to obtain the three-dimensional deformation field V of the landslide body; According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained; If the average deformation is greater than the preset deformation threshold in the corresponding direction, a warning message is triggered and the preset management terminal is prompted.

[0012] In this solution, the marking point is a device with a corner reflector installed on the top and a target ball installed on the bottom.

[0013] In this solution, after obtaining the three-dimensional point cloud data at different time nodes, the following steps are further included: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

[0014] One or more technical solutions proposed in this application have at least the following technical effects: Marking points are set up in the monitoring area, and corner reflectors and target balls are installed on the top of the marking points to achieve the unification of radar data and three-dimensional point cloud data in the spatial coordinate system; by pre-processing the three-dimensional point cloud data, the three-dimensional points that are easily affected by external interference and fluctuate are reduced to improve data accuracy and efficiency, such as deleting the three-dimensional points corresponding to non-stem and taller vegetation elements; by combining slope radar data and three-dimensional point cloud data, the three-dimensional deformation field of the landslide is accurately and reliably acquired. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1The flowchart of the landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning of the present invention is shown; Figure 2 The block diagram of the landslide deformation collaborative monitoring system based on slope radar and three-dimensional scanning of the present invention is shown. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0018] Figure 1 The flowchart of the landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning of the present invention is shown.

[0019] like Figure 1 As shown, the present invention discloses a landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning, comprising: S101, setting a marker point in the landslide monitoring area and measuring the three-dimensional coordinates of the marker point; S102: monitoring the monitoring area at predetermined first time intervals using a predetermined slope radar, obtaining radar data at different time points, and processing the data to obtain a line-of-sight deformation of the monitoring area; S103, periodically scanning the monitoring area at predetermined second time intervals using a predetermined three-dimensional laser scanner to obtain three-dimensional point cloud data at different time points; S104, taking the three-dimensional coordinates of the marker point as a spatial reference point, taking the sight line 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; S105, obtaining an average deformation of the entire landslide monitoring area based on the three-dimensional deformation field V of the landslide body; S106: If the average deformation value is greater than the preset deformation value threshold value in the corresponding direction, a warning message is triggered and a preset management terminal is prompted.

[0020] According to an embodiment of the present invention, a slope radar device is set up in a set area, and the landslide area is continuously observed. A preset first time period is used as a sampling interval to obtain radar complex data in a time series, and the sampling interval can be in the order of minutes or hours. During the intermission or critical period of radar monitoring, a three-dimensional laser scanner is used to regularly scan the landslide body. The preset second time period can be the same as the preset first time period 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. D-lnSAR processing is performed on the time series complex images obtained by the slope radar to extract the line-of-sight deformation variable of each pixel point. The three-dimensional deformation field V of the landslide body includes the deformation variables A of multiple pixel points. ,in Represents the deformation of the point in the east direction, a positive value indicates moving eastward, and a negative value indicates moving westward; Indicates the deformation of the point in the north direction. A positive value indicates movement toward the north, and a negative value indicates movement toward the south. It represents the deformation of the point in the vertical direction, a positive value indicates upward uplift, and a negative value indicates downward subsidence; the deformation of all points in the east direction is averaged to obtain the average deformation of the three-dimensional deformation field V of the landslide 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 in different directions, for example, the deformation threshold in the vertical direction is 5 mm.

[0021] Furthermore, the total displacement of each three-dimensional point is determined based on the three-dimensional deformation field V of the landslide body; after traversing all three-dimensional points, the average displacement of the current time node is obtained; based on the average displacement and the displacement of the reference time, the average deformation rate from the current time node to the reference time node is obtained, and the warning level of the current landslide monitoring area is determined based on the preset range within which the average deformation rate falls.

[0022] It should be noted that the total displacement of each three-dimensional point is set to , the formula is: , calculate the average of the total displacement of all three-dimensional points and get the average displacement of the current time node ; Set the deformation rate to , the formula is: ,in Indicates a time node The average displacement of Indicates the benchmark time node The average displacement of Indicates a time node To the benchmark time node time difference; the preset range is set according to actual conditions, 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.

[0023] According to an embodiment of the present invention, the further embodiment includes: Obtain soil moisture values ​​within the landslide monitoring area; According to the humidity value range that the soil humidity value in the landslide monitoring area falls into, the cycle optimization coefficient is obtained; Set the preset first time period to , the second time period is preset to , whose formula is , where n=1 or 2; Indicates the preset first time period or the preset second time period after adjustment; Indicates the preset first time period or the preset second time period, Represents the optimization coefficient.

[0024] It should be noted that the soil moisture value is displayed in percentage. For example, if the soil moisture value is 40%, it can be obtained by testing with a soil moisture rapid 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 or equal to 70%. When the soil moisture value is less than 40%, the corresponding cycle optimization coefficient is zero; when the soil moisture value is greater than or equal to 40% and less than 70% and higher than 70%, the corresponding cycle optimization coefficient is set to ; When the soil moisture value is greater than or equal to 70%, the corresponding cycle optimization coefficient is set to ,in .

[0025] According to an embodiment of the present invention, the marking point is a device with a corner reflector installed on the top and a target ball installed on the bottom.

[0026] It should be noted that the corner reflector is a device composed of three mutually perpendicular metal plates. It has a special geometric collision and can reflect the incident radar wave back to the radar receiver along a completely parallel path, which makes it appear as two extremely bright points on the radar image, that is, a permanent scatterer; the surface of the target sphere is covered with a high-efficiency micro-prism material, which can reflect the laser beam almost completely back to the scanner along the original path. In the three-dimensional scanning point cloud, the target sphere will appear as a sphere with extremely high intensity, regular shape and easy to automatically identify; therefore, the two types of data can be strictly unified in the spatial coordinate system and time series through the marker points.

[0027] According to an embodiment of the present invention, after obtaining the three-dimensional point cloud data at different time nodes, the method further includes: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

[0028] It should be noted that the derived orthographic projection image is a projection image in the direction of the radar line of sight; the elements in the sub-landslide image include buildings, stones, vegetation, etc.; the element features are the color, height, size and other features of the corresponding elements, such as the size of stones, the height of vegetation, etc.; the preset element features include building element features, stone element features and other element features that are less affected by the natural environment.

[0029] According to an embodiment of the present invention, the further embodiment includes: Extract the element area retained in the sub-landslide image; If the area of ​​the element retained in the sub-landslide image is greater than a preset area threshold, the preset area threshold is subtracted from the area of ​​the element retained in the sub-landslide image to obtain a first area difference; Extracting the boundary lines of the corresponding retained elements in the corresponding sub-landslide image; Taking the boundary line as a reference, shrinking the first area difference inwards to obtain an area corresponding to the inwardly shrunk first area difference; According to the area corresponding to the first inwardly contracted area difference, three-dimensional points corresponding to the area corresponding to the first inwardly contracted area difference are determined, and the corresponding three-dimensional points are deleted.

[0030] It should be noted that the area of ​​the retained elements in each sub-landslide image is limited by a preset area threshold, which further reduces the computational complexity 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 internal points in the retained elements, the accuracy of the corresponding three-dimensional points is further improved.

[0031] According to an embodiment of the present invention, the further embodiment includes: When the area of ​​the elements retained in the sub-landslide image is zero, the vegetation contour in the sub-landslide image is extracted; Based on the vegetation contours in the sub-landslide image, determining the features and corresponding feature values ​​of the corresponding vegetation; Normalizing the characteristic values ​​of vegetation to obtain normalized characteristic values; Multiply the normalized eigenvalue by the corresponding feature weight coefficient to obtain the corresponding feature priority index; All feature priority indices are accumulated to obtain the priority index of the corresponding vegetation; Arrange the priority indexes of vegetation from large to small, save the 3D points within the outline of the vegetation corresponding to the largest priority index, and delete the other 3D points.

[0032] It should be noted that the characteristics of the vegetation include the width and flatness of the leaves, the thickness of the stems, the height of the vegetation, etc., among which the flatter and smaller the leaves, the larger the corresponding normalized eigenvalue; the thicker the stems, the larger the corresponding normalized eigenvalue; the shorter the height, the larger the corresponding normalized eigenvalue; the flatter and smaller the leaves, the thicker the stems, and the shorter the height, the less the corresponding vegetation is affected by the environment, and the more stable and accurate the three-dimensional point cloud data obtained by scanning with a three-dimensional laser scanner.

[0033] According to an embodiment of the present invention, the step of using the sight line deformation as a constraint condition and fusing it with the three-dimensional point cloud data at different time nodes to obtain the three-dimensional deformation field V of the landslide body specifically includes: Split the sight line into shape variables, and the formula is: ,in represents the sight line deformation, The unit vector representing the radar line of sight direction; Taking the three-dimensional coordinates of the marker point as the spatial reference point, the point coordinates in the three-dimensional point cloud data at different time nodes are converted into three-dimensional coordinate points under the spatial reference point. ; The three-dimensional coordinate point under the reference time node For reference, each subsequent time node is aligned with the three-dimensional coordinate point under the reference time node, and the formula is: , where A satisfies ;in Represents the vector of the three-dimensional coordinate point corresponding to the i-th time node , The vector representing the three-dimensional coordinate point corresponding to the benchmark time node , R represents the rotation matrix between the scans at two different time nodes, and M represents the translation vector between the scans at two different time nodes. ; A represents the true deformation vector, ; represents the observation error vector; T represents the transpose of the vector; Taking the three-dimensional coordinate points at the same time node as the benchmark, a multivariate linear equation system of the three-dimensional coordinate points at the same time node is constructed to obtain the set of R, M, and A of the three-dimensional scan at the corresponding time node; Based on the preset algorithm, the three-dimensional deformation of the corresponding 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; Traverse all time nodes and combine the three-dimensional deformation variables of the landslide body at different time nodes into the three-dimensional deformation field V of the landslide body.

[0034] It should be noted that three three-dimensional coordinate points at the same time node are randomly selected and aligned with the three-dimensional coordinate points at the reference time node to obtain a multivariate linear equation system of the three-dimensional coordinate points. The rotation matrix R, translation vector M and three-dimensional deformation vector A of each point are jointly solved through least squares adjustment or Kalman filtering algorithms. constraints, further reducing the uncertainty of the solution.

[0035] According to an embodiment of the present invention, the unit vector of the radar sight direction passes through the pitch angle of the radar beam. and azimuth To determine, the formula is: .

[0036] It should be noted that according to , further, we can conclude .

[0037] According to an embodiment of the present invention, the step of obtaining the observation error vector specifically includes: Extract environmental data information at the i-th time node; Obtain historical 3D point cloud data and corresponding historical environmental data information; Compare and analyze the environmental data information at the i-th time node with the historical environmental data information to obtain the environmental data similarity value; When the similarity value of the environmental data 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; Extracting the saved historical three-dimensional point cloud data and extracting the historical observation error vector in the saved historical three-dimensional point cloud data; The historical observation error vectors in the saved historical 3D point cloud data are averaged to obtain the observation error vector at the current i-th time node.

[0038] It should be noted that the historical 3D point cloud data is the calibration data before the corresponding 3D scanner measures the landslide monitoring area, including the actual coordinates of the calibration points in different historical environments and the corresponding 3D scanner measurement coordinates. The actual coordinates are subtracted from the corresponding 3D scanner measurement coordinates to obtain the coordinate measurement errors of the corresponding calibration points in different directions. , and then determine the historical observation error vector corresponding to the calibration point .

[0039] A second aspect of the present invention 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 program for a landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning. When the program is executed by the processor, the following steps are implemented: Setting marker points in the landslide monitoring area and measuring the three-dimensional coordinates of the marker points; At intervals of a preset first time period, the monitoring area is monitored by a preset slope radar, radar data at different time nodes are obtained, and the line-of-sight deformation of the monitoring area is obtained by processing; A second time period is preset at intervals, and the monitoring area is regularly scanned by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; The three-dimensional coordinates of the marker point are used as the spatial reference point, the sight line deformation is used as the constraint condition, and the three-dimensional point cloud data at different time nodes are fused to obtain the three-dimensional deformation field V of the landslide body; According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained; If the average deformation is greater than the preset deformation threshold in the corresponding direction, a warning message is triggered and the preset management terminal is prompted.

[0040] In this solution, the marking point is a device with a corner reflector installed on the top and a target ball installed on the bottom.

[0041] In this solution, after obtaining the three-dimensional point cloud data at different time nodes, the following steps are further included: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

[0042] The present invention discloses a collaborative landslide deformation monitoring method and system based on slope radar and three-dimensional scanning. The method obtains the deformation variable in the line of sight direction through the slope radar; regularly scans the monitoring area through a three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; and then combines the deformation variable in the line of sight direction with the three-dimensional point cloud data to achieve accurate and reliable acquisition of the three-dimensional deformation field of the landslide, thereby improving the accuracy of monitoring.

[0043] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: 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, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0044] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0045] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0046] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0047] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A landslide deformation collaborative monitoring method based on slope radar and 3D scanning, characterized in that: include: Setting marker points in the landslide monitoring area and measuring the three-dimensional coordinates of the marker points; At intervals of a preset first time period, the monitoring area is monitored by a preset slope radar, radar data at different time nodes are obtained, and the line-of-sight deformation of the monitoring area is obtained by processing; A second time period is preset at intervals, and the monitoring area is regularly scanned by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; The three-dimensional coordinates of the marker point are used as the spatial reference point, the sight line deformation is used as the constraint condition, and the three-dimensional point cloud data at different time nodes are fused to obtain the three-dimensional deformation field V of the landslide body; According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained; If the average deformation is greater than the preset deformation threshold in the corresponding direction, a warning message is triggered and the preset management terminal is prompted.

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

3. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 1 is characterized in that: After obtaining the three-dimensional point cloud data at different time nodes, the method further includes: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

4. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 3 is characterized in that: Also includes: Extract the element area retained in the sub-landslide image; If the area of ​​the element retained in the sub-landslide image is greater than a preset area threshold, the preset area threshold is subtracted from the area of ​​the element retained in the sub-landslide image to obtain a first area difference; Extracting the boundary lines of the corresponding retained elements in the corresponding sub-landslide image; Taking the boundary line as a reference, shrinking the first area difference inwards to obtain an area corresponding to the inwardly shrunk first area difference; According to the area corresponding to the first inwardly contracted area difference, three-dimensional points corresponding to the area corresponding to the first inwardly contracted area difference are determined, and the corresponding three-dimensional points are deleted.

5. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 1 is characterized in that: The step of taking the sight line deformation as a constraint condition and fusing it with the three-dimensional point cloud data at different time nodes to obtain the three-dimensional deformation field V of the landslide body specifically includes: Split the sight line into shape variables, and the formula is: ,in represents the sight line deformation, The unit vector representing the radar line of sight direction; Taking the three-dimensional coordinates of the marker point as the spatial reference point, the point coordinates in the three-dimensional point cloud data at different time nodes are converted into three-dimensional coordinate points under the spatial reference point. ; The three-dimensional coordinate point under the reference time node For reference, each subsequent time node is aligned with the three-dimensional coordinate point under the reference time node, and the formula is: , where A satisfies ;in Represents the vector of the three-dimensional coordinate point corresponding to the i-th time node , The vector representing the three-dimensional coordinate point corresponding to the benchmark time node , R represents the rotation matrix between the scans at two different time nodes, and M represents the translation vector between the scans at two different time nodes. ; A represents the true deformation vector, ; represents the observation error vector; T represents the transpose of the vector; Taking the three-dimensional coordinate points at the same time node as the benchmark, a multivariate linear equation system of the three-dimensional coordinate points at the same time node is constructed to obtain the set of R, M, and A of the three-dimensional scan at the corresponding time node; Based on the preset algorithm, the three-dimensional deformation of the corresponding 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; Traverse all time nodes and combine the three-dimensional deformation variables of the landslide body at different time nodes into the three-dimensional deformation field V of the landslide body.

6. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 5 is characterized in that: The unit vector of the radar line of sight passes through the pitch angle of the radar beam and azimuth To determine, the formula is: 。 7. The landslide deformation collaborative monitoring method based on slope radar and three-dimensional scanning according to claim 5 is characterized in that: The step of obtaining the observation error vector specifically includes: Extract environmental data information at the i-th time node; Obtain historical 3D point cloud data and corresponding historical environmental data information; Compare and analyze the environmental data information at the i-th time node with the historical environmental data information to obtain the environmental data similarity value; When the similarity value of the environmental data 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; Extracting the saved historical three-dimensional point cloud data and extracting the historical observation error vector in the saved historical three-dimensional point cloud data; The historical observation error vectors in the saved historical 3D point cloud data are averaged to obtain the observation error vector at the current i-th time node.

8. The landslide deformation collaborative monitoring system based on slope radar and 3D scanning is characterized by: The system comprises a memory and a processor, wherein the memory stores a program for collaboratively monitoring landslide deformation based on slope radar and three-dimensional scanning, and when the program is executed by the processor, the following steps are implemented: Setting marker points in the landslide monitoring area and measuring the three-dimensional coordinates of the marker points; At intervals of a preset first time period, the monitoring area is monitored by a preset slope radar, radar data at different time nodes are obtained, and the line-of-sight deformation of the monitoring area is obtained by processing; A second time period is preset at intervals, and the monitoring area is regularly scanned by a preset three-dimensional laser scanner to obtain three-dimensional point cloud data at different time nodes; The three-dimensional coordinates of the marker point are used as the spatial reference point, the sight line deformation is used as the constraint condition, and the three-dimensional point cloud data at different time nodes are fused to obtain the three-dimensional deformation field V of the landslide body; According to the three-dimensional deformation field V of the landslide body, the average deformation of the entire landslide monitoring area is obtained; If the average deformation is greater than the preset deformation threshold in the corresponding direction, a warning message is triggered and the preset management terminal is prompted.

9. The landslide deformation collaborative monitoring system based on slope radar and three-dimensional scanning according to claim 8 is characterized in that: The marking point is a device with a corner reflector installed on the top and a target ball installed on the bottom.

10. The landslide deformation collaborative monitoring system based on slope radar and three-dimensional scanning according to claim 8, characterized in that: After obtaining the three-dimensional point cloud data at different time nodes, the method further includes: Based on the preset 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; Dividing the landslide image into multiple sub-landslide images according to a preset grid size; Extracting elements and corresponding element features in the sub-landslide image; Compare and analyze the element features in the sub-landslide image with the preset element features to obtain the element feature similarity value; 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 to obtain the deleted element; Based on the deleted elements, the corresponding 3D points in the 3D point cloud data are determined and deleted.

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