A high and steep slope dangerous rock mass instability mode judgment method and system

Abstract

The application provides a high and steep slope dangerous rock mass instability mode judgment method and system, belongs to the field of rock mass engineering, acquires point cloud data of a dangerous rock mass in a target area, searches for neighborhood points of point clouds at different positions of a slope according to coordinate information in the dangerous rock mass point cloud data, carries out plane fitting and coplanarity testing on the neighborhood points, merges coplanar point sets, and calculates the inclination and dip angle of a structural plane according to the point sets. The structural plane is clustered and grouped, the structural plane is fitted and intersection calculation is carried out. In combination with the topographic features, rock mass geometric shape and structural plane development of a high and steep slope, point-plane calculation and analysis are carried out, the dangerous rock mass is positioned and its failure mode is identified, the accuracy of the judgment is improved, the failure mode of the slope rock mass is finely divided, and this is beneficial to the disaster prevention and mitigation work of rockfall disasters of high and steep slopes.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of rock mass engineering, and particularly relates to a high and steep slope dangerous rock mass instability mode judgment method and system. BACKGROUND

[0002] Rock slope is often cut by surrounding structural planes, and dangerous rock mass with regional distribution characteristics is often formed. These dangerous rock masses are prone to instability under the action of external disturbance factors such as rainfall and earthquakes, and can cause major catastrophic geological disasters. Therefore, identifying dangerous rock masses and judging their possible failure modes are key links in mountain disaster prevention and mitigation work.

[0003] Traditional manual ground investigation and ground-based three-dimensional laser scanning devices often face problems such as height limitation and insufficient accuracy, thereby restricting the comprehensiveness and accuracy of high and steep slope dangerous rock mass investigation. With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles equipped with LiDAR sensors can obtain high-precision point cloud data, providing a convenient and accurate solution for slope rock mass structural plane feature identification and dangerous rock mass identification. For example, the invention patent with the Chinese patent document number CN111178214B provides a dangerous rock mass rapid identification method based on unmanned aerial vehicle photography technology. The method generates a point cloud model from high-precision optical photographs, and after denoising and thinning processing, the boundary of the dangerous rock mass is fitted using spatial clustering and support vector machine algorithm, thereby realizing the identification and preliminary stability analysis of the dangerous rock mass.

[0004] Although the existing investigation technology has obvious advantages in improving the speed and efficiency of dangerous rock mass identification, it often ignores the development of structural planes, the geometric characteristics of rock mass, and the topographic factors such as slope angle and tendency, thereby limiting the accuracy and accuracy of dangerous rock mass identification and instability mode determination. SUMMARY

[0005] In order to improve the accuracy of dangerous rock mass identification and instability mode determination, the application provides a high and steep slope dangerous rock mass instability mode judgment method and system.

[0006] In order to achieve the above purpose, the application provides the following technical scheme:

[0007] A high and steep slope dangerous rock mass instability mode judgment method, comprising the following steps:

[0008] Obtain the dangerous rock mass point cloud data of the target area, search the neighborhood points of the point cloud at different positions of the slope according to the coordinate information in the dangerous rock mass 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 a point cloud search radius and extract the normal vector of the optimal fitting plane within the search radius, and calculate the tendency and inclination of the point cloud within the search radius of the target area according to the normal vector of the optimal fitting plane within the search radius.

[0009] 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 according to 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;

[0010] According to the inclination and dip angle of the fitting plane, the structural surface is clustered and grouped to obtain structural surface grouping data; according to the centroid of the translated point set and the radius of the structural surface trace length, a disc plane is fitted, and the inclination and dip angle of the structural surface intersection line are calculated;

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

[0012] Preferably, the coplanarity of the different neighborhood point sets is checked, and the coplanar point sets are merged, specifically including the following steps:

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

[0014] The distance of all points in the neighborhood point set to the fitting plane is iteratively calculated, and a distance threshold is set. If the calculated d n is less than d t , it is set as an inner point, otherwise it is set as an outer point; the number of inner points and outer points is counted to calculate the iteration times;

[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] Wherein, d n is the distance of the point to the fitting plane; d t is the distance threshold;

[0018] The included angle between the normal vectors of the fitting planes of different neighborhood point sets is calculated, specifically through the following formula:

[0019]

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

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

[0022] If the offset is less than a set threshold, the two point sets are considered to be coplanar; if the point sets are coplanar, the coplanar point sets are merged; wherein N i (A i , B i , C i , D i ) are the optimal plane normal vectors of the neighborhood point set i; N j (A j , B j , C j , D j ) are the optimal plane normal vectors 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] Merging the coplanar point sets.

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

[0025]

[0026] wherein θs is the dip 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 dip 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 planes.

[0028] Preferably, the dangerous rock mass failure mode includes plane sliding, wedge sliding and bending toppling.

[0029] Preferably, the dangerous rock mass in the target area is located and the dangerous rock mass failure mode is determined according to the dip and the dip angle within the point cloud search radius, the dip and the dip angle of the structural plane and the dip and the dip angle of the intersection line of the structural planes, wherein the identification formula of the plane sliding mode is specifically:

[0030]

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

[0032]

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

[0034]

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

[0036] The application further provides a high and steep slope dangerous rock mass instability mode judgment system, and specifically comprises:

[0037] The data processing module is configured to acquire point cloud data of a dangerous rock mass in a target region, perform neighborhood point searching on point clouds at different positions of the slope according to coordinate information in the point cloud data of the dangerous rock mass, obtain different neighborhood point sets, perform coplanarity testing on the different neighborhood point sets, merge coplanar point sets, set a point cloud search radius, extract a normal vector of an optimal fitting plane within the search radius, and calculate a tendency and an inclination within the point cloud search radius of the target region according to the normal vector of the optimal fitting plane within the search radius.

[0038] The structural surface calculation module is configured to translate a centroid of the merged coplanar point set to an origin, calculate a normal vector of an optimal fitting plane of the point set after translation, calculate a tendency and an inclination of the fitting plane according to the normal vector of the optimal fitting plane, and calculate distances between all point clouds in the point set and the centroid, and take the farthest distance as a radius of a structural surface trace length.

[0039] The structural surface intersection line module is configured to group structural surfaces according to the tendency and the inclination of the fitting plane, obtain structural surface grouping data, fit a disc plane according to the centroid of the point set after translation and the radius of the structural surface trace length, and calculate a tendency and an inclination of a structural surface intersection line.

[0040] The discrimination and positioning module is configured to position and determine a dangerous rock mass damage mode of the target region according to the tendency and the inclination within the point cloud search radius, the tendency and the inclination of the structural surface, and the tendency and the inclination of the structural surface intersection line.

[0041] The application further provides a computer device, which comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to realize the steps in the high and steep slope dangerous rock mass instability mode judgment method.

[0042] The application further provides a computer readable storage medium, which stores a computer program, and the computer program can execute the steps in the high and steep slope dangerous rock mass instability mode judgment method when loaded by a processor.

[0043] The high and steep slope dangerous rock mass instability mode judgment method provided by the application has the following beneficial effects:

[0044] The application obtains point cloud data of a high and steep slope dangerous rock mass region, performs point surface calculation and analysis, executes neighborhood point search to perform plane fitting and coplanar type inspection, obtains the dip and dip angle of a structural surface and its intersection line, and introduces the development characteristics of different structural surfaces. A point cloud search radius is set, the dip and dip angle in the point cloud search radius of a target region are calculated, and the influence of topographic factors of point clouds in different positions is considered. Then, the structural surface is clustered and grouped, the structural surface is fitted, and intersection calculation is performed. According to the calculation and analysis results, the high and steep slope dangerous rock mass is positioned and the failure mode is determined, the fine division of the slope rock mass failure mode is realized, more accurate information is provided for rockfall protection engineering, and strong support is provided for the disaster prevention and mitigation work of high and steep slope rockfall disasters. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application and the design scheme thereof, the drawings required by the present embodiments will be briefly introduced as follows. The drawings in the following description are only part of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0046] Figure 1 The flow chart of a high and steep slope dangerous rock mass instability mode judgment method in the embodiment of the present application.

[0047] Figure 2 The structural surface intersection distribution diagram in the embodiment of the present application.

[0048] Figure 3 The slope dip angle and dip distribution diagram in the embodiment of the present application, wherein, Figure 3 (a) of the figure is a slope dip distribution diagram, Figure 3 (b) of the figure is a dip angle distribution diagram.

[0049] Figure 4 The schematic diagram of dangerous rock mass of different potential instability modes in the embodiment of the present application, wherein, Figure 4 (a) of the figure is a schematic diagram of a plane sliding mode dangerous rock mass, Figure 4 (b) of the figure is a schematic diagram of a wedge sliding mode dangerous rock mass, Figure 4 (c) of the figure is a schematic diagram of a bending and toppling mode dangerous rock mass. DETAILED DESCRIPTION

[0050] In order for those skilled in the art to better understand the technical scheme of the present application and can be implemented, the present application will be described in detail below in conjunction with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical scheme of the present application, and cannot limit the protection scope of the present application.

[0051] EMBODIMENT

[0052] The application provides a high and steep slope dangerous rock mass instability mode judgment method, like Figure 1 as shown, specifically comprising the following steps:

[0053] Step 1, obtaining high-precision point cloud data of the research area.

[0054] The point cloud data of the slope is obtained by mounting a LiDAR sensor on a UAV. Multi-angle oblique photogrammetry is carried out on the high and steep slope. In order to ensure that the data meet the subsequent analysis requirements, a scanning mode with strong penetration and high point cloud density should be selected during photogrammetry, and the scanning angle should be reasonably set. At the same time, repeated scanning work should be carried out in the area with low point cloud density to ensure the integrity and accuracy of the slope point cloud data. Then, based on the point cloud data obtained by the UAV, three-dimensional reconstruction is carried out to generate high-precision point cloud data and three-dimensional real scene models of the research area.

[0055] Step 2, extracting the coordinate information of the point cloud.

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

[0057] Step 3, performing neighborhood point search on the research area.

[0058] The k-NN algorithm is adopted, the search radius or the number of neighborhood points is set, the neighborhood point search is carried out on the point cloud at different positions of the slope, and a certain range or a certain number of neighborhood point sets are established, so as to extract the point cloud features in the subsequent step. In this example, the number of neighborhood points is set to 30, and the neighborhood point search is completed.

[0059] Step 4, plane fitting is carried out on different neighborhood point sets and coplanarity test is carried out.

[0060] Step 4.1, selecting a sample set with a point cloud number of s (s≥3) from different neighborhood point sets, and obtaining the initial fitting plane parameters N0(A0, B0, C0, D0) by using the least square method. The selected sample set with a point cloud number of 6.

[0061] Step 4.2, assuming 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 of all points in the sample set to the fitting plane is calculated.

[0063]

[0064] Step 4.3, setting a distance threshold d t , if the calculated d n is less than d t , it is defined as an inner point, otherwise, it is defined as an outer point. The number of inner points and outer points is recorded. The selected distance threshold d t is 0.3.

[0065] Step 4.4, repeating the selection of sample set s (s≥3) and repeating the process of step 4.2 and step 4.3 to obtain more inner points and the best plane parameters. Set the iteration number k, select the model with the minimum cost function value (i.e. F M min) within the iteration number k as the optimal model, and obtain the optimal plane normal vector (A, B, C, D). The iteration number can be calculated according to the Monte Carlo type probability method, specifically through formula (2), the calculation formula of the cost function is formula (3) and (4). Set the iteration number k to 100.

[0066]

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

[0068]

[0069] Wherein, d n is the distance from the point to the fitted plane; d t is the distance threshold.

[0070] Step 4.5, according to the above steps, the optimal plane parameters N i (A i , B i , C i , D i ) of all neighborhood point sets can be obtained. In order to determine whether different neighborhood point sets are coplanar, first use the angle between the normal vectors of the planes fitted by different neighborhood point sets, through formula (5). If the cosine value of the angle is close to 1, it is considered that the two point sets are parallel. Set the threshold value, check the difference of the offset between the planes through formula (6), if the offset is less than the set threshold value, it is considered that the two point sets are coplanar. If the point sets are coplanar, the coplanar point sets are merged. The offset threshold value set in this example is 0.1.

[0071]

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

[0073] where, N i (A i , B i , C i , D i ) are the optimal plane normal vectors of the neighborhood point set i; N j (A j , B j , C j , D j ) are the optimal plane normal vectors 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, 4260 point sets

[0075] Step 5, translate the obtained point set, obtain the normal vector of the translated point set, and convert the structural surface characteristics containing the dip, dip angle and plane radius according to the normal vector.

[0076] Step 5.1, subtract the average value of the point position according to formulas (7)-(9) from the point set obtained in step 4, move the centroid of the point set to the origin, so as to eliminate the influence of translation, and focus on the relative distribution of the point set, and the average coordinate value is taken as the centroid of the plane.

[0077]

[0078] where, x pi , y pi and z pi are the x, y and z axis coordinates of the point pi; x′ pi , y′ pi and z′ pi are the coordinates of the translated point pi; 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 of the coplanar point set. Since the dip of the plane is the angle between the projection of the plane normal vector and the north, and the dip angle of the plane is the angle between the plane normal vector and the horizontal plane. Therefore, the dip angle can be converted according to formula (10), and the dip can be converted according to formula (11) by using the plane normal vector N of the point set.

[0080]

[0081] where, θ is the dip 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 plane.

[0082]

[0083] where φ is the dip direction, A, B are the components of the plane normal vector on x, y respectively.

[0084] The trace length of the structural plane, i.e. the exposed length of the structural plane, is an important characteristic parameter for determining the instability mode of the dangerous rock mass. The distance of all points in the set from the centroid is calculated, and the farthest distance is taken as the radius of the trace length of the structural plane. As in step 4, 4260 pieces of information of the structural plane are obtained in this step.

[0085] Step 6, according to the dip direction and dip angle data of the fitted structural plane, define the PSO particle swarm, and group the structural planes with different development characteristics. The internal requirement of the subsequent kinematic analysis is conducive to the similar control of the direction and range of the possible damage of the structural planes in the same group.

[0086] The PSO particle swarm algorithm is used to set the number of particle swarms, the number of clustering categories and the number of iterations, and the structural plane data obtained above is clustered and grouped. According to the dip direction and dip angle data of the 4260 structural planes, the data with similar dip direction and dip angle are summarized and divided into 3 groups. In this example, the number of particles is set to 20, the number of iterations is set to 50, and the number of clustering categories is set to 3. The above 4260 structural planes are divided into 3 groups. The representative dip direction and dip angle data of each group of structural planes are obtained. In the example, the representative data of the three groups are as follows: structural plane group 1: dip direction 11.31°, dip angle 40.03°; structural plane group 2: dip direction 178.60°, dip angle 88.63°; structural plane group 3: dip direction 245.32°, dip angle 88.22°.

[0087] Step 7, determine and extract the intersection line of the structural plane.

[0088] Since the wedge sliding and bending toppling failure modes of the structural plane need to consider the mutual cutting of different structural planes.

[0089] Step 7.1, according to the centroid and radius obtained in step 5, fit a disc plane, use the Hessian operator to fit the structural plane and perform line intersection determination.

[0090] Step 7.2, screen out the disc intersection points that do not meet the normal intersection, such as too many intersection points, repeated intersection points, etc. According to the least squares method, fit the disc plane intersected by the two structural planes, use the principal component analysis method to obtain the plane normal vector of the disc plane, and according to formulas (10)-(11), obtain the dip direction (φi) and dip angle (θi) of the plane. Take the diameter of the disc as the size of the intersection line, and obtain the data of the intersection of different structural planes. The dip direction and dip angle of the intersection line of different structural planes are calculated as shown in formula (12). Figure 2

[0091] Step 8, calculate the topographic factors of the slope.​

[0092] Based on the point cloud distribution and distance of the research area, the search radius is set, and the terrain factors of the point cloud at different positions are calculated. The least square method is used to fit the point cloud within the search radius as a plane, and the normal vector of the plane can be obtained. According to the normal vector of the plane and according to formulas (12)-(13), the dip and dip angle of the slope point cloud within the search radius are calculated.

[0093]

[0094] Where θs is the dip 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 dip within the search radius, if φs is calculated as negative, it is added by 360° to ensure that the range of dip is 0ˉ360°; A and B are the components of the normal vector on the x and y axes respectively.

[0097] The search radius is set to 2m, and the calculated slope dip and dip angle are shown in FIGS. 1 and 2. Figure 3

[0098] Step 9, according to different conditions, the dangerous rock mass damage mode is distinguished, and the dangerous rock mass under different damage modes is output. Considering three common dangerous rock mass damage modes, which are plane sliding, wedge sliding and bending toppling. The different identification conditions of the three are as follows:

[0099] Plane sliding: the dip of the structure surface is the same as the dip of the search radius, and the dip angle is greater than the defined internal friction angle of the slope and less than the dip angle within the search radius, then the rock mass at the position of the structure surface is prone to plane sliding, and the identification formula is as follows:

[0100]

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

[0102] Wedge sliding: if two groups of structure surfaces intersect, and the dip angle of the intersection line is greater than the internal friction angle of the slope and less than the dip angle within the search radius, and the dip of the intersection line of the structure surface is the same as the dip within the search radius, then the rock mass at the position of the structure surface is prone to wedge sliding, and the identification formula is as follows:

[0103]

[0104] Where φs is the dip within the search radius; φi is the dip of the intersection line of the structure surface; θi is the dip angle of the intersection line of the structure surface; ​The internal friction angle of the slope.

[0105] Bend collapse: if the inclination of the search radius is opposite to the inclination of the structure surface, the fitting plane of the search radius and the structure surface is almost the same or intersects at a small angle, and the inclination of the structure surface is greater than the internal friction angle of the slope body, then the rock mass at the position of the structure surface is prone to bend collapse, and the identification formula is as follows:

[0106]

[0107] Wherein, the inclination of the search radius is φs; the inclination of the structure surface is φ; the inclination of the structure surface is θ; and the inclination of the search radius is θs. The internal friction angle of the slope.

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

[0109] The application also provides a high and steep slope dangerous rock mass instability mode judgment system, which specifically comprises:

[0110] The data processing module is used for acquiring the point cloud data of the dangerous rock mass in the target area, performing neighborhood point search on the point clouds at different positions of the slope according to the coordinate information in the point cloud data of the dangerous rock mass, obtaining different neighborhood point sets, performing coplanarity test on the different neighborhood point sets, merging the coplanar point sets, setting a point cloud search radius, extracting the normal vector of the optimal fitting plane within the search radius, and calculating the inclination and inclination within the point cloud search radius of the target area according to the normal vector of the optimal fitting plane within the search radius.

[0111] The structure surface calculation module is used for translating the centroid of the merged coplanar point set to the origin, calculating the normal vector of the optimal fitting plane of the translated point set, and calculating the inclination and inclination of the fitting plane according to the normal vector of the optimal fitting plane; and calculating the distance between all point clouds in the point set and the centroid, and taking the farthest distance as the radius of the trace length of the structure surface.

[0112] The structure surface intersection module is used for clustering and grouping the structure surfaces according to the inclination and inclination of the fitting plane, obtaining structure surface grouping data, fitting a disc plane according to the centroid of the translated point set and the radius of the trace length of the structure surface, and calculating the inclination and inclination of the structure surface intersection.

[0113] The discrimination and positioning module is used for positioning and determining the damage mode of the dangerous rock mass in the target area according to the inclination and inclination within the point cloud search radius, the inclination and inclination of the structure surface, and the inclination and inclination of the structure surface intersection.

[0114] ​The modules in the high and steep slope dangerous rock mass instability mode judgment system can be realized by software, hardware, or a combination thereof. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the modules.

[0115] The present application also provides a computer device including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in the high and steep slope dangerous rock mass instability mode judgment method embodiment. For details, refer to the method embodiment, which will not be described here.

[0116] Further, the present application also provides a non-transitory computer readable storage medium containing instructions. For example, a memory containing instructions, which can be executed by the processor of a computer device to complete the above method. For example, the non-transitory 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, etc. When the computer program is executed by the processor, the steps in the high and steep slope dangerous rock mass instability mode judgment method embodiment can be implemented. For details, refer to the method embodiment, which will not be described here.

[0117] Those skilled in the art should understand that the embodiments of the present application can provide a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0118] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows 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 the 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 produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0119] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0121] It should be noted that the above-mentioned detailed implementation can make those skilled in the art more fully understand the present application, but in no way limit the present application. Therefore, although the present application has been described in detail in the present specification and examples, those skilled in the art should understand that the present application can still be modified or replaced by equivalents; and all technical solutions and improvements which do not deviate from the spirit and scope of the present application are covered in the protection scope of the present application. Any reference signs in the claims should not be considered as limiting the claims. Simple changes or equivalent replacements of technical solutions which are obvious to those skilled in the art within the scope of the present disclosure are all within the protection scope of the present application.

Claims

1. A high and steep slope dangerous rock mass instability mode judgment method, characterized in that, The method comprises the following steps: Obtaining point cloud data of a dangerous rock mass in a target region, performing neighborhood point searching on point clouds at different positions of a slope according to coordinate information in the point cloud data of the dangerous rock mass, obtaining different neighborhood point sets, performing coplanarity testing on the different neighborhood point sets, merging coplanar point sets, setting a point cloud searching radius and extracting a normal vector of an optimal fitting plane within the searching radius, and calculating a dip direction and a dip angle within the point cloud searching radius of the target region according to the normal vector of the optimal fitting plane within the searching radius; wherein the coplanarity testing on the different neighborhood point sets and the merging of the coplanar point sets specifically comprise the following steps: A search radius or a number of neighborhood points is set, a k-NN algorithm is used to search neighborhood points of point clouds at different positions of the slope, and a neighborhood point set is obtained; distances of all points in the neighborhood point set to a fitting plane are iteratively calculated, a distance threshold is set, and if the calculated distance is less than the distance threshold, the point is set as an inner point, otherwise, the point is set as an outer point; and the number of inner points and outer points is counted to calculate an iteration number. less than , is set as an inner point, otherwise, is set as an outer point; and the number of inner points and outer points is counted to calculate an iteration number; Obtaining optimal plane parameters of the neighborhood point sets according to a cost function value; wherein the cost function is specifically as follows: ； ； wherein, is the distance to the fitted plane; is the distance threshold; Calculating an included angle between normal vectors of fitting planes of different neighborhood point sets, specifically through the following formula: ； When the cosine value of the included angle is close to 1, the two point sets are parallel; checking a difference in offset between the planes, specifically as follows: ； If the offset is less than a set threshold, it is considered that the two point sets are coplanar; if the point sets are coplanar, the coplanar point sets are merged; wherein 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; is the plane angle between i and j; D ij is the offset between the two planes; the coplanar point sets are merged; Translating a centroid of the merged coplanar point set to an origin, calculating a normal vector of an optimal fitting plane of the point set after translation, calculating a dip direction and a dip angle of the fitting plane according to the normal vector of the optimal fitting plane, and calculating distances between all point clouds in the point set and the centroid, taking a farthest distance as a radius of a trace length of a structural surface; Clustering and grouping the structural surface according to the dip direction and the dip angle of the fitting plane, obtaining structural surface grouping data, fitting a disc plane according to the centroid of the point set after translation and the radius of the trace length of the structural surface, and calculating a dip direction and a dip angle of a structural surface intersection line; Positioning the dangerous rock mass in the target region and determining a failure mode of the dangerous rock mass according to the dip direction and the dip angle within the point cloud searching radius, the dip direction and the dip angle of the structural surface, and the dip direction and the dip angle of the structural surface intersection line; the failure mode of the dangerous rock mass comprises a plane sliding mode, a wedge sliding mode and a bending and toppling mode; wherein a judgment formula of the plane sliding mode is specifically as follows: ； a judgment formula of the wedge sliding mode is specifically as follows: ； a judgment formula of the bending and toppling mode is specifically as follows: ； wherein is the inclination of the slope within the search radius; is the inclination of the structure plane; is the inclination of the structure plane; is the internal friction angle of the slope; is the inclination of the structure plane intersection; is the inclination of the structure plane intersection.

2. The high and steep slope dangerous rock mass instability mode judging method according to claim 1, characterized in that, The dip direction and the dip angle within the point cloud searching radius of the target region are calculated through the following formula: ； ； wherein is the dip within the search radius; A, B, and C are the components of the normal vector on the x, y, and z axes, respectively; is the dip within the search radius; A, B, and C are the components of the normal vector on the x, y, and z axes, respectively; 3. The high and steep slope dangerous rock mass instability mode judging method according to claim 1, characterized in that, Clustering and grouping the structural surface by using a PSO particle swarm algorithm.

4. A high and steep slope dangerous rock mass instability mode judgment system, characterized in that, It comprises: A data processing module is configured to obtain point cloud data of a dangerous rock mass in a target region, perform neighborhood point searching on point clouds at different positions of a slope according to coordinate information in the point cloud data of the dangerous rock mass, obtain different neighborhood point sets, perform coplanarity testing on the different neighborhood point sets, merge coplanar point sets, set a point cloud searching radius, extract a normal vector of an optimal fitting plane within the searching radius, and calculate a dip direction and a dip angle within the point cloud searching radius of the target region according to the normal vector of the optimal fitting plane within the searching radius; wherein the coplanarity testing on the different neighborhood point sets and the merging of the coplanar point sets specifically comprise the following steps: A search radius or a number of neighborhood points is set, a k-NN algorithm is used to search neighborhood points of point clouds at different positions of the slope, and a neighborhood point set is obtained; distances of all points in the neighborhood point set to a fitting plane are iteratively calculated, a distance threshold is set, and if the calculated distance is less than the distance threshold, the point is set as an inner point, otherwise, the point is set as an outer point; and the number of inner points and outer points is counted to calculate an iteration number. less than , is set as an inner point, otherwise, it is set as an outer point; and the number of inner points and outer points is counted to calculate an iteration number; Obtaining optimal plane parameters of the neighborhood point sets according to a cost function value; wherein the cost function is specifically as follows: ； ； wherein, is the distance to the fitted plane; is the distance threshold; Calculating an included angle between normal vectors of fitting planes of different neighborhood point sets, specifically through the following formula: ； When the cosine value of the included angle is close to 1, the two point sets are parallel; the offset difference between the planes is checked, specifically: ； If the offset is less than a set threshold, the two point sets are considered coplanar; if the point sets are coplanar, the coplanar point sets are merged; wherein 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; is the plane angle between i and j; D ij is the offset between the two planes; the coplanar point sets are merged; The structural surface calculation module is configured 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 according to 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; The structural surface intersection module is configured to group the structural surfaces according to the inclination and dip angle of the fitting plane to obtain structural surface grouping data; fit a disc 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 angle of the structural surface intersection; The discrimination positioning module is configured to search for the inclination and dip angle within the radius of the point cloud, the inclination and dip angle of the structural surface, and the inclination and dip angle of the structural surface intersection, to position and determine the dangerous rock mass in the target area and the failure mode of the dangerous rock mass; the failure mode of the dangerous rock mass includes plane sliding, wedge sliding, and bending and toppling; wherein the identification formula of the plane sliding mode is specifically: ； The identification formula of the wedge sliding mode is specifically: ； The identification formula of the bending and toppling mode is specifically: ； wherein is the dip of the slope within the search radius; is the dip of the structural plane; is the dip angle of the structural plane; is the internal friction angle of the slope; is the dip of the intersection line of the structural planes; is the dip angle of the intersection line of the structural planes.

5. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-4. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is loaded by the processor to execute the steps of the method of any one of claims 1 to 3.

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