Landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography

Abstract

The landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography disclosed by the application obtains orthographic images and digital surface models through unmanned aerial vehicle oblique photography, and identifies landslide range and surface cracks; airborne radar scanning is used to construct a landslide digital elevation model; combined with three-dimensional scanning of exploratory wells / excavation surfaces, the soil layer thickness is determined; and then, through fusion calculation of multi-source data, stability parameters are calculated, intelligent early warning of landslide disasters is realized, and the precision, efficiency and automation level of landslide monitoring are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster monitoring, in particular to a landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography. BACKGROUND

[0002] Traditional landslide monitoring methods mostly rely on manual patrol, single-point sensors or single remote sensing technology, which have problems such as low efficiency, narrow coverage, insufficient accuracy, etc.; oblique photography can obtain high-resolution ground images, but it is difficult to penetrate vegetation or surface cover; airborne radar can obtain three-dimensional ground clouds, but it cannot effectively identify surface cracks and soil structure when used alone. The existing technology lacks a comprehensive monitoring system of multi-source data fusion and intelligent analysis, resulting in insufficient accuracy and timeliness of landslide warning. SUMMARY

[0003] In order to solve at least one of the above technical problems, the purpose of the present application is to provide a landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography, which can realize intelligent early warning of landslide disasters and improve the accuracy, efficiency and automation level of landslide monitoring.

[0004] The present application provides a landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography, comprising:

[0005] Obtain disaster area images by unmanned aerial vehicle oblique photography, construct orthographic projection images and digital surface models, intelligently identify based on orthographic images, extract landslide influence range and ground crack plane distribution map;

[0006] Perform airborne radar three-dimensional scanning on the landslide influence range, obtain landslide area point cloud data, and construct a landslide digital elevation model in combination with the digital surface model;

[0007] Obtain three-dimensional scanning data of the exploration well / excavation surface in the landslide area, and construct an exploration well / excavation surface digital elevation model in combination with the landslide digital elevation model;

[0008] Determine crack parameters by GNSS landslide crack testing and landslide profile mapping, the crack parameters at least including crack length, width and strike;

[0009] Extract the thickness of different soil qualities in the exploration well / excavation surface digital elevation model; comprehensively analyze the thickness of different soil qualities and crack parameters to evaluate the stability parameters of the current landslide geology;

[0010] If the stability parameters of the current landslide geology are less than a preset first stability parameter threshold, an alarm information is triggered.

[0011] In the present application, the step of obtaining disaster area images by unmanned aerial vehicle oblique photography and constructing a digital surface model specifically comprises:

[0012] Based on the unmanned aerial vehicle with five camera, obtain multi-view image set , each image i contains internal and external orientation elements: photographic center coordinates And rotation matrix ;

[0013] By matching the preset feature points, the connection points between images are established, and all parameters are optimized by adopting bundle block adjustment, for any image point And its corresponding object point , the collinear equation is satisfied: , wherein Is the scale factor, f is the focal length; Indicates a 3*3 rotation matrix, Indicates the photographic center three-dimensional coordinate vector; the objective function of adjustment optimization is: ;

[0014] Based on the minimum energy function, the disparity map is determined, and the dense point cloud is generated according to the photographic set ;

[0015] The dense point cloud is interpolated into the preset regular network to obtain a digital surface model.

[0016] In the scheme, the step of constructing the landslide digital elevation model specifically comprises:

[0017] The airborne LiDAR system measures the laser round-trip time , and combines the POS data to obtain the point cloud , wherein , wherein , Is the laser emission angle; Indicates the three-dimensional coordinates of the GNSS antenna phase center, R indicates the laser ranging value, Indicates the attitude rotation matrix provided by the inertial measurement unit,

[0018] The radar point cloud And the point cloud in the digital surface model Are registered to obtain a transformation matrix T;

[0019] Based on the preset cloth simulation filtering algorithm, the ground points are separated, the preset point cloud height is Z, the simulated cloth node height is , by iteratively solving , the ground point cloud Is obtained; wherein Indicates the terrain height, Indicates the maximum height difference threshold;

[0020] The ground point cloud The difference value is the grid DEM, and a landslide digital elevation model is obtained , wherein represents a grid point; represents an elevation value of the lth known ground point, represents a Kriging weight of the lth known point on the current grid point, and L represents the number of known ground points used for interpolation; represents an elevation value of the landslide digital elevation model at the grid point .

[0021] In the scheme, the step of obtaining three-dimensional scanning data of the exploration well / excavation surface in the landslide area and combining the landslide digital elevation model to construct a digital elevation model of the exploration well / excavation surface includes the following steps:

[0022] Based on the three-dimensional scanning data of the exploration well / excavation surface in the landslide area, the point cloud of the exploration well / excavation surface is determined .

[0023] The boundary point cloud set B of the exploration well / excavation surface on the DEM is extracted, and the point cloud of the exploration well / excavation surface is projected onto a profile plane along the well wall direction to obtain a two-dimensional elevation model of the profile ; the projection coordinates are , wherein v and w are base vectors of the profile plane, , and the origin is .

[0024] The profile elevation model is combined with the landslide digital elevation model to obtain a digital elevation model of the exploration well / excavation surface , and the formula is .

[0025] In the scheme, the step of extracting the thickness of different soil qualities in the digital elevation model of the exploration well / excavation surface includes the following steps:

[0026] The feature vector of each point cloud in the digital elevation model of the exploration well / excavation surface is extracted;

[0027] The feature vector of the point cloud is divided according to a preset rule to obtain a soil layer category;

[0028] The point cloud in the digital elevation model of the exploration well / excavation surface is divided according to different soil layer categories to construct a set of depth values of all points in the corresponding layer .

[0029] The thickness of the soil layer is set as H, and if the exploration well / excavation surface is vertical, the formula is , wherein represents the depth value of point a in the soil layer A; represents the thickness of the soil layer A;

[0030] If the exploration well / digging surface is not vertical, the inclination angle of the exploration well / digging surface is extracted , and the formula is .

[0031] In the scheme, the thickness of different soil, crack parameters are comprehensively analyzed, and the formula for evaluating the stability parameters of the current landslide geology is specific:

[0032] For each exploration well / digging surface, the cracks and corresponding crack parameters in the set region range are matched;

[0033] The exploration well / digging surface is numbered as b, and the stability parameters of the landslide where the exploration well / digging surface is located are set as , and the formula is , wherein represents the cohesion weighting coefficient of the soil layer A, N represents the set of all soil types, represents the length of the crack J in the set region range, represents the width of the crack J in the set region range; is the included angle between the extension direction of the crack J and the slope direction, and M is the set of cracks in the set region range;

[0034] After traversing all the exploration well / digging surfaces numbered, the stability parameter set of the landslide of the grid where all the exploration well / digging surfaces are located is obtained;

[0035] The stability parameter set of the landslide of the grid where all the exploration well / digging surfaces are located is calculated by averaging the values, and the stability parameters of the current landslide geology are obtained.

[0036] In the scheme, it also includes:

[0037] Any one of the stability parameters in the stability parameter set is compared and analyzed with the preset first stability parameter threshold value, and if the stability parameter is less than the preset first stability parameter threshold value, the grid where the corresponding stability parameter is located is the reference, and the stability parameters of the landslide in the adjacent grid are extracted;

[0038] The stability parameters of the landslide in the adjacent grid are compared and analyzed one by one with the preset second stability parameter threshold value, and if there is a stability parameter of the landslide in the adjacent grid less than the preset second stability parameter threshold value, a local warning information is triggered with the reference grid as the center and the adjacent grid;

[0039] The preset second stability parameter threshold value is greater than the preset first stability parameter threshold value.

[0040] In the scheme, it also includes:

[0041] According to the landslide influence range, the stability parameters of the landslide where the exploration well / digging surface is located in the grid where the landslide outer line is located are determined.

[0042] if the stability parameter of the landslide in which the exploration well / excavation surface is located in the grid in which the outer edge line of the landslide is located is greater than the first preset stability parameter threshold and less than the second preset stability parameter threshold, triggering the addition of the exploration well / excavation surface information;

[0043] based on the added exploration well / excavation surface information, expanding the outer edge line outward by a preset size based on the outer edge line of the landslide to obtain an expanded edge line;

[0044] determining an expanded grid area according to the expanded edge line and the grid line;

[0045] adding at least one set of exploration well / excavation surface in the expanded grid area to recalculate the stability parameter of the current landslide geology.

[0046] In the scheme, after obtaining the stability parameter of the landslide in which the exploration well / excavation surface is located, the following steps are further included:

[0047] obtaining the depth value h(t) of the exploration well / excavation surface at the current time node;

[0048] obtaining the depth value h(t-1) of the exploration well / excavation surface at the previous time node;

[0049] subtracting the depth value of the exploration well / excavation surface at the previous time node from the depth value of the exploration well / excavation surface at the current time node to obtain a depth difference value;

[0050] determining a stability parameter revision coefficient of the landslide in which the corresponding exploration well / excavation surface is located according to the preset depth range in which the depth difference value falls;

[0051] multiplying the stability parameter of the landslide in which the exploration well / excavation surface is located by the stability parameter revision coefficient of the landslide in which the corresponding exploration well / excavation surface is located to obtain the stability parameter of the landslide in which the current exploration well / excavation surface is located after revision.

[0052] In the scheme, the following steps are further included:

[0053] dividing the depth difference value by the time difference between the corresponding adjacent two time nodes to obtain a depth backfill rate;

[0054] if the depth backfill rate is greater than a preset depth backfill rate threshold, generating secondary landslide warning information in the grid in which the corresponding exploration well / excavation surface is located;

[0055] after traversing all exploration wells / excavation surfaces, determining the number of grids that trigger the secondary landslide warning information;

[0056] if the number of grids that trigger the secondary landslide warning information is greater than a preset number threshold, triggering the secondary landslide warning information of the entire monitoring area.

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

[0058] The orthographic image and the digital surface model are acquired through the oblique photography of the unmanned aerial vehicle, the landslide range and the surface crack are identified, the landslide digital elevation model is constructed by using the airborne radar scanning, the soil layer thickness is determined in combination with the three-dimensional scanning of the exploration well / excavation surface, and the stability parameters are calculated through the fusion of the multi-source data, so that the intelligent early warning of the landslide disaster is realized. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A flowchart of the landslide geological disaster intelligent monitoring method based on the airborne radar and the oblique photography is shown.

[0060] Figure 2 A schematic diagram of the landslide digital elevation model is shown.

[0061] Figure 3 A schematic diagram of the landslide grid is shown. DETAILED DESCRIPTION

[0062] In order to more clearly understand the above-mentioned purposes, features and advantages of the application, the 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 application and the features in the embodiments can be combined with each other without conflict.

[0063] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from those described herein, therefore, the protection scope of the application is not limited by the specific embodiments disclosed below.

[0064] Figure 1 A flowchart of the landslide geological disaster intelligent monitoring method based on the airborne radar and the oblique photography is shown.

[0065] As Figure 1 shown, the application discloses a flowchart of the landslide geological disaster intelligent monitoring method based on the airborne radar and the oblique photography, which comprises:

[0066] S101, acquiring the disaster area image through the oblique photography of the unmanned aerial vehicle, constructing the orthographic projection image and the digital surface model, intelligently identifying based on the orthographic image, and extracting the landslide influence range and the surface crack plane distribution map;

[0067] S102, performing the airborne radar three-dimensional scanning on the landslide influence range, acquiring the landslide area point cloud data, and constructing the landslide digital elevation model in combination with the digital surface model;

[0068] S103, acquire three-dimensional scanning data of the well / excavation face in the landslide area, and construct a digital elevation model of the well / excavation face by combining it with the landslide digital elevation model;

[0069] S104. Determine crack parameters by GNSS testing of landslide cracks and landslide profile mapping. The crack parameters include at least crack length, width and orientation.

[0070] S105, Extract the thickness of different soil types from the digital elevation model of the well / excavation face; Conduct a comprehensive analysis of the thickness and crack parameters of different soil types to assess the stability parameters of the current landslide geology;

[0071] S106, If the current stability parameters of the landslide geology are less than the preset first stability parameter threshold, an alarm message will be triggered.

[0072] According to an embodiment of the present invention, the landslide impact range is first efficiently delineated by oblique photography, then the landslide impact area is precisely scanned by guided radar, and then GNSS and well / excavation face scanning are used to obtain accurate parameters for key parts. Finally, all parameters are comprehensively evaluated to obtain the current landslide geological stability parameters.

[0073] Specifically, following a pre-set drone flight path, operators used drones to conduct five-lens oblique photography of the landslide area, acquiring images with an overlap rate exceeding 80%. These images were then processed using software such as ContextCapture to generate a 5-cm resolution orthophoto map (DOM) and a digital surface model (DSM), determining the landslide's impact area. Next, drones equipped with InSAR radar were deployed to scan the landslide's impact area, acquiring high-density point clouds. In CloudCompare software, the radar point clouds were registered with the DSM, and a cloth simulation filtering (CSF) algorithm was used to filter out tree and building point clouds, resulting in a clean "landslide digital elevation model (DEM)," as shown below. Figure 2 As shown, it reflects the true surface elevation. Under safe conditions, geologists set up at least one well / excavation trench within the gridded area to reveal the soil structure. Based on the point cloud reflection intensity (e.g., weak reflection for clay, strong reflection for gravel) and color (manually labeled different soil layer colors), a region growing segmentation algorithm is used to automatically divide the profile point cloud into soil layer types such as "plain fill layer" and "silty clay layer," and the vertical distribution range of each cluster is statistically analyzed. The lidar used is a D-LiDAR2200 model, whose point cloud data accuracy meets the requirements of ±5cm@100m horizontally, ±5cm@100m vertically, and a ground resolution of 3cm@150m.

[0074] According to an embodiment of the present invention, the step of acquiring disaster area images through UAV oblique photography and constructing a digital surface model specifically includes:

[0075] Using a drone equipped with a five-lens camera to acquire multi-view image sets Each image i contains interior and exterior orientation elements: coordinates of the camera center. and rotation matrix ;

[0076] Connection points between images are established by matching preset feature points, and all parameters are optimized using bundle adjustment for any image point. Its corresponding object point They satisfy the collinearity equation: ,in Here, f is the scaling factor, and f is the focal length. This represents a 3x3 rotation matrix. The objective function for adjustment optimization is: (The vector represents the three-dimensional coordinates of the camera center.) ;

[0077] Based on minimizing the energy function, a disparity map is determined, and a dense point cloud is generated from the photographic set. ;

[0078] The dense point cloud is interpolated onto a predefined rule network to obtain a digital surface model.

[0079] It should be noted that the numerical surface model is set as Its formula is , ,in Indicates the digital surface model at grid points The elevation values ​​at the location, i and j represent the row and column indices of the DEM grid. The search domain is defined as the set of original point cloud data that participates in the current grid point difference calculation, where k represents the search domain. The index of a certain original point within the region. This represents the weight of the k-th original point, which is inversely proportional to the squared distance. This represents the elevation value of the k-th original point. Represents grid points The horizontal distance to k original points; Represents extremely small positive numbers, such as This is to prevent the denominator from being zero and to ensure numerical stability; Indicates image index, ; Indicates the object point index. ; Indicates the first The object point at the th The actual measured coordinates of the image points on the image, in pixels; represents the image point coordinate calculated according to the current exterior orientation element and the object point coordinate.

[0080] According to the embodiment of the present application, the step of constructing the landslide digital elevation model specifically comprises:

[0081] The airborne LiDAR system measures the laser round-trip time , combined with the POS data to obtain the point cloud , wherein , wherein , is the laser emission angle; represents the three-dimensional coordinates of the GNSS antenna phase center, R represents the laser ranging value, represents the attitude rotation matrix provided by the inertial measurement unit,

[0082] The radar point cloud is registered with the point cloud in the digital surface model to obtain the transformation matrix T;

[0083] The ground points are separated based on a preset cloth simulation filtering algorithm, the preset point cloud height is Z, and the simulation cloth node height is , the ground point cloud is obtained by iteratively solving ; wherein represents the terrain height, represents the maximum height difference threshold;

[0084] The ground point cloud is subtracted from the digital surface model to obtain the landslide digital elevation model , wherein represents the grid point; represents the elevation value of the lth known ground point, represents the Kriging weight of the lth known point to the current grid point, and L represents the number of known ground points used for interpolation; represents the elevation value of the landslide digital elevation model at the grid point .

[0085] It should be noted that the corresponding laser ranging value R is calculated by the laser round-trip time and the speed of light, and the transformation matrix T satisfies: , wherein represents the number of corresponding point pairs used for registration, represents the i2th point (source point) in the radar point cloud, represents the nearest point corresponding to in the DSM point cloud.

[0086] According to the embodiment of the present application, the step of acquiring the three-dimensional scanning data of the exploratory well / excavation face in the landslide area and combining the landslide digital elevation model to construct the exploratory well / excavation face digital elevation model specifically comprises:

[0087] Based on the three-dimensional scanning data of the exploratory well / excavation face in the landslide area, the exploratory well / excavation face point cloud is determined ;

[0088] The boundary point cloud set B of the exploratory well / excavation face on the DEM is extracted, and the exploratory well / excavation face point cloud is projected along the well wall direction to the profile plane to obtain the two-dimensional elevation model of the profile The projection coordinates are ; and v, w are the profile plane base vectors, , and the origin is ;

[0089] The profile elevation model is combined with the landslide digital elevation model to obtain the exploratory well / excavation face digital elevation model , and the formula is .

[0090] It should be noted that represents the coordinates (u, v) of point k on the two-dimensional profile plane, (v, w) represents a set of orthogonal base vectors of the profile plane, represents the original coordinates of point k in the three-dimensional space, , and represents the selected origin coordinates on the profile plane.

[0091] According to the embodiment of the present application, the step of extracting the thickness of different soil qualities in the exploratory well / excavation face digital elevation model specifically comprises:

[0092] The feature vector of each point cloud in the exploratory well / excavation face digital elevation model is extracted;

[0093] The feature vector of the point cloud is divided according to the preset rule to obtain the soil layer category;

[0094] The point cloud in the exploratory well / excavation face digital elevation model is divided according to different soil layer categories to construct the set of depth values of all points corresponding to the layer ;

[0095] The soil layer thickness is set as H, and if the exploratory well / excavation face is vertical, the formula is , wherein represents the depth value of point a in the soil layer A; represents the thickness of the soil layer A;

[0096] If the exploratory well / excavation face is not vertical, the inclination angle of the exploratory well / excavation face is extracted , and the formula is .

[0097] It should be noted that the feature vector of the point cloud includes the color, reflection intensity, etc. of the corresponding point cloud, and the soil layer is divided into cultivated soil, silty clay and gravel layer through cluster analysis of the feature vector; different cohesion weighting coefficients are set between different soil layers, such as the cohesion weighting coefficient of the cultivated soil is set to 0.8.

[0098] According to the embodiment of the present application, the thickness and crack parameters of different soil are comprehensively analyzed, and the formula for evaluating the stability parameter of the current landslide geology is specific:

[0099] For each exploration well / digging surface, the cracks and corresponding crack parameters in the set region range are matched;

[0100] The exploration well / digging surface is numbered as b, and the stability parameter of the landslide where the exploration well / digging surface is located is set as , and the formula is , wherein represents the cohesion weighting coefficient of the soil layer A, N represents the set of all soil layer types, represents the length of the crack J in the set region range, represents the width of the crack J in the set region range; is the angle between the extension direction of the crack J and the slope direction, and M is the set of cracks in the set region range;

[0101] After traversing all the exploration wells / digging surfaces for numbering, the stability parameter set of the landslide of the grid where all the exploration wells / digging surfaces are located is obtained;

[0102] The numerical values in the stability parameter set of the landslide of the grid where all the exploration wells / digging surfaces are located are averaged to obtain the stability parameter of the current landslide geology.

[0103] It should be noted that, as shown in Figure 3 , the landslide affected area is divided into multiple grid regions, at least one exploration well / digging surface is arranged in each grid region, the crack extension direction and the corresponding crack direction, and the slope direction is the main slope direction of the entire landslide, such as the main slope direction of the landslide is 40 degrees; for example, the angle between the extension direction of the crack J and the slope direction is 5 degrees, and sin(5°)=0.087; , represents the corresponding weight coefficient.

[0104] According to the embodiment of the present application, it further comprises:

[0105] The stability parameter set is compared with the first stability parameter threshold, and if the stability parameter is less than the first stability parameter threshold, the grid where the corresponding stability parameter is located is the reference, and the stability parameters of the landslide in the adjacent grid are extracted.

[0106] comparing the stability parameters of the landslides in the adjacent grids with the preset second stability parameter threshold value one by one, if there is a stability parameter of a landslide in the adjacent grids less than the preset second stability parameter threshold value, triggering a local warning information with the reference grid as the center and the adjacent grids;

[0107] The preset second stability parameter threshold value is greater than the preset first stability parameter threshold value.

[0108] It should be noted that the preset second stability parameter threshold value is slightly greater than the preset first stability parameter threshold value, for example, the preset first stability parameter threshold value is set to 1.7, and the preset second stability parameter threshold value is set to 2.0; by setting two threshold values, the measurement error of individual stability parameters is prevented from misleading the entire monitoring system.

[0109] Further, the preset first stability parameter threshold value is dynamically adjusted according to the soil water content in the landslide image area, the higher the soil water content, the lower the corresponding preset first stability parameter threshold value, thereby reducing the warning threshold.

[0110] According to the embodiment of the present application, further comprising:

[0111] According to the landslide influence range, the stability parameter of the landslide in which the exploration well / excavation surface in the grid where the landslide outer edge line is located is determined;

[0112] If the stability parameter of the landslide in which the exploration well / excavation surface in the grid where the landslide outer edge line is located is greater than the preset first stability parameter threshold value and less than the preset second stability parameter threshold value, triggering the addition of exploration well / excavation surface information;

[0113] Based on the addition of exploration well / excavation surface information, the preset size is expanded outward based on the landslide outer edge line to obtain an expanded edge line;

[0114] According to the expanded edge line and the grid line, an expanded grid area is determined;

[0115] At least one set of exploration well / excavation surface is added in the expanded grid area to recalculate the stability parameter of the current landslide geology.

[0116] It should be noted that when the stability parameter is less than the preset first stability parameter threshold value, the current landslide is in a warning state, and in order to ensure safety, the exploration well / excavation surface cannot be excavated; when the stability parameter is greater than the preset second stability parameter threshold value, it means that the current landslide is very stable and does not need to be further explored; when the stability parameter is greater than the preset first stability parameter threshold value and less than the preset second stability parameter threshold value, it means that the current landslide is in a stable state, but it is not very clear, therefore, the precision can be improved by adding exploration well / excavation surface.

[0117] According to the embodiment of the present application, after obtaining the stability parameter of the landslide where the exploratory well / excavation surface is located, the following steps are further included:

[0118] Obtaining the depth value h(t) of the exploratory well / excavation surface at the current time node;

[0119] Obtaining the depth value h(t-1) of the exploratory well / excavation surface at the previous time node;

[0120] Subtracting the depth value of the exploratory well / excavation surface at the previous time node from the depth value of the exploratory well / excavation surface at the current time node to obtain a depth difference value;

[0121] According to the preset depth range where the depth difference value falls, a stability parameter revision coefficient corresponding to the landslide where the exploratory well / excavation surface is located is determined;

[0122] Multiplying the stability parameter of the landslide where the exploratory well / excavation surface is located by the stability parameter revision coefficient corresponding to the landslide where the exploratory well / excavation surface is located to obtain the stability parameter of the landslide where the exploratory well / excavation surface is located after revision.

[0123] It should be noted that by monitoring the exploratory well / excavation surface in real time, the depth change of the corresponding exploratory well / excavation surface is measured, and the exploratory well / excavation surface is taken as a point of view, which can intuitively reflect whether the current landslide soil is in a sliding state. For example, if the depth value at the current time node is significantly reduced compared to the depth value at the previous time node, it indicates that the soil above the current exploratory well / excavation surface is sliding downward, entering the exploratory well / excavation surface, and thus reducing the depth. Different depth difference values correspond to different stability parameter revision coefficients, and the greater the depth difference value, the smaller the corresponding stability parameter revision coefficient. The stability parameter revision coefficient is less than 1.

[0124] According to the embodiment of the present application, the following steps are further included:

[0125] Dividing the depth difference value by the time difference corresponding to the adjacent two time nodes to obtain a depth backfill rate;

[0126] If the depth backfill rate is greater than a preset depth backfill rate threshold, a secondary landslide warning information in the grid where the corresponding exploratory well / excavation surface is located is generated;

[0127] After traversing all exploratory wells / excavation surfaces, the number of grids triggering the secondary landslide warning information is determined;

[0128] If the number of grids triggering the secondary landslide warning information is greater than a preset number threshold, a secondary landslide warning information of the entire monitoring area is triggered.

[0129] It should be noted that the current landslide movement state is intuitively reflected by the depth backfill rate, for example, when the depth backfill rates corresponding to two grid inner exploration wells / excavation surfaces exceed the preset depth backfill rate threshold, a secondary landslide warning of the entire monitoring area is triggered.

[0130] The disclosed landslide geological disaster intelligent monitoring method based on airborne radar and oblique photography acquires orthophoto and digital surface model through oblique photography of the unmanned aerial vehicle, identifies landslide range and surface cracks; utilizes airborne radar scanning to construct a landslide digital elevation model; combines three-dimensional scanning of the exploration well / excavation surface to determine the soil thickness; and then calculates the stability parameters by fusing the multi-source data, thereby realizing intelligent early warning of the landslide disaster; and the application improves the precision, efficiency and automation level of landslide monitoring.

[0131] 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 the devices or units, which can be electrical, mechanical or other forms.

[0132] 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 embodiment scheme.

[0133] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0134] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by relevant hardware of program instructions, and the foregoing program can be stored in a computer readable storage medium, and the program performs the steps of 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 disk or an optical disk, and various media that can store program codes.

[0135] Alternatively, the integrated unit of the present application can also be stored in a computer readable storage medium if it is realized in the form of a software function module and sold or used as an independent product. 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 disk or an optical disk, and various media that can store program codes.

Claims

1. A method for intelligent monitoring of landslide geological hazards based on airborne radar and oblique photography, characterized in that, include: By acquiring images of the disaster area through oblique photography by drones, constructing orthophoto images and digital surface models, and intelligently identifying the landslide impact range and surface crack distribution map based on orthophoto images; Airborne radar is used to perform three-dimensional scanning of the landslide impact area to obtain point cloud data of the landslide area, and a digital elevation model of the landslide is constructed by combining the digital surface model. Obtain 3D scanning data of wells / excavation faces within the landslide area, and construct digital elevation models of wells / excavation faces by combining them with the landslide digital elevation model; The crack parameters are determined by GNSS testing of landslide cracks and landslide profile mapping. The crack parameters include at least crack length, width, and orientation. Extract the thickness of different soil types from the digital elevation model of the well / excavation face; conduct a comprehensive analysis of the thickness and crack parameters of different soil types to assess the stability parameters of the current landslide geology; If the current stability parameters of the landslide geology are less than the preset first stability parameter threshold, an alarm message will be triggered.

2. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 1, characterized in that, The steps for acquiring disaster area images through drone oblique photography and constructing a digital surface model specifically include: Using a drone equipped with a five-lens camera to acquire multi-view image sets Each image i contains interior and exterior orientation elements: coordinates of the camera center. and rotation matrix ; Connection points between images are established by matching preset feature points, and all parameters are optimized using bundle adjustment for any image point. Its corresponding object point They satisfy the collinear equations: ,in Here, f is the scaling factor, and f is the focal length. This represents a 3x3 rotation matrix. The objective function for adjustment optimization is: (The vector represents the three-dimensional coordinates of the camera center.) ; Based on minimizing the energy function, a disparity map is determined, and a dense point cloud is generated from the photographic set. ; The dense point cloud is interpolated onto a predefined rule network to obtain a digital surface model.

3. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 2, characterized in that, The steps for constructing the digital elevation model of the landslide specifically include: Airborne LiDAR systems measure laser round-trip time. Then, point cloud data is obtained by combining POS data. ,in ,in , This refers to the laser emission angle; This represents the three-dimensional coordinates of the GNSS antenna phase center, where R represents the laser ranging value. This represents the attitude rotation matrix provided by the inertial measurement unit. radar point cloud Point clouds in digital surface models Registration yields the transformation matrix T; Ground points are separated based on a preset cloth simulation filtering algorithm, with a preset point cloud height of Z and a simulated cloth node height of [missing value]. Solving iteratively , obtain ground point clouds ;in Indicates terrain elevation. Indicates the maximum elevation difference threshold; Ground point clouds The difference is a grid DEM, which yields the landslide digital elevation model. ,in Represents grid points; This represents the elevation value of the l-th known ground point. represents the kriging weight of the l-th known point to the current grid point, and L represents the number of known ground points used for interpolation; This indicates the digital elevation model of the landslide at the grid points. The elevation value at that location.

4. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 3, characterized in that, The steps of acquiring three-dimensional scanning data of the well / excavation face within the landslide area and constructing a digital elevation model of the well / excavation face in conjunction with the landslide digital elevation model specifically include: Based on the 3D scanning data of the wells / excavation faces within the landslide area, the point cloud of the wells / excavation faces was determined. ; Extract the boundary point cloud B from the DEM to determine the wellhead / excavation face, and then combine the point cloud of the wellhead / excavation face. Projecting along the well wall direction onto the profile plane yields a two-dimensional elevation model of the profile. ; Projected coordinates are Where v and w are the basis vectors of the profile plane. The origin; By combining the profile elevation model with the landslide digital elevation model, a digital elevation model of the well / excavation face is obtained. Its formula is .

5. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography as described in claim 4, characterized in that, The steps for extracting the thickness of different soil types in the digital elevation model of the well / excavation face specifically include: Extract the feature vector of each point cloud in the digital elevation model of the well / excavation face; The feature vectors of the point cloud are divided according to preset rules to obtain soil layer categories; The point cloud in the digital elevation model of the well / excavation face is divided according to different soil layer categories, and a set of depth values ​​of all points in the corresponding layer is constructed. ; Let the soil layer thickness be H. If the well / excavation face is vertical, the formula is: ,in This represents the depth value of point a in soil layer A; This indicates the thickness of soil layer A; If the well / excavation face is not vertical, then extract the dip angle of the well / excavation face. Its formula is .

6. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography as described in claim 5, characterized in that, The formula for comprehensively analyzing the thickness and crack parameters of different soil types to evaluate the stability parameters of the current landslide geology is as follows: For each exploration well / excavation face, match the cracks and corresponding crack parameters within the specified area; The well / excavation face is numbered 'b', and the stability parameter of the landslide where the well / excavation face is located is set as follows: Its formula is ,in Let N represent the cohesion weighting coefficient of soil layer A, and let N represent the set of all soil layer types. This indicates the length of crack J within the defined area. This indicates the width of crack J within the defined area; Let J be the angle between the direction of crack J's extension and the slope, and M be the set of cracks within the defined area. After traversing all wells / excavation faces and numbering them, the set of stability parameters for landslides in all grids containing wells / excavation faces is obtained; The stability parameters of the landslide are obtained by averaging the values ​​of the stability parameters of the grid containing all exploration wells / excavation faces.

7. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 6, characterized in that, Also includes: Compare any stability parameter in the stability parameter set with a preset first stability parameter threshold. If the stability parameter is less than the preset first stability parameter threshold, then take the grid where the corresponding stability parameter is located as the benchmark and extract the stability parameters of the landslide in the adjacent grid. The stability parameters of landslides in adjacent grids are compared and analyzed one by one with the preset second stability parameter threshold. If the stability parameters of landslides in adjacent grids are less than the preset second stability parameter threshold, a local warning message is triggered with the reference grid as the center and adjacent grids. The preset second stability parameter threshold is greater than the preset first stability parameter threshold.

8. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 7, characterized in that, Also includes: Based on the landslide's impact range, determine the stability parameters of the landslide where the exploration well / excavation face is located within the grid containing the outer edge of the landslide; If the stability parameter of the landslide where the well / excavation face is located within the grid of the outer edge of the landslide is greater than the preset first stability parameter threshold and less than the preset second stability parameter threshold, then the information of the well / excavation face will be added. Based on the addition of well / excavation face information, the outer edge of the landslide is expanded outward by a preset dimension to obtain the outer edge line; Determine the outer grid area based on the outer edge line and grid lines; At least one additional well / excavation face is added within the expanded grid area to recalculate the stability parameters of the current landslide geology.

9. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 6, characterized in that, After obtaining the stability parameters of the landslide where the exploratory well / excavation face is located, the following are also included: Obtain the depth value h(t) of the exploration well / excavation face at the current time node; Obtain the depth value h(t-1) of the exploration well / excavation face at the previous time node; The depth difference is obtained by subtracting the depth of the well / excavation face at the previous time point from the depth of the well / excavation face at the current time point. Based on the preset depth range into which the depth difference falls, determine the stability parameter revision coefficient of the landslide where the corresponding exploration well / excavation face is located; Multiply the stability parameter of the landslide where the well / excavation face is located by the corresponding stability parameter revision factor to obtain the revised stability parameter of the landslide where the current well / excavation face is located.

10. The intelligent monitoring method for landslide geological hazards based on airborne radar and oblique photography according to claim 9, characterized in that, Also includes: Divide the depth difference by the time difference between two adjacent time points to obtain the depth backfill rate; If the depth backfill rate is greater than the preset depth backfill rate threshold, a secondary landslide warning message is generated within the grid where the corresponding well / excavation face is located. After traversing all exploration wells / excavation faces, determine the number of grids that triggered secondary landslide warning messages; If the number of grids that trigger secondary landslide warnings exceeds a preset threshold, secondary landslide warnings will be triggered for the entire monitored area.

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