An intelligent identification and stability analysis system for dangerous rock based on non-contact measurement

Abstract

The application discloses a dangerous rock intelligent identification and stability analysis system based on non-contact measurement, which comprises a surveying and collecting subsystem and a central control subsystem; the surveying and collecting subsystem is used for close-range photogrammetry of the dangerous rock through a UAV, collecting three-dimensional point cloud data of the dangerous rock, and uploading the three-dimensional point cloud data to the central control subsystem; the central control subsystem is used for point cloud processing and structural surface clustering processing according to the collected three-dimensional point cloud data of the dangerous rock, judging the type of the dangerous rock and whether the dangerous rock has potential damage according to the processing results of the point cloud processing and the structural surface clustering processing, and calculating the stability coefficients of different types of the dangerous rock when the potential damage exists, so as to obtain the stability analysis result of the dangerous rock according to the stability coefficients of the different types of the dangerous rock. The application improves the operation efficiency through unmanned surveying, and improves the accuracy of the identification and stability analysis of the dangerous rock based on the three-dimensional point cloud model of the dangerous rock and the pre-prepared algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geological disaster research, and particularly relates to a dangerous rock intelligent identification and stability analysis system based on non-contact measurement. BACKGROUND

[0002] Dangerous rock collapse is one of the typical geological disasters in mountainous areas, which has the characteristics of wide distribution, strong suddenness, strong disaster-causing and high concealment, and it is difficult to measure effectively and accurately by conventional surveying means. The western region generally has high and steep mountains, which provides natural conditions for the incubation of collapse disasters. The Party and the state will place disaster prevention, disaster reduction and disaster relief in a more prominent position, and the importance of dangerous rock collapse prevention is increasingly prominent.

[0003] Dangerous rock refers to rock mass that has not yet collapsed, but has the main conditions for collapse and has appeared precursory phenomena. Dangerous rock mass is in an unstable state and may slide, collapse and other geological disasters at any time under the action of gravity, earthquake, fissure water pressure and other inducing factors. The mountainous area has high and steep mountains, deep valleys and a large number of dangerous rocks, which may collapse at any time. Traditional contact surveying methods cannot efficiently and accurately measure the high mountains of hundreds of meters, and often rely on the experience of engineers, which has certain errors. With the rise of non-contact measurement technologies such as unmanned aerial vehicles and laser radars, a new solution is provided for dangerous rock survey and identification. However, there is no mature system for dangerous rock identification, so an accurate, efficient and intelligent dangerous rock intelligent identification method and stability analysis method are needed. SUMMARY

[0004] In view of the above problems of the prior art, the present application provides a dangerous rock intelligent identification and stability analysis system based on non-contact measurement. By using non-contact measurement technology, improved KNN algorithm and GASAK algorithm are used to realize the identification and clustering analysis of dangerous rock structure surface, and the equatorial projection method is used to preliminarily judge the stability of the dangerous rock, so as to improve the accuracy and efficiency of dangerous rock identification and stability analysis.

[0005] In order to solve the above technical problems, the present application adopts the following technical scheme:

[0006] The application discloses a dangerous rock intelligent identification and stability analysis system based on non-contact measurement, which comprises a surveying and collecting subsystem and a central control subsystem; the surveying and collecting subsystem is used for performing multi-view close photogrammetry on the dangerous rock by means of a UAV, collecting three-dimensional point cloud data of the dangerous rock and uploading the three-dimensional point cloud data to the central control subsystem; the central control subsystem is used for performing point cloud processing and structural surface clustering processing according to the collected three-dimensional point cloud data of the dangerous rock, judging the type of the dangerous rock and whether the dangerous rock has potential damage according to the processing results of the point cloud processing and the structural surface clustering processing, and when the dangerous rock has potential damage, calculating the stability coefficients of different types of the dangerous rock, and obtaining a stability analysis result of the dangerous rock according to the stability coefficients of the different types of the dangerous rock.

[0007] As a preferred solution, the central control subsystem comprises a point cloud processing module, a structural surface processing module, a type judging module and a stability analysis module.

[0008] The point cloud processing module is used for processing the three-dimensional point cloud data of the dangerous rock collected by the surveying and collecting subsystem based on an improved KNN algorithm, so as to adaptively determine the number of domain points of each target point, realize dangerous rock point cloud set plane fitting and normal vector calculation, and construct a three-dimensional point cloud model of the dangerous rock.

[0009] The structural surface processing module is used for clustering and analyzing the point cloud set plane obtained by the point cloud processing module by means of an improved K-means algorithm based on a genetic simulated annealing algorithm, identifying the dominant group of the structural surface point cloud and the structural surface parameters of each group, and the structural surface parameters comprise structural surface occurrence information.

[0010] The type judging module is used for identifying the structural surface features of the dangerous rock according to the three-dimensional point cloud model of the dangerous rock and the structural surface parameters, and obtaining a type judgment result of the dangerous rock according to the structural surface features of the dangerous rock.

[0011] The stability analysis module is used for qualitatively analyzing the dangerous rock based on the polar projection method in combination with the structural surface parameters obtained by the structural surface processing module according to the type judgment result of the dangerous rock obtained by the type judging module, judging whether the dangerous rock has potential damage, and when it is judged that the dangerous rock has potential damage, calculating the stability coefficients of different types of the dangerous rock and quantitatively evaluating the stability of the dangerous rock.

[0012] As a preferred solution, the specific processing flow of the point cloud processing module is as follows:

[0013] S101, input the three-dimensional point cloud data of the dangerous rock, and determine the maximum value D of the number of domain points D according to the density distribution of the three-dimensional point cloud data max and the minimum value D min of the number of domain points D, and adaptively determine the number of domain points D of the target point in the point cloud space based on the improved KNN algorithm.

[0014] S102, for any target point p in the dangerous rock three-dimensional point cloud data space i =(x i ,y i ,z i ), according to the determined number of domain points D, D-1 nearest domain points of the target point are obtained, and the point cloud set {P} is constituted with the target point, and the center point p'=(x', y', z') of the point cloud set {P} is calculated;

[0015] S103, according to the matrix composed of the three-dimensional coordinates of the point cloud in the point cloud set {P} in the target point domain and the matrix composed of the three-dimensional coordinates of the center point of the point cloud in the point cloud set {P} in the target point domain covariance matrix is constructed T is the transpose symbol;

[0016] S104, eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues λ=(λ1, λ2, λ3) and corresponding eigenvectors μ=(μ1, μ2, μ3); the relationship of eigenvalue decomposition satisfies λμ=Mμ, λ1, λ2, λ3 and μ1, μ2, μ3 respectively represent the eigenvalues and eigenvectors corresponding to the three coordinate dimensions of the target point domain point cloud;

[0017] S105, the eigenvector corresponding to the minimum eigenvalue is selected as the normal vector of the plane of the point cloud set {P}, and the plane equation of each point in the point cloud set {P} is established by using the normal vector;

[0018] S106, based on the plane equation of each point, the least square method is used to adjust the equation parameters so that the sum of the square of the perpendicular distance of all points in the point cloud set {P} to the plane is minimized, thereby determining the plane where the target point p i =(x i ,y i ,z i ) is located;

[0019] S107, through the processing of steps S102-S106, the normal vector calculation and plane fitting are respectively performed on each target point in the dangerous rock three-dimensional point cloud data, the plane where each target point is located is determined, and the dangerous rock three-dimensional point cloud model is constructed.

[0020] As a preferred scheme, in step S106, the objective function that the least square method is used to adjust the equation parameters so that the sum of the square of the perpendicular distance of all points in the point cloud set {P} to the plane is minimized is:

[0021]

[0022] d n =|ax n +by n +czn +d|;

[0023] In the formula, D represents the number of neighborhood points contained in the point cloud {P}; d e Let d be the sum of the squared perpendicular distances from all target points in the point cloud to the plane. n It is any nth point p in the point cloud {P} n =(x n ,y n ,z n The distance from the origin to the plane, n = 1, 2, ..., D; a, b, c are the components of the corresponding unit normal vector of the plane in the x, y, z coordinate axes; d represents the distance from the origin to the plane.

[0024] As a preferred embodiment, the planar fitting of the point cloud cluster further includes: introducing a characteristic index entropy, and calculating linear, planar, and dispersion indices through eigenvalues ​​to quantify the spatial distribution characteristics of the point cloud and identify planar regions within the point cloud; the equation for the characteristic index entropy is:

[0025] E=L λ ln(L λ )+P λ ln(P λ )+S λ ln(S λ );

[0026] L λ = (λ1+λ2) / λ1;

[0027] P λ = (λ2-λ3) / λ1;

[0028] S λ =λ3 / λ1;

[0029] In the formula, E is the characteristic exponent entropy, and L... λ P is a linear exponent. λ S is the isal index. λ S is the dispersion index. λ +P λ +L λ =1.

[0030] As a preferred embodiment, the specific processing flow of the structural surface processing module is as follows:

[0031] S201. Obtain the objective function for structural surface clustering:

[0032] In the formula, J is the objective function, which is the sum of the distances from each structural surface to the cluster center, and d 2 (N i ,K lrepresents the distance between the structure surface Ni to the cluster center K l i Ni represents the ith structure surface, i = 1, 2, 3…, h, h represents the number of structure surfaces of the ith cluster set; K l Kl represents the cluster center of the ith cluster set, l = 1, 2, 3…, L, L represents the number of cluster sets;

[0033] S202, using a genetic simulated annealing algorithm to improve the K-means algorithm to minimize the objective function, completing the clustering analysis of the point cloud set plane.

[0034] As a preferred solution, in step S202, the step of using a genetic simulated annealing algorithm to improve the K-means algorithm to minimize the objective function according to the similarity measure between the structure surfaces, and completing the clustering analysis of the point cloud set plane includes:

[0035] S2021, initializing the genetic algorithm parameters, simulated annealing algorithm parameters and K-means clustering algorithm parameters of the GASAK algorithm, setting the maximum iteration number Maxgen, the initial temperature T0, the temperature cooling coefficient t, and the termination stable temperature T end ; the value range of the temperature cooling coefficient t is (0.5, 1);

[0036] S2022, randomly generating an initial cluster center matrix, setting the fitness function of the GASAK algorithm and the membership function of each cluster center;

[0037] S2023, setting the initial loop count variable gen = 0;

[0038] S2024, taking the cluster set of the structure surface point cloud grouping as the individual of the population, randomly selecting the population, and calculating the membership u il and individual fitness f i of each individual according to the membership function and the comfort function;

[0039] S2025, after the crossover operation on the randomly selected population, the mutation operation is performed again, and the new membership u i ′ l and the new fitness f i ′ of the newly generated individual are calculated according to the fitness function and the membership function;

[0040] S2026, judging whether the new fitness is greater than the old individual fitness, when it is satisfied, the newly generated individual replaces the old individual; when it is not satisfied, the following probability formula of the simulated annealing algorithm is used to receive the newly generated cluster center, replacing the old individual:

[0041]

[0042] ​wherein P is a probability of receiving a newly generated cluster center, e is a base of a natural logarithm, f i is a fitness of a newly generated individual after the current annealing iteration update of the annealing algorithm, f i is a fitness of an old individual before the current annealing iteration update of the annealing algorithm, K is a Boltzmann constant, T gen is an annealing temperature of the current annealing iteration of the annealing algorithm;

[0043] S2027, it is judged whether gen<Maxgen, if yes, gen is increased by 1, and the step S2024 is returned to update the individual in the population; otherwise, S2028 is performed;

[0044] S2028, it is judged whether T gen <T end is satisfied, if yes, the GASAK algorithm is ended and the optimal solution is output; otherwise, a cooling operation is performed, T gen =t·T gen is updated, and the step S2023 is returned to.

[0045] As a preferred scheme, the membership u il and the individual fitness f i of the individual are respectively:

[0046]

[0047] wherein K l is a cluster center of the lth cluster set of the individual, N i is the ith structural surface in the lth cluster set of the individual, i=1, 2, 3…, h, h represents the number of structural surfaces of the lth cluster set, l=1, 2, 3…, L; L is the number of cluster sets; d 2 (N i ,K l ) represents a distance from the structural surface N i to the cluster center K l ; and m is a weight distribution coefficient.

[0048] As a preferred scheme, in the stability analysis module, based on the equatorial projection method, the structural surface parameters obtained by the structural surface processing module are combined to perform qualitative analysis on the dangerous rock, and the specific process of judging whether the dangerous rock has potential damage is as follows:

[0049] First, project the three-dimensional structure surface parameters to the two-dimensional equatorial plane to obtain the equatorial projection diagram of the structure surface, then, identify the mutual relationship and spatial distribution of the structure surface according to the equatorial projection diagram, analyze to obtain the failure mode area of the dangerous rock, the failure mode includes sliding failure and toppling failure, finally, judge whether the dangerous rock is located in the failure mode area by evaluating the position of the pole of the intersection line of the structure surface on the equatorial projection diagram, if the dangerous rock is located in the failure mode area, it is determined that the dangerous rock exists potential damage.

[0050] As a preferred solution, the dangerous rock type judgment result includes: compression-shear slip type, tension-shear toppling type and composite falling type.

[0051] The stability coefficient of different dangerous rock types is calculated by the following formula:

[0052] The stability coefficient calculation method of the compression-shear slip type dangerous rock:

[0053]

[0054] The stability coefficient calculation method of the tension-shear toppling type dangerous rock:

[0055] The overturning point is inside:

[0056] The overturning point is outside:

[0057] The stability coefficient calculation method of the composite falling type dangerous rock:

[0058]

[0059] In the formula, W is the weight of the dangerous rock mass, P is the horizontal seismic force, β is the inclination of the main control structure surface, C, Q is the water pressure of the main control structure surface of the dangerous rock, e1 is the vertical height of the main control structure surface filled with water, a is the horizontal distance from the center of gravity of the dangerous rock mass to the exposed surface of the base, lk f is the standard value of the tensile strength of the dangerous rock mass, H is the vertical height of the dangerous rock mass, h1 is the vertical height of the main control structure surface through section, b f is the contact length of the dangerous rock mass and the base, 0k P is the horizontal seismic force, h0 is the vertical distance from the center of gravity of the dangerous rock mass to the exposed surface of the base.

[0060] Compared with the prior art, the present application has the following technical effects:

[0061] (1) The present application utilizes the unmanned aerial vehicle close-up photogrammetry technology to realize the dangerous rock survey, greatly reduces the manpower and material resources cost in the traditional survey, improves the personal safety guarantee of the survey personnel, enhances the accuracy and breadth of the dangerous rock survey, and the unmanned aerial vehicle can be used repeatedly for a long time, improving the survey efficiency; on the basis of the conventional close-up photogrammetry route, the present application carries out multi-view compensation shooting on part of the structural surface, removes the visual blind area, realizes the acquisition of multi-view and high-precision slope rock mass information, and thus establishes a three-dimensional slope model with higher precision.

[0062] (2) The present application realizes the intelligent recognition of dangerous rock and the judgment of stability by the improved KNN algorithm, GASAK algorithm and the combination of the equatorial projection method through the central control subsystem, thereby forming a complete system from the dangerous rock survey, data acquisition, dangerous rock recognition and stability analysis, realizing the intelligent recognition of dangerous rock and the judgment of stability.

[0063] (3) The present application uploads the three-dimensional point cloud data obtained by the unmanned aerial vehicle survey to the central control subsystem for processing, realizes the unmanned survey and real-time data transmission, and the central control subsystem can process the stability of the dangerous rock at the first time after the survey is completed, so that the protection personnel can propose the corresponding prevention and control measures more timely according to the stability evaluation result, helping to improve the prevention and control efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to make the purpose, technical scheme and advantages of the application more clear, the application will be further described in detail below with reference to the drawings, in which:

[0065] Figure 1 The figure is a functional structure diagram of the dangerous rock intelligent recognition and stability analysis system based on non-contact measurement disclosed by the present application.

[0066] Figure 2 The figure is a non-contact measurement schematic diagram of the unmanned aerial vehicle of the embodiment of the present application.

[0067] Figure 3 The figure is a dangerous rock type schematic diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0069] The present application will be further described below in detail with reference to the drawings.

[0070] The existing dangerous rock survey and identification mainly relies on traditional contact survey means, which is difficult to perform efficient and accurate measurement on the mountain peak of hundreds of meters, and the identification of dangerous rock stability often depends on the experience of engineers, which is easy to have errors. With the rise of non-contact measurement technologies such as unmanned aerial vehicles and laser radars, new solutions are provided for dangerous rock survey and identification, but there is no mature system for dangerous rock identification. In view of the above problems and deficiencies, the present application obtains three-dimensional point cloud data by non-contact measurement technology, identifies dangerous rock structure surface by improved KNN algorithm, and realizes grouping of dominant structure surface by GASAK algorithm, extracts key parameters such as structure surface trace length and spacing, and finally judges the stability of the identified dangerous rock based on the polar projection method, thereby improving the accuracy of stability identification.

[0071] As shown in Figure 1 The present embodiment discloses a dangerous rock intelligent identification and stability analysis system based on non-contact measurement, which comprises a survey and collection subsystem and a central control subsystem.

[0072] The survey and collection subsystem is used for multi-view close-up photogrammetry of dangerous rocks by unmanned aerial vehicles, collection of three-dimensional point cloud data of dangerous rocks, and uploading to the central control subsystem.

[0073] The central control subsystem is used for point cloud processing and structure surface clustering processing according to the collected three-dimensional point cloud data of dangerous rocks, judging the type of dangerous rock and whether there is potential damage to the dangerous rock according to the processing results of the point cloud processing and structure surface clustering processing, and calculating the stability coefficient of different types of dangerous rocks when there is potential damage, and obtaining the stability analysis result of the dangerous rock according to the stability coefficient of the different types of dangerous rocks.

[0074] In the present application, as Figure 2As shown, the unmanned aerial vehicle is used to survey the dangerous rock point cloud data, and the target dangerous rock is measured by laser radar multi-view close photography, that is, on the basis of the conventional route, the shooting distance is kept unchanged, the supplementary route is added, and the local area is shot from multiple angles to compensate, so as to eliminate the visual blind area, supplement the local image, make the collected dangerous rock three-dimensional point cloud data more accurate and comprehensive, and avoid the risk of manual measurement. When the unmanned aerial vehicle completes data collection, the cloud is uploaded to the central control subsystem in real time, the dangerous rock point cloud data is quickly responded and timely analyzed, and the stability analysis result of the dangerous rock is obtained, so that the protection personnel can propose corresponding prevention measures according to the stability evaluation result, and help to improve the prevention efficiency.

[0075] In specific implementation, the central control subsystem includes a point cloud processing module, a structure surface processing module, a type judgment module and a stability analysis module. The central control subsystem realizes intelligent identification of dangerous rock and judgment of stability by combining improved KNN algorithm, GASAK algorithm and equatorial projection method, thereby forming a complete system from dangerous rock survey, data collection, dangerous rock identification and stability analysis, and realizing intelligent identification of dangerous rock and judgment of stability. Each module is described in detail below.

[0076] The point cloud processing module is used to process the dangerous rock three-dimensional point cloud data collected by the surveying and collecting subsystem based on the improved KNN algorithm, to adaptively determine the number of domain points of each target point, realize dangerous rock point cloud set plane fitting and normal vector calculation, and build a dangerous rock three-dimensional point cloud model.

[0077] Specifically, the specific processing procedure of the point cloud processing module is as follows:

[0078] S101, input the dangerous rock three-dimensional point cloud data, and determine the maximum value D of the domain point number D according to the density distribution of the three-dimensional point cloud data. max And the minimum value D min , the domain point number D of the target point in the point cloud space is adaptively determined based on the improved KNN algorithm.

[0079] S102, for any target point p i =(x i ,y i ,z i ) in the dangerous rock three-dimensional point cloud data space, according to the determined domain point number D, D-1 nearest domain points of the target point are obtained, and the target point and the D-1 nearest domain points constitute a point cloud set {P}, and the center point p'=(x',y',z') of the point cloud set {P} is calculated, and the calculation formula of the center point p' is: p n =(x n ,y n ,z n) is the n-th point in the point cloud set {P}, n = 1, 2, …, D.

[0080] S103, constructing a matrix composed of three-dimensional coordinates of target point field point cloud in the point cloud set {P} and a matrix composed of three-dimensional coordinates of the center point of the target point field point cloud in the point cloud set {P} constructing a covariance matrix T is a transpose symbol;

[0081] S104, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues λ = (λ1, λ2, λ3) and corresponding eigenvectors μ = (μ1, μ2, μ3); the relationship of eigenvalue decomposition satisfies λμ = Mμ, λ1, λ2, λ3 and μ1, μ2, μ3 respectively represent the eigenvalues and eigenvectors corresponding to the three coordinate dimensions of the target point field point cloud.

[0082] S105, selecting the eigenvector corresponding to the smallest eigenvalue as the normal vector of the plane of the point cloud set {P}, and using the normal vector to establish the plane equation of each point in the point cloud set {P}.

[0083] S106, based on the plane equation of each point, using the least square method to adjust the equation parameters so that the sum of the squared perpendicular distances of all points in the point cloud set {P} to the plane is minimized, thereby determining the plane in which the target point p i = (x i , y i , z i ) is located.

[0084] S107, through the processing of steps S102-S106, the normal vector calculation and plane fitting are respectively performed on each target point in the three-dimensional point cloud data of the dangerous rock, and the plane in which each target point is located is determined, thereby constructing the three-dimensional point cloud model of the dangerous rock.

[0085] In order to improve the calculation accuracy, a feature index entropy is also introduced in the plane fitting of the dangerous rock point cloud set, and a linear index, a planar index and a dispersion index are calculated through the eigenvalue to quantify the spatial distribution characteristics of the point cloud and identify the plane region in the point cloud.

[0086] The equation of the feature index entropy is:

[0087] E = L λ ln(L λ ) + P λ ln(P λ ) + S λ ln(S λ );

[0088] L λ = (λ1+λ2) / λ1;

[0089] P λ = (λ2- λ3) / λ1;

[0090] S λ = λ3 / λ1;

[0091] wherein E is the characteristic exponential entropy, L λ is the linear exponential, P λ is the planar exponential, S λ is the scattered exponential, S λ + P λ + L λ = 1.

[0092] The coplanarity of the target point field point cloud is calculated according to the following formula:

[0093]

[0094] Suppose the plane equation is:

[0095] ax + by + cz + d = 0;

[0096] wherein a, b, c are the components of the unit normal vector of the plane in the respective coordinate axis direction, and a 2 + b 2 + c 2 = 1. d represents the distance from the origin to the plane, d = -(ax' + by' + cz'), and (x', y', z') are the three-dimensional coordinates of the center point p' of the point cloud set {P}.

[0097] In order to make the fitted plane the best plane, it is required to satisfy that the sum of squares of the vertical distances of all points in the point cloud set {P} to the plane is minimum, i.e.:

[0098]

[0099] d n = |ax n + by n + cz n + d|;

[0100] wherein D represents the number of field points contained in the point cloud set {P}; d e is the sum of squares of the vertical distances of all target points in the point cloud set to the plane, d n is the distance of any nth point p n = (x n , y n , z n ) in the point cloud set {P} to the plane, n = 1, 2, …, D.

[0101] In a specific application, a least square method can be used to fit the adjustment equation parameters to achieve the goal of minimizing the sum of squares of the perpendicular distance from all points in the point cloud set {P} to the plane, so as to determine a target point p i =(x i ,y i ,z i ) in the plane. By using the processing method of steps S102-S106 above, the normal vector calculation and plane fitting of each target point in the dangerous rock three-dimensional point cloud data can be performed respectively, and the plane in which each target point is located can be determined. Furthermore, the plane information of each target point is used to construct a dangerous rock three-dimensional point cloud model.

[0102] In the application, the improved KNN algorithm is used to adaptively determine the K value of the field point according to the field space characteristics of each target point, improve the sensitivity to the local characteristics of the point cloud data, make the point cloud set plane fitting more accurate, and secondly, the algorithm dynamically adjusts the K value based on the point cloud density distribution, optimizes the plane fitting process, and enhances the recognition ability of the complex dangerous rock structure. At the same time, the feature index entropy is introduced, and the quantization accuracy of the point cloud space distribution characteristics is further improved through eigenvalue analysis, which is helpful to more accurately identify and analyze the structure surface of the dangerous rock, so that the dangerous rock intelligent recognition and stability analysis system is more efficient and accurate in processing the dangerous rock survey in high-altitude and steep mountainous areas.

[0103] The structure surface processing module is used to improve the K-means algorithm based on the genetic simulated annealing algorithm, perform clustering analysis on the point cloud set plane processed by the point cloud processing module based on the GASAK algorithm, and identify and obtain the advantage grouping of the structure surface point cloud and the structure surface parameters of each group, the structure surface parameters including structure surface occurrence information.

[0104] The GASAK algorithm is a structure surface clustering method obtained by improving the K-means algorithm based on the genetic simulated annealing algorithm. According to the spatial vector angle formula, the acute angle θ between two structure surface normal vectors n i 、n j is:

[0105] θ=arccos|n i ·n j T |;

[0106] Then the distance d(n i ,n j ) between the unit normal vectors is:

[0107]

[0108] Then the similarity measure of two structure surfaces i and j is expressed as:

[0109] ​

[0110] For the classical K-means clustering, it is assumed that the structural surfaces are divided into L groups, each group as a cluster set, K l is the cluster center of the lth cluster set as an individual, N i is the ith structural surface in the lth cluster set as an individual, i = 1, 2, 3…, h, h represents the number of structural surfaces of the lth cluster set, l = 1, 2, 3…, L; the cluster center K l is:

[0111]

[0112] If u ij represents the structural surface N i , the membership of the cluster center K l , the objective function J represents the sum of the distance of each structural surface to the cluster center, d 2 (N i , K j ) represents the distance of the structural surface N i to the cluster center K l , then u ij and J can be expressed as:

[0113]

[0114] In the formula, m is the weight distribution coefficient, generally m = 2.

[0115] In order to make the objective function J reach the minimum value, the distance of each parameter of the structural surface to the cluster center must be as small as possible, which is realized by genetic annealing algorithm.

[0116] Therefore, specifically, the specific processing procedure of the structural surface processing module is as follows:

[0117] S201, obtaining the objective function of structural surface clustering:

[0118] In the formula, J is the objective function, that is, the sum of the distance of each structural surface to the cluster center, d 2 (N i , K l ) represents the distance of the structural surface N i to the cluster center K l , N i is the ith structural surface, i = 1, 2, 3…, h, h represents the number of structural surfaces of the lth cluster set; K l is the cluster center of the lth cluster set, l = 1, 2, 3…, L, L is the number of cluster sets;

[0119] S202, using a genetic simulated annealing algorithm to improve the K-means algorithm to minimize the objective function, complete the clustering analysis of the point cloud set plane.

[0120] Specifically, in step S202, the step of using a genetic simulated annealing algorithm to improve the K-means algorithm to minimize the objective function according to the similarity measure between the structural surfaces, and completing the clustering analysis of the point cloud set plane includes:

[0121] S2021, initialize the genetic algorithm parameters, simulated annealing algorithm parameters and K-means clustering algorithm parameters of the GASAK algorithm, set the maximum iteration number Maxgen, the initial temperature T0, the temperature cooling coefficient t, and the termination stable temperature T end ; the value range of the temperature cooling coefficient t is (0.5, 1);

[0122] S2022, randomly generate an initial clustering center matrix, set the fitness function of the GASAK algorithm and the membership function of each clustering center;

[0123] S2023, set the initial loop count variable gen = 0;

[0124] S2024, take the clustering set of the structural surface point cloud group as the individual of the population, randomly select the population, and calculate the membership u il and the individual fitness f i of each individual according to the membership function and the comfort function;

[0125] S2025, after the crossover operation on the randomly selected population, the mutation operation is performed again, and the new membership u i ' l and the new fitness f i ' of the new individual are calculated according to the fitness function and the membership function;

[0126] S2026, judge whether the new fitness is greater than the old individual fitness, when it is satisfied, the new individual replaces the old individual; when it is not satisfied, the following probability formula of the simulated annealing algorithm is used to receive the new generated clustering center, instead of the old individual:

[0127]

[0128] In the formula, P is the probability of receiving the new generated clustering center, e is the base of natural logarithm, f i ' is the fitness of the new individual generated after the current iteration update of the annealing algorithm, f i is the fitness of the old individual before the current iteration update of the annealing algorithm, K is the Boltzmann constant, T gen is the annealing temperature of the current iteration of the annealing algorithm;

[0129] S2027, judging whether gen<Maxgen, if yes, setting gen to be self plus 1, returning to step S2024, updating the individual in the population; otherwise, performing S2028;

[0130] S2028, judging whether T gen <T end , if yes, ending the GASAK algorithm and outputting the optimal solution; otherwise, performing the cooling operation, updating T gen =t·T gen , returning to step S2023.

[0131] Wherein, the membership degree u il and the individual fitness f i are respectively:

[0132]

[0133] Wherein, in the formula, K l is the cluster center of the lth cluster set of the individual, N i is the ith structural surface in the lth cluster set of the individual, i=1, 2, 3…, h, h represents the number of structural surfaces of the lth cluster set, l=1, 2, 3…, L, L is the number of cluster sets; d 2 (N i ,K l ) represents the distance from the structural surface N i to the cluster center K l ; m is a weight distribution coefficient, generally m=2.

[0134] In the application, the GASAK algorithm combines the advantages of the genetic algorithm and the simulated annealing algorithm, optimizes the K-means clustering process, and effectively improves the clustering quality and precision. Through the local search ability of simulated annealing and the global search ability of the genetic algorithm, the GASAK algorithm can more accurately identify the dangerous rock structural surface, and can maintain high clustering performance even under complex and variable geological conditions. Secondly, the algorithm realizes efficient grouping of the structural surface point cloud by calculating the included angle and similarity measurement between the structural surface normal vectors, provides key structural surface parameters for intelligent identification and stability analysis of dangerous rocks, and effectively avoids local optimal solution through the iterative optimization process of the GASAK algorithm.

[0135] The type judgment module is used for identifying the dangerous rock structural surface characteristics according to the dangerous rock three-dimensional point cloud model and the structural surface parameters, and obtaining a dangerous rock type judgment result according to the dangerous rock structural surface characteristics.

[0136] Specifically, the dangerous rock type judgment result includes: compression-shear slip type, tension-shear overturning type and composite falling type; the judgment principle used by the type judgment module is:

[0137] The compression-shear slip type: the main control structural plane of the dangerous rock mass is generally controlled by a weak interlayer with a low dip angle or an unloading tensile structural plane in the slope, the dip angle of the main control structural plane is very small, usually ≤45°, and the gravity center is located inside the main control structural plane. Under the influence of gravity and external load, the main control structural plane deteriorates and penetrates, and gradually slips, and the whole shows compression-shear instability.

[0138] The tension-shear overturning type: the main control structural plane is an unloading tensile type of steep (side) slope, and the dip angle is usually > 25°. The lower end of the main control structural plane is behind the rock cavity or the free surface. Under the influence of gravity and external load, the dangerous rock mass generally rotates around the intersection point of the low end of the main control structural plane or the bottom of the dangerous rock mass and the free surface, thereby causing toppling failure.

[0139] The composite falling type: controlled by two main control structural planes, including a first main control structural plane close to the horizontal and a second main control structural plane with a dip angle < 80°. The first main control structural plane mainly occurs tensile failure, and the second main control structural plane mainly occurs compression-shear failure. The first main control structural plane is affected by tension, and the crack gradually expands and extends to a certain range. Then the dangerous rock mass starts to slide along the second main control structural plane. When the deformation reaches the critical value, the dangerous rock mass loses stability and falls.

[0140] In specific implementation, the type judgment module can use a classification data table matching program code to realize the judgment and identification of the dangerous rock types of the above-mentioned compression-shear slip type, tension-shear overturning type and composite falling type, or use a commonly used classification network tool in the prior art for data feature classification, such as an SVM (Support Vector Machine) classifier or a BP (Back Propagation) neural network, to realize the judgment and identification. Thus, according to the dangerous rock three-dimensional point cloud model and the structural plane parameters, the dangerous rock type judgment result is identified and determined.

[0141] The stability analysis module is used to qualitatively analyze the dangerous rock based on the polar projection method and the structural plane parameters obtained by the structural plane processing module according to the dangerous rock type judgment result obtained by the type judgment module, to judge whether the dangerous rock has potential damage. When it is determined that the dangerous rock has potential damage, the stability coefficients of different dangerous rock types are calculated to quantitatively evaluate the stability of the dangerous rock.

[0142] In the stability analysis module, based on the stereographic projection method and combined with the structural surface parameters obtained from the structural surface processing module, a qualitative analysis of the unstable rock is performed to determine whether the unstable rock has potential damage. The specific process is as follows: First, the three-dimensional structural surface parameters are projected onto a two-dimensional stereographic plane to obtain the stereographic projection map of the structural surface. Then, based on the stereographic projection map, the interrelationships and spatial distribution of the structural surfaces are identified, and the failure mode regions of the unstable rock are analyzed. The failure modes include sliding failure and toppling failure. Finally, by evaluating the position of the extreme point of the intersection line of the structural surfaces on the stereographic projection map, it is determined whether the unstable rock is located in the failure mode region. If the unstable rock is located in the failure mode region, it is determined that the unstable rock has potential damage.

[0143] Specifically, the principle of stereographic projection for qualitative analysis of rock mass stability is as follows: Using a sphere as a projection tool, three-dimensional structural surfaces are projected onto the sphere's two-dimensional stereographic plane in the form of points and lines. The angles and combinations between structural surfaces are displayed on the plane, used to determine the cutting direction, sliding direction, failure mode, and boundary conditions of the slope rock mass, thereby assessing the stability of the slope rock mass. Based on the interrelationships of the dip angle of a single structural surface, the slope angle, the friction angle of the slope rock mass, and the dip angles of the intersection lines of each set of structural surfaces, the failure modes of the slope rock mass can be broadly classified into two types: sliding failure and toppling failure. Sliding failure is further divided into planar sliding and wedge sliding based on the number of structural surfaces cutting the rock mass. Planar sliding is rock block sliding failure caused by the cutting of a set of main controlling structural surfaces; wedge sliding is failure caused by the formation of a wedge by the cutting of two sets of structural surfaces, resulting in wedge sliding. Toppling failure is further divided into direct toppling and flexural toppling based on the failure mode and dip angle.

[0144] First, potential sliding and toppling failure zones are drawn on the stereographic projection plane. Then, the stability of the unstable rock is determined by whether the poles of structural planes and their intersection lines fall into the sliding or toppling zones. When the pole of a structural plane or its intersection line falls into the sliding or toppling zone, it indicates that the plane represented by that structural plane or the wedge represented by its intersection line has a potential risk of sliding failure or toppling failure. Correspondingly, in assessing the potential failure of unstable rock, if the pole of a structural plane or its intersection line falls within the failure mode area, the unstable rock is determined to have potential failure.

[0145] In this invention, based on the judgment result of the type judgment module, the stability coefficient of different types of unstable rocks is calculated to quantitatively assess the stability of the unstable rocks, such as... Figure 3 As shown, the specific method for calculating the stability coefficient of compression-shear slip type unstable rock is as follows:

[0146]

[0147] Method for calculating the stability coefficient of tension-shear overturning unstable rock:

[0148] The overturning point is on the inside:

[0149] The overturning point is on the outside:

[0150] Method for calculating the stability coefficient of composite fall-type unstable rock:

[0151]

[0152] In the formula, W is the self-weight of the unstable rock mass, P is the horizontal seismic force, β is the dip angle of the main controlling structural plane, and C, Let Q be the shear strength parameter, and Q be the water pressure on the main structural surface controlled by the unstable rock mass, Q = 0.5γ. w e1 2 l q , l q e1 is the length of the unstable rock mass along the direction of the steep cliff or slope, e1 is the vertical height of the water filling of the main controlling structural surface, a is the horizontal distance from the center of gravity of the unstable rock mass to the exposed surface of the base, and f is the length of the unstable rock mass along the direction of the steep cliff or slope. lk Here, H represents the standard value of the tensile strength of the unstable rock mass, H is the vertical height of the unstable rock mass, h1 is the vertical height of the section through which the main controlling structural plane is connected, and l is the vertical height of the unstable rock mass. b f is the contact length between the unstable rock mass and the base. 0k denoted as the standard value of the tensile strength between the unstable rock mass and the base, where P is the horizontal seismic force and h0 is the vertical distance from the center of gravity of the unstable rock mass to the exposed surface of the base.

[0153] In this invention, the stability of unstable rocks is analyzed using stereographic projection, which projects three-dimensional structural parameters onto a two-dimensional stereographic plane. This simplifies the visualization and analysis of complex geological structures, enabling a more intuitive understanding of the structural characteristics and spatial relationships of unstable rocks. Secondly, stereographic projection effectively identifies potential sliding and toppling failure zones by displaying the angles and combinations between structural planes, providing a qualitative basis for judging the stability of unstable rocks. Simultaneously, this invention incorporates quantitative analysis to calculate the stability coefficient of unstable rocks, further improving the accuracy of the assessment.

[0154] In summary, the present application adopts unmanned aerial vehicle to survey dangerous rock point cloud data, and through laser radar, multi-view close photography measurement is carried out on the target dangerous rock, and local area is shot from multiple angles to compensate, so as to remove the visual blind area, supplement the local image, make the collected dangerous rock three-dimensional point cloud data more accurate and more comprehensive, and avoid the risk of manual measurement; the improved KNN algorithm is used, so that the field point number K value can be adaptively determined according to the field space characteristics of each target point, the sensitivity to the local characteristics of the point cloud data is improved, and the point cloud set plane fitting is more accurate; the GASAK algorithm is used, which combines the advantages of genetic algorithm and simulated annealing algorithm, optimizes the K-means clustering process, and effectively improves the clustering quality and precision; the stability of the dangerous rock is analyzed by the equatorial projection method, the three-dimensional structure surface parameters are projected onto the two-dimensional equatorial plane, the visualization and analysis process of the complex geological structure are simplified, so that the structure characteristics and spatial relationship of the dangerous rock can be intuitively understood, and the potential sliding and dumping damage area is effectively identified, thereby providing a qualitative basis for judging the stability of the dangerous rock.

[0155] The dangerous rock intelligent identification and stability analysis system based on non-contact measurement can comprehensively and accurately survey the dangerous rock in the western region, improve the operation efficiency through unmanned and intelligent operation, and save the operation cost. Based on the dangerous rock three-dimensional point cloud model and the preset algorithm program, accurate dangerous rock body geometric information and structure surface parameter information can be obtained, the dangerous rock type can be judged, the stability of the dangerous rock body can be qualitatively analyzed and quantitatively calculated, and a whole process scheme of dangerous rock survey, parameter extraction, stability judgment and disaster prevention in the western mountainous area can be formed.

[0156] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described by referring to the preferred embodiments of the present application, those skilled in the art should understand that various changes can be made in form and details without departing from the spirit and scope of the present application defined in the appended claims.

Claims

1. A dangerous rock intelligent identification and stability analysis system based on non-contact measurement, characterized in that, The system comprises a surveying and collecting subsystem and a central control subsystem; The surveying and collecting subsystem is configured to perform multi-view close-range photogrammetry on the dangerous rock by using a UAV, collect three-dimensional point cloud data of the dangerous rock, and upload the three-dimensional point cloud data to the central control subsystem; The central control subsystem is configured to perform point cloud processing and structural surface clustering processing on the collected three-dimensional point cloud data of the dangerous rock, determine the type of the dangerous rock and whether the dangerous rock has potential damage according to the processing results of the point cloud processing and the structural surface clustering processing, and calculate the stability coefficients of different types of dangerous rocks when the dangerous rock has potential damage, so as to obtain a stability analysis result of the dangerous rock according to the stability coefficients of the different types of dangerous rocks. The central control subsystem comprises a point cloud processing module, and the specific processing procedure of the point cloud processing module is as follows: S101、input the dangerous rock three-dimensional point cloud data, determine the maximum value D of the field point number D according to the density distribution of the three-dimensional point cloud data max And the minimum value D min , the field point number D of the target point in the point cloud space is adaptively determined based on the improved KNN algorithm; S102、For any one target point in the three-dimensional point cloud data space of dangerous rock According to the determined number of domain points D, D-1 nearest domain points of the target point domain are obtained, and the target point constitutes a point cloud set {P}, and the center point of the point cloud set {P} is calculated ; S103, constructing a matrix composed of three-dimensional coordinates of the target point field point cloud in the point cloud set {P} and a matrix composed of three-dimensional coordinates of the target point field point cloud center in the point cloud set {P} , constructing a covariance matrix ; is a transpose symbol; S104, eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues and corresponding eigenvectors ; The eigenvalue decomposition satisfies the relationship , and respectively represent the eigenvalues and eigenvectors corresponding to the three coordinate dimensions of the point cloud of the target point region. S105, selecting a feature vector corresponding to the minimum eigenvalue as a normal vector of a plane of the point cloud set {P}, and establishing a plane equation of each point in the point cloud set {P} by using the normal vector; S106, based on the plane equation of each point, the least square method is used to fit and adjust the equation parameters, so that the square sum of the vertical distance of all points in the point cloud set {P} to the plane is minimum, thereby determining the target point The plane where the target point is located; S107, performing normal vector calculation and plane fitting on each target point in the three-dimensional point cloud data of the dangerous rock respectively by the processing of steps S102-S106, determining the plane where each target point is located, and thus constructing a three-dimensional point cloud model of the dangerous rock.

2. The dangerous rock intelligent identification and stability analysis system based on non-contact measurement according to claim 1, characterized in that, The central control subsystem further comprises a structural surface processing module, a type judgment module, and a stability analysis module; The structural surface processing module is configured to improve the K-means algorithm according to a genetic simulated annealing algorithm, perform clustering analysis on the point cloud set plane obtained by the point cloud processing module by using the GASAK algorithm, identify an advantage grouping of structural surface point clouds and structural surface parameters of each group, and the structural surface parameters comprise structural surface occurrence information; The type judgment module is configured to identify a structural surface feature of the dangerous rock according to the three-dimensional point cloud model of the dangerous rock and the structural surface parameters, and obtain a type judgment result of the dangerous rock according to the structural surface feature of the dangerous rock; The stability analysis module is configured to perform qualitative analysis on the dangerous rock based on the equatorial projection method and in combination with the structural surface parameters obtained by the structural surface processing module according to the type judgment result of the dangerous rock obtained by the type judgment module, determine whether the dangerous rock has potential damage, and perform stability coefficient calculation on different types of dangerous rocks when it is determined that the dangerous rock has potential damage, so as to quantitatively evaluate the stability of the dangerous rock. 3.The dangerous rock intelligent identification and stability analysis system based on non-contact measurement of claim 1, wherein, In step S106, the least square method is used to fit and adjust the equation parameters, so that the objective function of minimizing the square sum of the vertical distance of all points in the point cloud set {P} to the plane is: ； ； In the formula, D represents the number of field points contained in the point cloud set {P}; is the sum of the square of the vertical distance of all target points in the point cloud set to the plane, is the distance of any i-th point in the point cloud set {P} to the plane, ; a, b, c are the components of the unit normal vector of the plane in the x, y, z coordinate axis directions; d represents the distance of the origin to the plane.​​ 4. The dangerous rock intelligent identification and stability analysis system based on non-contact measurement according to claim 1, characterized in that, The plane fitting of the dangerous rock point cloud set further comprises: introducing a feature entropy, calculating a linear index, a planar index, and a dispersion index by using the eigenvalue, quantifying the spatial distribution characteristics of the point cloud, and identifying a planar region in the point cloud; and an equation of the feature entropy is: ； ； ； ； wherein is the characteristic exponent entropy, is the linear exponent, is the surface exponent, is the dispersion exponent, S λ + P λ + L λ = 1.

5. The dangerous rock intelligent identification and stability analysis system based on non-contact measurement according to claim 2, characterized in that, The specific processing procedure of the structural surface processing module is as follows: S201, acquiring a target function of structure surface clustering: ;​ In the formula, The objective function is the sum of the distances from each structural surface to the cluster center. Representing structural planes To the cluster center distance, Let h be the i-th structural surface, i = 1, 2, 3, ..., h, where h represents the i-th structural surface. The number of structural surfaces in each cluster set; For the first Cluster centers of a cluster set =1,2,3,…,L, where L is the number of clusters; S202, improving the K-means algorithm by using the genetic simulated annealing algorithm to minimize the objective function, and completing clustering analysis on the point cloud set plane.

6. The dangerous rock intelligent identification and stability analysis system based on non-contact measurement according to claim 5, characterized in that, In step S202, the step of improving the K-means algorithm by using the genetic simulated annealing algorithm to minimize the objective function and completing clustering analysis on the point cloud set plane according to the similarity measurement between the structural surfaces comprises: S2021、initialize the genetic algorithm parameters, simulated annealing algorithm parameters and K-means clustering algorithm parameters of the GASAK algorithm, set the maximum iteration number Maxgen, the initial temperature T0, the temperature cooling coefficient t, and the termination stable temperature T end ; the temperature cooling coefficient t is in the range of (0.5, 1); S2022, randomly generate an initial clustering center matrix, set the fitness function of the GASAK algorithm and the membership function of each clustering center; S2023, set initial loop count variable = 0; S2024, take the clustering set grouped by the structural surface point cloud as an individual of the population, randomly select the population, and calculate the membership of each individual according to the membership function and the comfort function and individual fitness ; S2025. After performing crossover on the randomly selected population, perform mutation, and recalculate the new membership degree for the newly generated individuals based on the fitness function and membership function. and new adaptability ; S2026, determine whether the new fitness is greater than the old individual fitness, when satisfied, the newly generated individual replaces the old individual; when not satisfied, the following probability formula of the simulated annealing algorithm is used to receive the newly generated clustering center, replacing the old individual: ； wherein, is the probability of receiving the newly generated cluster center, is the base of the natural logarithm, is the fitness of the newly generated individual after the current annealing iteration update of the annealing algorithm, is the fitness of the old individual before the current annealing iteration update of the annealing algorithm, K is the Boltzmann constant, is the annealing temperature of the current annealing iteration of the annealing algorithm; S2027, determine whether gen<Maxgen, if yes, let gen add 1, return to step S2024, update the individuals in the population; otherwise, proceed to S2028; S2028, determine whether the condition is satisfied , if yes, end the GASAK algorithm and output the optimal solution; otherwise, perform a cooling operation, update , and return to step S2023.

7. The dangerous rock intelligent identification and stability analysis system based on non-contact measurement according to claim 6, characterized in that, affinity of the individual and individual fitness respectively: ， ； wherein, in the formula, is the cluster center of the i-th cluster set of individuals, is the cluster center of the i-th cluster set of individuals, is the i-th structural surface in the i-th cluster set of individuals, i = 1, 2, 3…, h, h represents the number of structural surfaces of the i-th cluster set, is the i-th structural surface in the i-th cluster set of individuals, i = 1, 2, 3…, h, h represents the number of structural surfaces of the i-th cluster set, is the i-th structural surface in the i-th cluster set of individuals, i = 1, 2, 3…, h, h represents the number of structural surfaces of the i-th cluster set, = 1, 2, 3…, L; L is the number of cluster sets; represents the distance from the structural surface to the cluster center ; m is a weight distribution coefficient. 8.The non-contact measurement based dangerous rock intelligent identification and stability analysis system according to claim 2, characterized in that, In the stability analysis module, based on the equatorial projection method, the structure surface parameters obtained by the structure surface processing module are used to perform qualitative analysis on the dangerous rock, and the specific process of determining whether the dangerous rock has potential damage is as follows: Firstly, the three-dimensional structure surface parameters are projected onto a two-dimensional equatorial plane to obtain an equatorial projection diagram of the structure surface, then the mutual relationship and spatial distribution of the structure surface are identified according to the equatorial projection diagram, a damage mode area of the dangerous rock is analyzed, the damage mode includes sliding damage and toppling damage, finally, whether the dangerous rock is located in the damage mode area is determined by evaluating the position of the pole of the intersection line of the structure surface on the equatorial projection diagram, if the dangerous rock is located in the damage mode area, it is determined that the dangerous rock has potential damage. 9.The non-contact measurement based dangerous rock intelligent identification and stability analysis system according to claim 1, characterized in that, The dangerous rock type judgment result includes: compression-shear sliding type, tension-shear toppling type and composite falling type; The stability coefficients of different dangerous rock types are calculated by the following formula: The stability coefficient calculation method of the compression-shear sliding type dangerous rock is: ； The stability coefficient calculation method of the tension-shear toppling type dangerous rock is: The point of overturning is at the inner side: ; Tilting point at outside: ; The stability coefficient calculation method of the composite falling type dangerous rock is: ； where W is the weight of the dangerous rock mass, P is the horizontal seismic force, β is the dip angle of the main controlling structural plane, C and φ are the shear strength parameters, Q is the water pressure of the main controlling structural plane of the dangerous rock mass, is the vertical height of the main controlling structural plane filled with water, a is the horizontal distance from the center of gravity of the dangerous rock mass to the exposed surface of the base, lk is the standard value of the tensile strength of the dangerous rock mass, H is the vertical height of the dangerous rock mass, h1 is the vertical height of the main controlling structural plane through section, l b is the contact length of the dangerous rock mass with the base, 0k is the standard value of the tensile strength between the dangerous rock mass and the base, P is the horizontal seismic force, h0 is the vertical distance from the center of gravity of the dangerous rock mass to the exposed surface of the base.

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