Method and system for judging instability mode of dangerous rock mass of high and steep slope

Abstract

The invention provides a high and steep slope dangerous rock mass instability mode judgment method and system, and belongs to the field of rock mass engineering, dangerous rock mass point cloud data of a target area is obtained, neighborhood point searching is performed on point clouds at different positions of a slope according to coordinate information in the dangerous rock mass point cloud data, plane fitting and coplanarity testing are performed on neighborhood points, and a dangerous rock mass instability mode judgment result is obtained. And merging the coplanar point sets, and calculating to obtain the inclination and the inclination angle of the structural plane according to the point sets. And clustering and grouping the structural surfaces, fitting the structural surfaces and performing intersection calculation. Point-surface calculation and analysis are carried out by combining topographic features of the high and steep slope, geometric morphology of the rock mass and development conditions of structural surfaces, the dangerous rock mass is positioned, the damage mode of the dangerous rock mass is identified, the judgment accuracy is improved, the damage mode of the slope rock mass is finely divided, and disaster prevention and reduction work of rockfall disasters of the high and steep slope is facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of rock mass engineering, and in particular relates to a method and system for judging the instability mode of dangerous rock masses on high and steep slopes. Background Art

[0002] Rock slopes confined and cut by structural surfaces often form dangerous rock masses with regional distribution characteristics. These rock masses are prone to instability under external disturbances such as rainfall and earthquakes, triggering major catastrophic geological disasters. Therefore, identifying dangerous rock masses and determining their potential failure modes are key steps in disaster prevention and mitigation in mountainous areas.

[0003] Traditional manual ground surveys and ground-based three-dimensional laser scanning devices often face problems such as height limitations and insufficient precision, which restricts the comprehensiveness and accuracy of dangerous rock mass surveys on steep slopes. With the rapid development of drone technology, drones equipped with sensors such as LiDAR can obtain high-precision point cloud data, providing a convenient and accurate solution for identifying the structural surface features of slope rock masses and identifying dangerous rock masses. For example, the invention patent with Chinese patent publication number CN111178214B provides a method for rapid identification of dangerous rock masses based on drone photography technology. This method generates a point cloud model from high-precision optical photographs, and after denoising and thinning, uses spatial clustering and support vector machine algorithms to fit the boundaries of dangerous rock masses, thereby achieving identification of dangerous rock masses and preliminary stability analysis.

[0004] Although existing survey technologies have obvious advantages in improving the speed and efficiency of dangerous rock mass identification, they often ignore topographic factors such as structural surface development, rock mass geometric characteristics, and slope inclination and dip, thereby limiting the precision and accuracy of dangerous rock mass identification and instability mode determination. Summary of the Invention

[0005] In order to improve the accuracy of dangerous rock mass identification and instability mode determination, the present invention provides a method and system for determining the instability mode of dangerous rock mass on a high and steep slope.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for determining the instability mode of a dangerous rock mass on a steep slope comprises the following steps:

[0008] Acquire point cloud data of dangerous rock masses in the target area, perform neighborhood point searches on point clouds at different locations on the slope based on coordinate information in the point cloud data of the dangerous rock masses to obtain different neighborhood point sets; perform a coplanarity test on the different neighborhood point sets and merge the coplanar point sets; set a point cloud search radius and extract the normal vector of the best-fitting plane within the search radius, and calculate the inclination and dip angle within the search radius of the point cloud of the target area based on the normal vector of the best-fitting plane within the search radius;

[0009] The centroid of the merged coplanar point set is translated to the origin, the normal vector of the optimal fitting plane of the translated point set is calculated, and the inclination and dip angle of the fitting plane are calculated based on the normal vector of the optimal fitting plane; the distance between all point clouds in the point set and the centroid is calculated, and the farthest distance is used as the radius of the structural surface trace length;

[0010] Clustering and grouping the structural surfaces according to the inclination and dip of the fitting plane to obtain structural surface grouping data; fitting a disk plane according to the centroid of the translated point set and the radius of the structural surface trace length to calculate the inclination and dip of the structural surface intersection line;

[0011] According to the inclination and dip angle within the point cloud search radius, the inclination and dip angle of the structural surface, and the inclination and dip angle of the structural surface intersection line, the dangerous rock mass in the target area is located and the failure mode of the dangerous rock mass is determined.

[0012] Preferably, performing a coplanarity test on the different neighborhood point sets and merging the coplanar point sets specifically includes the following steps:

[0013] Set the search radius or number of neighborhood points of the neighborhood, use the k-NN algorithm to search the neighborhood points of the point cloud at different positions of the slope, and obtain the neighborhood point set;

[0014] Iteratively calculate the distance from all points in the neighborhood point set to the fitting plane, set the distance threshold, and if the calculated d n Less than d t , set as an interior point, otherwise set as an exterior point; count the number of interior points and exterior points and calculate the number of iterations;

[0015] The optimal plane parameters of the neighborhood point set are obtained according to the cost function value; wherein the cost function is specifically:

[0016]

[0017] Among them, d n is the distance from the point to the fitting plane; d t is the distance threshold;

[0018] Calculate the angle between the normal vectors of the fitted planes of different neighborhood point sets using the following formula:

[0019]

[0020] When the cosine of the angle is close to 1, the two point sets are parallel; check the offset difference between the planes, specifically:

[0021] D ij =|D i -D j |;

[0022] If the offset is less than the set threshold, the two point sets are considered to be coplanar; if the point sets are coplanar, the coplanar point sets are merged; where N i (A i , B i , C i , D i ) is the optimal plane normal vector of the neighborhood point set i; N j (A j ,B j ,C j ,D j ) is the optimal plane normal vector of the neighborhood point set j; θ ij is the plane angle between i and j; D ij is the offset between the two planes;

[0023] Merge sets of coplanar points.

[0024] Preferably, the inclination and dip angle within the target area point cloud search radius are calculated using the following formula:

[0025]

[0026] Where θs is the inclination angle within the search radius; A, B, and C are the components of the normal vector on the x, y, and z axes, respectively; φs is the inclination within the search radius; A and B are the components of the normal vector on the x and y axes, respectively.

[0027] Preferably, the PSO particle swarm algorithm is used to cluster and group the structural surfaces.

[0028] Preferably, the failure modes of the dangerous rock mass include plane sliding, wedge sliding and bending collapse.

[0029] Preferably, the dangerous rock mass in the target area is located and the failure mode of the dangerous rock mass is determined based on the inclination and inclination angle within the point cloud search radius, the inclination and inclination angle of the structural surface, and the inclination and inclination angle of the intersection line of the structural surface. The identification formula of the plane sliding mode is specifically:

[0030]

[0031] The specific identification formula of the wedge sliding mode is:

[0032]

[0033] The identification formula of the bending and dumping mode is specifically:

[0034]

[0035] Where φs is the inclination within the search radius; φ is the inclination of the structural surface; θ is the inclination angle of the structural surface; is the internal friction angle of the slope; φi is the inclination of the structural surface intersection line; θi is the inclination angle of the structural surface intersection line.

[0036] The present invention also provides a system for determining the instability mode of dangerous rock masses on steep slopes, which specifically includes:

[0037] The data processing module is used to obtain the dangerous rock point cloud data of the target area, perform neighborhood point search on the point cloud at different positions of the slope according to the coordinate information in the dangerous rock point cloud data, and obtain different neighborhood point sets; perform coplanarity test on the different neighborhood point sets and merge the coplanar point sets; set the point cloud search radius and extract the normal vector of the best fitting plane within the search radius, and calculate the inclination and dip within the search radius of the target area point cloud according to the normal vector of the best fitting plane within the search radius.

[0038] The structural surface calculation module is used to translate the centroid of the merged coplanar point set to the origin, calculate the normal vector of the optimal fitting plane of the translated point set, and calculate the inclination and inclination of the fitting plane based on the normal vector of the optimal fitting plane; calculate the distance between all point clouds in the point set and the centroid, and take the farthest distance as the radius of the structural surface trace length.

[0039] The structural surface intersection line module is used to cluster the structural surfaces according to the inclination and inclination of the fitting plane to obtain structural surface grouping data; fit the disk plane according to the centroid of the translated point set and the radius of the structural surface trace length, and calculate the inclination and inclination of the structural surface intersection line.

[0040] The identification and positioning module is used to locate the dangerous rock mass in the target area and determine the damage mode of the dangerous rock mass according to the inclination and inclination angle within the point cloud search radius, the inclination and inclination angle of the structural surface and the inclination and inclination angle of the structural surface intersection line.

[0041] The present invention also provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the method for determining the instability mode of a dangerous rock mass on a high and steep slope.

[0042] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, the steps described in the method for determining the instability mode of a dangerous rock mass on a high and steep slope can be executed.

[0043] The method for determining the instability mode of dangerous rock masses on steep slopes provided by the present invention has the following beneficial effects:

[0044] The present invention obtains point cloud data of dangerous rock mass areas on steep slopes, performs point-to-surface calculations and analysis, performs neighborhood point search for plane fitting and coplanarity test, obtains the inclination and dip of structural surfaces and their intersections, and introduces the development characteristics of different structural surfaces. Set the point cloud search radius, calculate the inclination and dip within the point cloud search radius of the target area, and consider the influence of terrain factors of point clouds at different locations. Then cluster the structural surfaces, fit the structural surfaces, and perform intersection calculations. Based on the calculation and analysis results, the dangerous rock mass on the steep slope is located and the failure mode is determined, so as to achieve a fine division of the slope rock failure mode and provide more accurate information for rockfall protection projects, thereby providing strong support for the prevention and mitigation of rockfall disasters on steep slopes. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0046] Figure 1 The present invention is a flowchart of a method for determining the instability mode of a dangerous rock mass on a steep slope according to an embodiment of the present invention.

[0047] Figure 2 This is a distribution diagram of the structural surface intersection lines in an embodiment of the present invention.

[0048] Figure 3 is a distribution diagram of slope inclination and tendency in an embodiment of the present invention, wherein: Figure 3 (a) is the slope inclination distribution map, Figure 3 (b) is the inclination distribution diagram.

[0049] Figure 4 Schematic diagram of dangerous rock masses with different potential instability modes identified in an embodiment of the present invention, wherein: Figure 4 (a) is a schematic diagram of the dangerous rock mass in plane sliding mode. Figure 4 (b) is a schematic diagram of the dangerous rock mass in the wedge sliding mode. Figure 4 (c) is a schematic diagram of a dangerous rock mass in a bending and tipping mode. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] Example

[0052] Taking the high and steep slope in the dam site of A hydropower station as an example, the present invention provides a method for judging the instability mode of dangerous rock mass on high and steep slopes, such as Figure 1 As shown, the specific steps include:

[0053] Step 1: Obtain high-precision point cloud data of the study area.

[0054] A drone equipped with a LiDAR sensor was used to acquire slope point cloud data. Multi-angle oblique photogrammetry was conducted on steep slopes. To ensure the data met the requirements of subsequent analysis, a scanning mode with strong penetration and high point cloud density was selected during photogrammetry, and the scanning angle was appropriately set. Furthermore, repeated scanning was performed in areas with lower point cloud density to ensure the integrity and accuracy of the slope point cloud data. Subsequently, 3D reconstruction was performed based on the drone-generated point cloud data, generating high-precision point cloud data and a 3D reality model of the study area.

[0055] Step 2: Extract the coordinate information of the point cloud.

[0056] Load the LAS format point cloud data into software that can process point cloud format, and use the CloudComopare software tool to convert the LAS format point cloud data into ASCII text format containing the point cloud coordinate X, Y, and Z information.

[0057] Step 3: Perform a neighborhood point search on the study area.

[0058] Using the k-NN algorithm, set the search radius or number of neighborhood points to search for neighborhood points at different locations on the slope. This creates a set of neighborhood points within a certain range or number for subsequent point cloud feature extraction. In this example, the number of neighborhood points is set to 30 to complete the neighborhood point search.

[0059] Step 4: Fit planes to different neighborhood point sets and perform coplanarity tests.

[0060] Step 4.1: Select a sample set of s (s ≥ 3) point clouds from different neighborhood point sets and use the least squares method to obtain the preliminary fitting plane parameters N0 (A0, B0, C0, D0). The sample set selected has 6 point clouds.

[0061] Step 4.2: Assume that the coordinates of the points in the neighborhood point set are P(x n ,y n ,z n )(n=1,2,3,…,s).

[0062] According to formula (1), the distance from all points in the sample set to the fitting plane is calculated.

[0063]

[0064] Step 4.3: Set a distance threshold d t , if the calculated d n Less than d t , define it as an inner point, otherwise, define it as an outer point. Record the number of inner points and outer points. The distance threshold d is selected t is 0.3.

[0065] Step 4.4, repeatedly select s (s ≥ 3) sample sets and repeat the process of steps 4.2 and 4.3 to obtain more interior points and optimal plane parameters. Set the number of iterations k, and select the one with the minimum cost function value within the number of iterations k (i.e., F M The model with the smallest (maximum) is selected as the optimal model to obtain the optimal plane normal vector (A, B, C, D). The number of iterations can be calculated using the Monte Carlo probability method, specifically using formula (2). The cost function is calculated using formulas (3) and (4). The number of iterations k is set to 100.

[0066]

[0067] Where p is the probability that all s points in the sample set are inliers; w is the proportion of inliers in the sample set; and t is the number of random selections.

[0068]

[0069] Among them, d n is the distance from the point to the fitting plane; d t is the distance threshold.

[0070] Step 4.5: According to the above steps, the optimal plane parameter N of all neighborhood point sets can be obtained. i (A i , B i , C i , D i ). To determine whether different neighborhood point sets are coplanar, first use different neighborhood point sets to fit the angle between the plane normal vectors, using formula (5). If the cosine of the angle is close to 1, the two point sets are considered to be parallel. Set a low threshold, and use formula (6) to check the offset difference between the planes. If the offset is less than the set threshold, the two point sets are considered coplanar. If the point sets are coplanar, the coplanar point sets are merged. The offset threshold set in this example is 0.1.

[0071]

[0072] D ij =|D i -D j | (6)

[0073] Among them, N i (A i , B i , C i , D i ) is the optimal plane normal vector of the neighborhood point set i; N j (A j ,B j ,C j ,D j ) is the optimal plane normal vector of the neighborhood point set j; θ ij is the plane angle between i and j; D ij is the offset between the two planes.

[0074] Through the coplanarity test, a total of 4260 point sets were obtained

[0075] Step 5: Translate the obtained point set, obtain the normal vector of the translated point set, and convert the structural surface features including inclination, dip angle, and plane radius according to the normal vector.

[0076] Step 5.1: Subtract the average value of the point positions from the point set obtained in step 4 according to formulas (7)-(9), so that the center of mass of the point set is moved to the origin to eliminate the influence of translation, thereby focusing on the relative distribution of the point set and taking the average coordinate value as the center of mass of the plane.

[0077]

[0078] Among them, x pi 、y pi and z pi are the x-, y-, and z-axis coordinates of point pi; x′ pi , y′ pi and z′ pi are the coordinates of point pi after translation; n is the total number of points in the point cloud.

[0079] Step 5.2: Repeat steps 4.1-4.4 for the translated point set to obtain the optimal fitting plane and its normal vector N for the coplanar point set. Since the inclination of a plane is the angle between the projection of the plane's normal vector and true north, and the inclination of a plane is the angle between the plane's normal vector and the horizontal plane, the plane normal vector N of the point set can be used to convert the inclination according to formula (10) and the inclination according to formula (11).

[0080]

[0081] Where θ is the inclination angle, A, B, and C are the components of the plane normal vector on the x, y, and z axes respectively; is the projection length of the plane normal vector on the x and y planes.

[0082]

[0083] Where φ is the inclination, A and B are the components of the plane normal vector in x and y respectively.

[0084] The trace length of the structural surface, or the exposed length of the structural surface, is a key characteristic parameter for determining the instability mode of a dangerous rock mass. The distance from the centroid to all point clouds in the set is calculated, and the farthest distance is used as the radius of the structural surface trace length. Similar to step 4, this step acquires information on 4,260 structural surfaces.

[0085] Step 6: Based on the inclination and dip data of the fitted structural surfaces, a PSO particle swarm is defined to cluster and group structural surfaces with different developmental characteristics. This is beneficial for the inherent requirements of subsequent kinematic analysis. Structural surfaces in the same group have similar control effects on the direction and range of possible damage.

[0086] The PSO particle swarm algorithm is used to set the number of particle swarms, the number of cluster categories and the number of iterations to cluster and group the structural surface data obtained above. Based on the inclination and dip angle data of 4260 structural surfaces, the data with similar inclination and dip angles are summarized and divided into 3 groups. In this example, the number of particles is set to 20, the number of iterations is 50, the clustering is classified into 3 categories, and the above 4260 structural surfaces are divided into 3 groups. The three groups obtained represent the representative inclination and dip angle data of each group of structural surfaces. The representative data of the three groups obtained in the example are structural surface group 1: inclination 11.31° and inclination 40.03°; structural surface group 2 inclination 178.60° and inclination 88.63°; structural surface group 3 inclination 245.32° and inclination 88.22°

[0087] Step 7: Determine and extract the intersection lines of the structural surfaces.

[0088] Due to the wedge sliding and bending collapse failure modes of the structural surface, it is necessary to consider the mutual cutting of different structural surfaces.

[0089] Step 7.1: Fit a disk plane based on the centroid and radius obtained in step 5, use the Hessian operator to fit the structural surface and perform row intersection determination.

[0090] Step 7.2: Filter out the disc intersections that do not meet the normal intersection requirements, such as too many intersections, repeated intersections, etc. The intersections are fitted to the disc plane that intersects the two structural surfaces using the least squares method. The principal component analysis method is used to obtain the plane normal vector of the disc plane. The inclination (φi) and inclination (θi) of the plane are obtained according to formulas (10)-(11). The diameter of the disc is used as the size of the intersection line to obtain the data of the intersection of different structural surfaces. The inclination and inclination of the intersection lines of different structural surfaces are calculated, as shown in the following example: Figure 2 shown.

[0091] Step 8: Calculate the topographic factors of the slope.

[0092] Based on the distribution and distance of the point cloud in the study area, a search radius is set to calculate the terrain factors of the point cloud at different locations. The point cloud within the search radius is fitted into a plane using the least squares method, and the normal vector of the plane is obtained. Based on the plane normal vector and according to formulas (12) and (13), the inclination and dip angle within the search radius of the slope point cloud are calculated.

[0093]

[0094] Where θs is the inclination angle within the search radius; A, B, and C are the components of the normal vector on the x, y, and z axes, respectively.

[0095]

[0096] Where φs is the inclination within the search radius. If φs is a negative number, add 360° to it to ensure that the inclination range is 0ˉ360°. A and B are the components of the normal vector on the x and y axes, respectively.

[0097] Set the search radius to 2m and calculate the slope inclination and dip angle. Figure 3 shown.

[0098] Step 9: Identify the failure modes of dangerous rock masses based on different conditions and output the dangerous rock masses under different failure modes. Consider three common failure modes of dangerous rock masses: plane sliding, wedge sliding, and bending and toppling. The different identification conditions for the three are as follows:

[0099] Plane sliding: If the inclination of the structural surface is the same as that of the search radius, and its inclination is greater than the defined internal friction angle of the slope and less than the inclination within the search radius, then the rock mass at the location of the structural surface is prone to plane sliding. The identification formula is as follows:

[0100]

[0101] Where φs is the inclination within the search radius; φ is the inclination of the structural surface; θ is the inclination angle of the structural surface; is the internal friction angle of the slope.

[0102] Wedge sliding: If two sets of structural surfaces intersect, and the inclination of the intersection line is greater than the internal friction angle of the slope and less than the inclination within the search radius, and the inclination of the structural surface intersection line is the same as the inclination within the search radius, then the rock mass at the location of the structural surface is prone to wedge sliding. The identification formula is as follows:

[0103]

[0104] Among them, φs is the inclination within the search radius; φi is the inclination of the structural surface intersection line; θi is the inclination angle of the structural surface intersection line; is the internal friction angle of the slope.

[0105] Bending and toppling: If the structural surface is tilted against the search radius, the structural surface and the fitting plane of the search radius are almost the same or intersect at a small angle, and the inclination angle of the structural surface is greater than the internal friction angle of the slope, then the rock mass at the location of the structural surface is prone to bending and toppling. The identification formula is as follows:

[0106]

[0107] Where φs is the inclination within the search radius; φ is the inclination of the structural surface; θ is the inclination angle of the structural surface; θs is the inclination angle within the search radius; is the internal friction angle of the slope.

[0108] The friction angle of the slope is set to 30° and the lateral limit is set to 20°. The positioning results of the dangerous rock mass under different instability modes are as follows: Figure 4 shown.

[0109] The present invention also provides a system for determining the instability mode of dangerous rock masses on steep slopes, which specifically includes:

[0110] The data processing module is used to obtain the dangerous rock point cloud data of the target area, perform neighborhood point search on the point cloud at different positions of the slope according to the coordinate information in the dangerous rock point cloud data, and obtain different neighborhood point sets; perform coplanarity test on different neighborhood point sets and merge the coplanar point sets; set the point cloud search radius and extract the normal vector of the best fitting plane within the search radius, and calculate the inclination and dip angle within the search radius of the target area point cloud according to the normal vector of the best fitting plane within the search radius.

[0111] The structural surface calculation module is used to translate the centroid of the merged coplanar point set to the origin, calculate the normal vector of the optimal fitting plane of the translated point set, and calculate the inclination and dip angle of the fitting plane based on the normal vector of the optimal fitting plane; calculate the distance between all point clouds in the point set and the centroid, and take the farthest distance as the radius of the structural surface trace length.

[0112] The structural surface intersection line module is used to cluster and group structural surfaces according to the inclination and dip of the fitting plane to obtain structural surface grouping data; fit the disk plane according to the centroid of the translated point set and the radius of the structural surface trace length, and calculate the inclination and dip of the structural surface intersection line.

[0113] The identification and positioning module is used to locate the dangerous rock mass in the target area and determine the failure mode of the dangerous rock mass based on the inclination and dip angle within the point cloud search radius, the inclination and dip angle of the structural surface, and the inclination and dip angle of the intersection line of the structural surface.

[0114] Each module in the aforementioned system for determining the instability mode of a dangerous rock mass on a steep slope can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0115] The present invention also provides a computer device comprising a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of an embodiment of a method for determining the instability mode of a dangerous rock mass on a steep slope. The specific implementation method can be found in the method embodiment and will not be repeated here.

[0116] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, wherein a computer program is stored on the storage medium. For example, a memory containing instructions, the instructions can be executed by a processor of a computer device to complete the above method. For example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, it can implement the steps in an embodiment of a method for determining the instability mode of a dangerous rock mass on a steep slope. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0117] Those skilled in the art will appreciate that embodiments of the present invention may provide methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the present specification and examples have described the invention in detail, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the scope of protection of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any person familiar with the art within the technical scope disclosed in the present invention falls within the scope of protection of the present invention.

Claims

1. A method for determining the instability mode of dangerous rock masses on steep slopes, characterized in that: The following steps are involved: Acquire point cloud data of dangerous rock masses in the target area, perform neighborhood point searches on point clouds at different locations on the slope based on coordinate information in the point cloud data of the dangerous rock masses to obtain different neighborhood point sets; perform a coplanarity test on the different neighborhood point sets and merge the coplanar point sets; set a point cloud search radius and extract the normal vector of the best-fitting plane within the search radius, and calculate the inclination and dip angle within the search radius of the point cloud of the target area based on the normal vector of the best-fitting plane within the search radius; The centroid of the merged coplanar point set is translated to the origin, the normal vector of the optimal fitting plane of the translated point set is calculated, and the inclination and dip angle of the fitting plane are calculated based on the normal vector of the optimal fitting plane; Calculate the distance between all point clouds in the point set and the centroid, and use the farthest distance as the radius of the structural surface trace length; Clustering and grouping the structural surfaces according to the inclination and dip of the fitting plane to obtain structural surface grouping data; fitting a disk plane according to the centroid of the translated point set and the radius of the structural surface trace length to calculate the inclination and dip of the structural surface intersection line; According to the inclination and dip angle within the point cloud search radius, the inclination and dip angle of the structural surface, and the inclination and dip angle of the structural surface intersection line, the dangerous rock mass in the target area is located and the failure mode of the dangerous rock mass is determined.

2. A method for determining the instability mode of a dangerous rock mass on a steep slope according to claim 1, characterized in that: Performing a coplanarity test on the different neighborhood point sets and merging the coplanar point sets specifically includes the following steps: Set the search radius or number of neighborhood points of the neighborhood, use the k-NN algorithm to search the neighborhood points of the point cloud at different positions of the slope, and obtain the neighborhood point set; Iteratively calculate the distance from all points in the neighborhood point set to the fitting plane, set the distance threshold, and if the calculated d n Less than d t , set as an interior point, otherwise set as an exterior point; count the number of interior points and exterior points and calculate the number of iterations; The optimal plane parameters of the neighborhood point set are obtained according to the cost function value; wherein the cost function is specifically: Among them, d n is the distance from the point to the fitting plane; d t is the distance threshold; Calculate the angle between the normal vectors of the fitted planes of different neighborhood point sets using the following formula: When the cosine of the angle is close to 1, the two point sets are parallel; check the offset difference between the planes, specifically: D ij =|D i -D j |; If the offset is less than the set threshold, the two point sets are considered to be coplanar; if the point sets are coplanar, the coplanar point sets are merged; where N i (A i , B i , C i , D i ) is the optimal plane normal vector of the neighborhood point set i; N j (A j ,B j ,C j ,D j ) is the optimal plane normal vector of the neighborhood point set j; θ ij is the plane angle between i and j; D ij is the offset between the two planes; Merge sets of coplanar points.

3. The method for determining the instability mode of a dangerous rock mass on a steep slope according to claim 1, wherein: The inclination and dip angle within the target area point cloud search radius are calculated using the following formula: Where θs is the inclination angle within the search radius; A, B, and C are the components of the normal vector on the x, y, and z axes, respectively; φs is the inclination within the search radius; A and B are the components of the normal vector on the x and y axes, respectively.

4. The method for determining the instability mode of a dangerous rock mass on a high and steep slope according to claim 1, wherein: The PSO particle swarm algorithm is used to cluster the structural surfaces.

5. The method for determining the instability mode of a dangerous rock mass on a steep slope according to claim 1, wherein: The failure modes of the dangerous rock mass include plane sliding, wedge sliding and bending collapse.

6. A method for determining the instability mode of a dangerous rock mass on a high and steep slope according to claim 5, characterized in that: According to the inclination and dip angle within the point cloud search radius, the inclination and dip angle of the structural surface, and the inclination and dip angle of the intersection line of the structural surface, the dangerous rock mass in the target area is located and the failure mode of the dangerous rock mass is determined, wherein the identification formula of the plane sliding mode is specifically: The specific identification formula of the wedge sliding mode is: The identification formula of the bending and dumping mode is specifically: Where φs is the inclination within the search radius; φ is the inclination of the structural surface; θ is the inclination angle of the structural surface; is the internal friction angle of the slope; φi is the inclination of the structural surface intersection line; θi is the inclination angle of the structural surface intersection line.

7. A system for determining the instability mode of dangerous rock masses on steep slopes, characterized in that: include: A data processing module is used to obtain point cloud data of dangerous rock masses in a target area, perform neighborhood point searches on point clouds at different locations on the slope based on coordinate information in the point cloud data of the dangerous rock masses to obtain different neighborhood point sets; perform a coplanarity test on the different neighborhood point sets and merge the coplanar point sets; set a point cloud search radius and extract the normal vector of the best-fitting plane within the search radius, and calculate the inclination and dip angle within the search radius of the point cloud of the target area based on the normal vector of the best-fitting plane within the search radius; A structural surface calculation module is used to translate the centroid of the merged coplanar point set to the origin, calculate the normal vector of the optimal fitting plane of the translated point set, and calculate the inclination and dip angle of the fitting plane based on the normal vector of the optimal fitting plane; calculate the distance between all point clouds in the point set and the centroid, and use the farthest distance as the radius of the structural surface trace length; A structural surface intersection module is used to cluster and group structural surfaces according to the inclination and dip of the fitting plane to obtain structural surface grouping data; fit a disk plane according to the centroid of the translated point set and the radius of the structural surface trace length to calculate the inclination and dip of the structural surface intersection line; The identification and positioning module is used to locate the dangerous rock mass in the target area and determine the damage mode of the dangerous rock mass according to the inclination and inclination angle within the point cloud search radius, the inclination and inclination angle of the structural surface and the inclination and inclination angle of the structural surface intersection line.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, it can execute the steps of the method according to any one of claims 1 to 6.

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