A rapid identification method for dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight

Abstract

The present invention discloses a method for rapidly identifying dangerous rock masses on steep slopes based on unmanned aerial vehicle (UAV) LiDAR (LiDAR) terrestrial flight. The method includes: using a UAV equipped with a camera and LiDAR system to conduct a survey flight over a target slope area; establishing a DOM image model of the slope to filter the resolved LiDAR point cloud data; combining the DOM image with perspective projection of the point cloud to perform preliminary interpretation and identification of the hazard volume; extracting typical geometric features corresponding to the roughness index, slope index, and structural surface orientation information based on the filtered ground point cloud; superimposing these geometric features on the corresponding DOM image of the slope; and interactively identifying dangerous rock masses by obtaining the DOM image of the rock slope surface and key geometric feature parameters based on the point cloud model. The introduction of the roughness index and slope index reduces the difficulty of identification, facilitates interactive identification of dangerous rock masses, and improves the accuracy and efficiency of identifying dangerous rock masses.

Description

Technical Field

[0001] The present invention belongs to the field of identification technology, and more specifically, relates to a method for quickly identifying dangerous rock masses on steep slopes based on unmanned aerial vehicle (UAV) LiDAR (Lidar) terrain simulation flight. Background Art

[0002] Visual identification is the primary application of DOM (Domain-of-Motion) imagery, generated using drone-acquired imagery and SfM (Simulation from Measurement) 3D reconstruction technology. This is then used to visually identify dangerous rock masses and extract their developmental characteristics. However, the accuracy and efficiency of current visual identification methods no longer meet engineering requirements. For one thing, factors such as weather, lighting, and camera angle during drone operations can reduce the quality of DOM images. Photogrammetry is less effective in modeling low-light areas such as cracks, steep slopes, and overhanging dangerous rock masses on rock slopes. Furthermore, subjective biases in the interpreter's judgment can also affect the accuracy of visual identification. Advances in detection methods have enabled image-based geological hazard interpretation to gradually evolve from visual identification to interactive human-computer interaction. However, due to the influence of weather, lighting, and camera angle, visual identification makes it difficult to directly determine the location of overhanging rock masses from images. Currently, there is a lack of a precise, efficient, and truly representative dangerous rock mass identification method that accurately reflects the topography and geometric characteristics of rock slopes. Summary of the Invention

[0003] To address the aforementioned shortcomings and improvements in existing technologies, the present invention provides a method for rapidly identifying dangerous rock masses on steep slopes based on unmanned aerial vehicle (UAV) LiDAR (LiDAR) terrain simulation. By acquiring high-precision DOM (Domain Mapping) images and a three-dimensional point cloud model, geometric characteristic parameters of dangerous rock masses, such as outcrop surface roughness and slope, are quantitatively extracted from the point cloud model as supplementary information for the DOM image, enabling interactive human-computer interaction in identifying dangerous rock masses.

[0004] The technical solution of the present invention relates to a method for quickly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight, which comprises the following steps:

[0005] S100: Use drones equipped with cameras and LiDAR systems to conduct survey flights over target slope areas.

[0006] S200: Based on the SfM 3D reconstruction technology, a DOM image model of the slope is established based on the acquired UAV image data;

[0007] S300: Using an analog filtering algorithm, the solved LiDAR point cloud data is filtered to remove building and vegetation point clouds, retaining the ground point cloud that reflects the ground undulation characteristics;

[0008] S400: Based on the slope DOM image, combined with point cloud perspective projection, preliminary interpretation and identification of the hazard body is performed according to the position of the projection shadow;

[0009] S500: The roughness index is introduced to reflect the unevenness of the outcrop slope; the slope index is introduced to reflect the location and scale of the suspended dangerous rock blocks. Based on the filtered ground point cloud, the typical geometric features corresponding to the roughness index, slope index and structural surface occurrence information are extracted;

[0010] S600: superimposing the typical geometric features corresponding to the roughness index and slope index obtained in step S500 onto the corresponding slope DOM image;

[0011] S700: By acquiring the DOM image of the rock slope and key geometric feature parameters based on the point cloud model, human-computer interactive identification of dangerous rock masses is achieved.

[0012] Furthermore, in step S100, the UAV adopts terrain-simulating flight, and the operation steps are as follows:

[0013] First, for the high-steep slope research area, a flight area was delineated, and a terrain-simulating flight route was planned based on the regional DSM. During the flight operation, the existing three-dimensional surface data DSM was used to keep the drone at a constant altitude above the ground target, autonomously adjust the flight altitude according to the terrain height, and perform multi-angle scanning on the obstructed areas of the protruding surface; a ground base station was set up and connected to the aircraft; and the drone executed the route mission to obtain the original data.

[0014] Furthermore, the simulation filtering algorithm in step S300 adopts the "cloth" simulation filtering algorithm, namely the CSF method, and the specific method is as follows:

[0015] First, the 3D point cloud is inverted. Then, by simulating cloth covering the inverted point cloud, an approximate surface shape is generated based on the position of the "cloth" surface. Finally, the distance between the points in the original point cloud data and the generated "cloth" surface is compared to identify ground points from the original point cloud data and separate non-ground points.

[0016] Furthermore, the roughness index γ in step S500 is calculated as follows:

[0017] The surface roughness index is the standard deviation of the point cloud elevation. The surface roughness of point i is calculated within a sampling window with a radius of 1.0m and the current point i as the center. The definition is as follows:

[0018]

[0019] Where z is the elevation of the sampling point within the sampling window, z' is the average elevation of all points within the sampling window, and n is the number of points in the sampling window.

[0020] The calculation of the surface roughness index is based on the elevation deviation calculation performed by fitting the elevation model within a moving local sampling window (radius 1.0 m).

[0021] Furthermore, the slope index θ in step S500 is calculated as follows:

[0022] The formula is obtained based on the plane normal vector of each point in the point cloud

[0023]

[0024] Where Nx, Ny, and Nz are the components of the plane normal vector.

[0025] Furthermore, in step S500, the method for obtaining the information of the structural surface occurrence is as follows:

[0026] The filtered ground point cloud is semi-automatically identified and clustered by DSE software to identify discontinuous structural surfaces. This is to identify point clusters with the same structural surface characteristics, determine the structural surface point clusters, color each point according to the clustered structural surface, and record the results of the dip and inclination as well as the proportion of each group of structural surfaces.

[0027] Furthermore, the specific method of step S600 is as follows: the typical geometric features obtained in step S500 and the corresponding slope DOM image are placed in the same spatial coordinate system, and the corresponding coordinates of the typical geometric features are superimposed on the corresponding slope DOM image in the form of orthographic projection.

[0028] Furthermore, in step S700, by obtaining the DOM image of the rock slope and key geometric feature parameters based on the point cloud model, the method for human-computer interactive identification of dangerous rock masses is as follows:

[0029] Based on DOM images, by introducing parameters such as roughness and slope that reflect the geometric characteristics of dangerous rock masses, human-computer interactive identification of suspended dangerous rock masses is carried out: for the steep slope formed after the rear edge separates from the parent rock, it can be judged through visual identification of DOM images; the areas with sudden changes in local roughness and suspended surfaces in adjacent areas are preliminarily identified as suspended dangerous rock masses; areas with sudden changes in roughness in a narrow and long distribution can be identified as steep slopes or cracks on the rear wall of the dumping dangerous rock mass.

[0030] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0031] In high-resolution 3D point clouds, dangerous rock masses are identified by the surface roughness of outcropping slopes. Dangerous rock masses often have higher surface roughness in certain areas. The slope can intuitively indicate the location of partially overhanging rock masses. The introduction of the roughness index and slope index reduces the difficulty of identification, facilitates human-computer interaction in dangerous rock mass identification, and improves the accuracy and efficiency of dangerous rock mass identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1F106 fault of the preferred embodiment of the present invention;

[0033] Figure 2 This is a DOM image of the study area before instability in a preferred embodiment of the present invention;

[0034] Figure 3 The CSF method is used to separate ground points and non-ground points in a preferred embodiment of the present invention.

[0035] Figure 4 This is the front DOM image of the dangerous rock mass in the preferred embodiment of the present invention;

[0036] Figure 5 This is a side DOM image of a dangerous rock mass in a preferred embodiment of the present invention;

[0037] Figure 6 It is a three-dimensional point cloud perspective projection of the dangerous rock mass in a preferred embodiment of the present invention;

[0038] Figure 7 A three-dimensional point cloud perspective projection of another perspective of the dangerous rock mass in a preferred embodiment of the present invention;

[0039] Figure 8 This is a schematic diagram of the right cross section of the iron ore in a preferred embodiment of the present invention (the white lines in the figure are cross-section lines);

[0040] Figure 9 for Figure 8 Horizontal distance-elevation diagram of the section shown by the center line;

[0041] Figure 10 The left cross-section diagram of the iron ore in the preferred embodiment of the present invention is selected (the white lines in the figure are cross-section lines);

[0042] Figure 11 for Figure 10 Horizontal distance-elevation diagram of the section shown by the center line;

[0043] Figure 12 The schematic diagram of the cross-section of the iron ore in the preferred embodiment of the present invention is selected (the white lines in the figure are hatching);

[0044] Figure 13 for Figure 12 Horizontal distance-elevation diagram of the section shown by the center line;

[0045] Figure 14 Schematic diagram of the research area of iron ore in a preferred embodiment of the present invention;

[0046] Figure 15 Schematic diagram of the distribution of structural surface point clusters in a preferred embodiment of the present invention;

[0047] Figure 16Schematic diagram of surface roughness index extraction in a preferred embodiment of the present invention;

[0048] Figure 17 This is a schematic diagram of the slope of the outcrop slope in a preferred embodiment of the present invention;

[0049] Figure 18 Schematic diagram of extraction of suspended surface of protruding rock mass in a preferred embodiment of the present invention;

[0050] Figure 19 Schematic diagram of human-computer interaction results based on DOM images and geometric feature parameters in a preferred embodiment of the present invention;

[0051] Figure 20 Flowchart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0053] The following is an explanation of rock mass identification using iron ore as an example:

[0054] Combining geological data with UAV survey results, it was determined that there are three main factors inducing the instability of Dongshantou: continuous blasting disturbance; the oblique intersection of the step and the controlling fault; and stress concentration at the corner of the step after wall expansion.

[0055] Under the continued influence of blasting disturbances in the future, the stability of the Dongshantou slope will remain poor. Therefore, further investigation of the dangerous rockfall hazards on the Dongshantou slope is urgently needed.

[0056] The embodiment of the present invention provides a method for quickly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight. Figure 1 , the method mainly includes the following steps:

[0057] S100: Use drones equipped with cameras and LiDAR systems to conduct survey flights over target slope areas.

[0058] Given the risk of rockfall on the Dongshantou slope, a high-precision 3D laser scanning of the unstable area of Dongshantou was conducted using a DJI M300 RTK equipped with an AlphaAir450 LiDAR. The flight parameters for this operation were: a 60-meter altitude, 60% heading overlap, 50% lateral overlap, 8 m / s speed, 3 echoes, and a sampling rate of 720,000 points / s. A base station was set up at any point and connected to the user's CORS station via the network to collect accurate base station coordinates. The aircraft is capable of RTK / PPK fusion differential operation, acquiring high-precision point-of-sight (POS) data. Once the position and orientation of the LiDAR system are determined, the 3D position of the laser spot can be uniquely determined. Based on this, the distance from the laser ranging system to the target and the system's attitude parameters were calculated to generate a 3D point cloud of the Dongshantou slope.

[0059] S200: Based on the SfM 3D reconstruction technology, a DOM image model of the slope is established based on the acquired UAV image data;

[0060] S300: Using an analog filtering algorithm, the solved LiDAR point cloud data is filtered to remove building and vegetation point clouds, retaining the ground point cloud that reflects the ground undulation characteristics;

[0061] The top of the Dongshantou slope is densely covered with vegetation, necessitating filtering of the raw point cloud data to separate ground points from non-ground points. Given the steep slope of the Dongshantou slope, a "cloth" simulation filtering algorithm (CSF method) was employed to filter and separate ground points from non-ground points. Filtering, as the first step in point cloud processing, is crucial for the accuracy of subsequent geometric feature extraction.

[0062] The "cloth" simulation filtering algorithm first inverts the 3D point cloud, then simulates cloth covering the inverted point cloud, generates an approximate surface shape based on the position of the "cloth" surface, and finally compares the distance between the points in the original point cloud data and the generated "cloth" surface to identify the ground points from the original point cloud data ( Figure 3 medium-light points) and separate non-ground points ( Figure 3 medium dark point).

[0063] Isolated trees and sparse vegetation are usually easily removed by applying an automatic filtering algorithm and manual thinning. Steeper slopes may require some trial and error in the filter parameters.

[0064] S400: Based on the slope DOM image, combined with point cloud perspective projection, preliminary interpretation and identification of the hazard body is performed according to the position of the projection shadow;

[0065] Please refer to Figures 4 to 7By using perspective projections of DOM images and 3D point clouds, we can make a preliminary assessment of the scale of potential rockfall hazards. Comparing drone images, the perspective projections of DOM images and 3D point clouds reveal some distinct dangerous rock masses. The rear wall of dangerous rock mass #I is separated from the parent rock, forming a wide rear wall gap. Cracks are present at the base of the dangerous rock mass, but no signs of fracturing or fragmentation are observed. Under blasting disturbance, strongly weathered laminar soil is filling the rear wall cracks, posing the risk of the dangerous rock mass collapsing. Dangerous rock masses #II and #III are irregular blocks with concave cavities at their lower portions. Under sustained external forces, they could collapse along the rear edge cracks.

[0066] S500: The roughness index is introduced to reflect the unevenness of the outcrop slope. The slope index is introduced to reflect the location and scale of the overhanging dangerous rock blocks. Based on the filtered ground point cloud, typical geometric features are extracted.

[0067] Please refer to Figures 8 to 13 The size information of the dangerous rock mass was extracted through the cross-section analysis of the 3D point cloud model: Figures 8 and 9 、 Figures 12 and 13 The cross section shows that the crack depth of the back wall of the #I dangerous rock mass is filled to 1.9m by the flowing soil above, the width between the back wall of the dangerous rock mass and the parent rock is 1.12m, the overall height of the #I dangerous rock mass is 12.4m, and the horizontal width is 18.9m. Figures 10 to 13 Cross-section view: The overall height of dangerous rock mass #II is 6.8m, the depth of the cavity is 3.1m, and the horizontal width is 27.9m.

[0068] Based on DOM images, it was determined that the dangerous rock mass on the outcrop of the rock slope was densely distributed, and the outcrop location was delineated as the research area. Please refer to Figure 14 , typical geometric features are extracted, including structural surface features, roughness index and slope index.

[0069] Structural surface information: From the perspective of typical failure types of rock slopes, structural surface characteristics and their combination relationships often play a controlling role in rock mass stability. The filtered ground point cloud is semi-automatically identified by DSE software for discontinuous structural surfaces and clustered to identify point clusters with the same structural surface characteristics. The J1, J2, J3, and J4 structural surface point clusters are determined, and each point is colored according to the clustered structural surface. Please refer to Figure 15 The results of the dip and inclination as well as the proportion of each structural surface are shown in Table 1. The point cluster J1 with the largest proportion represents the orientation of the entire slope surface, while the proportions of the other point clusters are relatively low, indicating that the structural effect of the Dongshantou slope rock mass is not obvious.

[0070] Table 1

[0071] Table 2Four sets of discontinuity and their characteristics

[0072]

[0073] The Dongshantou dangerous rock mass is mostly protruding from the slope. Most of the protruding rock masses are irregular in shape and have uneven edges. The roughness index is introduced to reflect the uneven characteristics of the outcrop slope. The surface roughness index is the standard deviation of the point cloud elevation. The surface roughness of point i is calculated within a sampling window with a radius of 1.0m and the current point i as the center.

[0074]

[0075] Where z is the elevation of the sampling point within the sampling window, z' is the average elevation of all points within the sampling window, and n is the number of points in the sampling window.

[0076] Slope is the most important parameter in landslide analysis because it directly affects slope stability. For rock slopes, slope can intuitively indicate the location of overhanging rock blocks. However, due to factors such as weather, lighting, and camera angle, it is difficult to directly determine the location of overhanging rock blocks from DOM images through visual identification.

[0077] The slope index of each point in the point cloud is calculated using the following formula. The surface roughness index is detected based on the eigenvalue ratio of the PCA method (principal component analysis), the geomorphological characteristics of the dangerous rock mass are extracted, and the slope changes in the local neighborhood are extracted based on the 3D point cloud.

[0078]

[0079] Where Nx, Ny, and Nz are the components of the plane normal vector.

[0080] S600: Based on the roughness index and slope of the point cloud model extracted in the previous step within a specific interval, which are used to reflect the unevenness characteristics of the outcrop slope and the position of the overhanging surface, the roughness and slope information of the specific interval are superimposed on the DOM image;

[0081] S700: By acquiring the DOM image of the rock slope and key geometric feature parameters based on the point cloud model, human-computer interactive identification of dangerous rock masses is achieved.

[0082] The surface roughness index is calculated based on the elevation deviation calculation of the elevation model fitted within a moving local sampling window (radius 1.0m). Most dangerous rock masses are more prominent than adjacent relatively stable rock masses. Based on this characteristic, dangerous rock masses can be identified by the surface roughness of the outcrop slope in high-resolution 3D point clouds. Dangerous rock masses often have higher surface roughness locally, so the roughness distribution can be used to directly reflect the edge of the dangerous rock mass. Please refer to Figure 16 .

[0083] Please refer to the slope calculated based on the 3D point cloud model of the outcrop slope. Figure 17 ,From the observation of the slope field of the point cloud model from three perspectives, it is found that the position and scale of the suspended rock mass can be intuitively reflected by extracting the slope interval from –60° to 60°. Figure 18 , Figure 18 The darker area is where the overhanging rock mass reflected by the slope is located.

[0084] It is difficult to identify overhanging dangerous rock masses of varying sizes that protrude from the slope surface through visual identification of DOM images alone. Based on DOM images, by introducing parameters such as roughness and slope that reflect the geometric characteristics of the dangerous rock mass, human-computer interactive identification of overhanging dangerous rock masses is performed: the rear edge of dangerous rock mass #II forms a steep slope after separating from the parent rock, which can be identified through visual identification of DOM images. Please refer to Figure 4 ; For the dangerous rock mass group #I with irregular shape and concave cavity formed at the bottom, it is difficult to identify it only through DOM image. It is necessary to combine the parameters reflecting the geometric characteristics of the dangerous rock mass, such as roughness and slope, to judge. Please refer to Figure 19 The black dashed area shows areas with sudden local roughness changes and adjacent overhanging surfaces, preliminarily identified as overhanging dangerous rock masses. The distribution of these overhanging dangerous rock masses is nearly parallel to the F106 fault plane. Comparing these with drone-generated outcrop photographs from various time periods (some of which allow for the identification of isolated rock masses through shadows), the dangerous rock masses identified using this method closely reflect the actual situation of dangerous rock masses on outcrop slopes.

[0085] By obtaining the DOM image of the rock slope and the key geometric characteristic parameters based on the point cloud model, we proposed a human-computer interactive dangerous rock mass identification method based on multi-source data fusion: For local roughness mutations and the presence of suspended surfaces in adjacent areas, they are identified as suspended dangerous rock masses. Please refer to Figure 19 The area enclosed by the black dotted line; for the sudden change in roughness with a narrow distribution that forms a surface, it is identified as the back wall scarp (crack) of the dumping type dangerous rock mass, please refer to Figure 19 The area enclosed by the red dotted line.

[0086] This invention introduces roughness and slope indices to reduce identification difficulty, facilitating human-computer interaction in identifying dangerous rock masses. In high-resolution 3D point clouds, dangerous rock masses are identified by the surface roughness of the outcropping slope, while the slope index can intuitively reflect the location of partially suspended rock blocks.

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for quickly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight, characterized by: It includes the following steps: S100: Use drones equipped with cameras and LiDAR systems to conduct survey flights over target slope areas. S200: Based on the SfM 3D reconstruction technology, a DOM image model of the slope is established based on the acquired UAV image data; S300: Using an analog filtering algorithm, the solved LiDAR point cloud data is filtered to remove building and vegetation point clouds, retaining the ground point cloud that reflects the ground undulation characteristics; S400: Based on the slope DOM image, combined with point cloud perspective projection, preliminary interpretation and identification of the hazard body is performed according to the position of the projection shadow; S500: The roughness index is introduced to reflect the unevenness of the outcrop slope; the slope index is introduced to reflect the location and scale of the suspended dangerous rock blocks. Based on the filtered ground point cloud, the typical geometric features corresponding to the roughness index, slope index and structural surface occurrence information are extracted; S600: superimposing the typical geometric features corresponding to the roughness index and slope index obtained in step S500 onto the corresponding slope DOM image; S700: By acquiring the DOM image of the rock slope and key geometric feature parameters based on the point cloud model, human-computer interactive identification of dangerous rock masses is achieved.

2. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized by: In step S100, the UAV performs a terrain-simulating flight, and the operation steps are as follows: First, for the high-steep slope research area, a flight area was delineated, and a terrain-simulating flight route was planned based on the regional DSM. During the flight operation, the existing three-dimensional surface data DSM was used to keep the drone at a constant altitude above the ground target, autonomously adjust the flight altitude according to the terrain height, and perform multi-angle scanning on the obstructed areas of the protruding surface; a ground base station was set up and connected to the aircraft; and the drone executed the route mission to obtain the original data.

3. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized in that: In step S300, the simulation filtering algorithm adopts the "cloth" simulation filtering algorithm, namely the CSF method, and the specific method is as follows: First, the 3D point cloud is inverted. Then, by simulating cloth covering the inverted point cloud, an approximate surface shape is generated based on the position of the "cloth" surface. Finally, the distance between the points in the original point cloud data and the generated "cloth" surface is compared to identify ground points and separate non-ground points from the original point cloud data.

4. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized by: The roughness index γ in step S500 is calculated as follows: The surface roughness index is the standard deviation of the point cloud elevation. The surface roughness of point i is calculated within a sampling window with a radius of 1.0m and the current point i as the center. The definition is as follows: Where z is the elevation of the sampling point within the sampling window, z' is the average elevation of all points within the sampling window, and n is the number of points in the sampling window; The calculation of the surface roughness index is based on the elevation deviation calculation performed by fitting the elevation model within a moving local sampling window with a radius of 1.0 m.

5. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized in that: The calculation method of the slope index θ in step S500 is as follows: The formula is obtained based on the plane normal vector of each point in the point cloud Where Nx, Ny, and Nz are the components of the plane normal vector.

6. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized in that: In step S500, the method for obtaining the information of the structural surface occurrence is as follows: The filtered ground point cloud is semi-automatically identified and clustered by DSE software to identify discontinuous structural surfaces in it, so as to identify point clusters with the same structural surface characteristics, determine the structural surface point clusters, color each point according to the clustered structural surface, and record the results of inclination and dip as well as the proportion of each group of structural surfaces.

7. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized in that: The specific method of step S600 is as follows: the typical geometric features obtained in step S500 and the corresponding slope DOM image are placed in the same spatial coordinate system, and the corresponding coordinates of the typical geometric features are superimposed on the corresponding slope DOM image in the form of orthographic projection.

8. The method for rapidly identifying dangerous rock masses on steep slopes based on UAV LiDAR terrain simulation flight according to claim 1 is characterized by: In step S700, by obtaining the DOM image of the rock slope and the key geometric characteristic parameters based on the point cloud model, the human-computer interactive method for identifying dangerous rock masses is as follows: Based on DOM images, by introducing parameters such as roughness and slope that reflect the geometric characteristics of dangerous rock masses, human-computer interactive identification of suspended dangerous rock masses is carried out: for the steep slope formed after the rear edge separates from the parent rock, it can be judged through visual identification of DOM images; the areas with sudden changes in local roughness and suspended surfaces in adjacent areas are preliminarily identified as suspended dangerous rock masses; areas with sudden changes in roughness in a narrow and long distribution can be identified as steep slopes or cracks on the rear wall of the dumping dangerous rock mass.

Images