Perilous rock mass risk source area identification method and device

Abstract

The present application provides a dangerous rock mass risk source area identification method and device, comprising: obtaining a dangerous rock mass image, generating dangerous rock mass point cloud data, determining the occurrence information, trace length data and structure surface spacing of the dangerous rock mass structure surface; dividing the dangerous rock mass into multiple sub-regions, and determining the control type structure surface of each sub-region; determining the shear strength of each control type structure surface based on each topographic parameter, wall surface strength, roughness coefficient, normal stress and basic friction angle; determining the uniaxial compressive strength of the rock in each sub-region; determining the radius and normal density of the dominant structure surface group in each sub-region, and determining the integrity coefficient of each sub-region; determining the dangerous rock sensitivity index of each sub-region based on the maximum trace length, the minimum structure surface spacing, the shear strength, the uniaxial compressive strength and the integrity coefficient in each sub-region, and determining the risk source area of the dangerous rock mass based on the dangerous rock sensitivity index of each sub-region. The present application improves the accuracy of the identification result of the dangerous rock mass risk source area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dangerous rock mass risk source area identification, and in particular to a dangerous rock mass risk source area identification method and device. BACKGROUND

[0002] The existence of dangerous rock mass has an important influence on human life. The occurrence of rock avalanches and other disasters can cause various accidents such as casualties, infrastructure damage, and traffic facilities unable to operate normally. At present, in order to identify the risk source area of the dangerous rock mass, it is generally necessary to measure the dangerous rock mass and then extract the structural information of the dangerous rock mass. The traditional measurement methods mainly include manual measurement, such as using a geological compass, a tape measure, and other contact type structural surface information acquisition technologies. This traditional contact type measurement technology has the problems of long time consumption, low accuracy, low efficiency, and great safety hazards. In order to improve the information acquisition efficiency, a series of intelligent measurement methods have appeared, such as photogrammetry technology and three-dimensional laser scanning technology. These technologies can realize the acquisition of the information of the structural surface of the research area through the acquisition of point cloud data combined with intelligent algorithms. For example, a Chinese patent with the publication number CN201711108494 and the invention name "Multi-angle identification method for structural surface occurrence and properties based on unmanned aerial vehicle" discloses a multi-angle identification method for the spatial distribution and properties of the structural surface based on an unmanned aerial vehicle.

[0003] In the above-mentioned prior art, the intelligent measurement method can extract the geometric characteristics of the structural surface of the dangerous rock mass, and the geometric characteristic parameters of the structural surface mainly include the occurrence of the structural surface, the trace length, and the interval information. However, the prior art does not disclose the strength, deformation characteristics, and failure mechanism of the dangerous rock mass, which can affect the instability of the dangerous rock mass, so that the method for identifying the instability state of the dangerous rock mass based on only the geometric parameters of the structural surface of the dangerous rock mass in the prior art has the disadvantage of inaccurate identification result, and thus cannot accurately identify the risk source area of the dangerous rock mass. Therefore, how to improve the accuracy of the identification result of the risk source area of the dangerous rock mass is a technical problem to be solved. SUMMARY

[0004] In view of this, the embodiments of the present application provide a dangerous rock mass risk source area identification method and device to eliminate or improve one or more defects in the prior art.

[0005] One aspect of the present application provides a dangerous rock mass risk source area identification method, which comprises:

[0006] obtaining a dangerous rock mass image, generating dangerous rock mass point cloud data based on the dangerous rock mass image, and determining the occurrence information, trace length data, and structural surface interval of the dangerous rock mass structural surface based on the dangerous rock mass point cloud data;

[0007] divide the dangerous rock mass into a plurality of sub-regions based on the point cloud data of the dangerous rock mass, determine a control-type structure surface of each of the sub-regions based on structure surfaces of dangerous rock mass in each of the sub-regions;

[0008] determine the shear strength of each of the control-type structure surfaces based on a topographic parameter, a wall surface strength, a roughness coefficient, a normal stress and a basic friction angle of each of the control-type structure surfaces;

[0009] obtain a temperature cloud atlas of the dangerous rock mass, and determine the uniaxial compressive strength of rock in each of the sub-regions based on the temperature cloud atlas;

[0010] determine a dominant structure surface group in each of the sub-regions, obtain a radius and a normal density of each of the dominant structure surface groups, and determine an integrity coefficient of each of the sub-regions based on the radius and the normal density of each of the dominant structure surface groups;

[0011] determine a dangerous rock sensitivity index of each of the sub-regions based on a maximum trace length, a minimum spacing of structure surfaces, a shear strength, a uniaxial compressive strength and an integrity coefficient in each of the sub-regions, and determine a risk source area of the dangerous rock mass based on the dangerous rock sensitivity index of each of the sub-regions.

[0012] In some embodiments of the present application, the calculation formula of the shear strength is:

[0013] ;

[0014] ;

[0015] ;

[0016] wherein M is a topographic parameter, is the shear strength, is the normal stress, is the wall surface strength, γ is the rock specific gravity, and L is the structure surface profile length, is the number of rising sections of the profile, is the number of rising sections of each of the undulations, F ij is the undulation parameter of the rising section of the i th undulation of the j th profile, and N is the impact test rebound value.

[0017] In some embodiments of the present application, the uniaxial compressive strength of rock in each of the sub-regions is determined based on the temperature cloud atlas, which comprises:

[0018] determining rock mass homogeneous regions in each of the sub-regions and a temperature rise curve of each of the rock mass homogeneous regions based on the temperature cloud atlas, and determining an initial temperature corresponding to each of the rock mass homogeneous regions and a heat flux density of a rock mass surface;

[0019] calculate heat absorption coefficients of the rocks in each of the rock mass homogeneous zones in a heating process based on the heating curve, the initial temperature, and the heat flow density;

[0020] determine the uniaxial compressive strength of the rocks in the sub-region based on the average heat absorption coefficient of the plurality of rock mass homogeneous zones in the sub-region.

[0021] In some embodiments of the present application, the formula for calculating the uniaxial compressive strength is:

[0022] ;

[0023] wherein R C is the uniaxial compressive strength, A and B are both lithology influence factors, is the average heat absorption coefficient.

[0024] In some embodiments of the present application, the integrity coefficient of each of the sub-regions is determined based on the radius and the normal density of each of the dominant structural plane groups, including:

[0025] determine the volume joint number of each of the rock mass homogeneous zones based on the radius and the normal density of each of the dominant structural plane groups;

[0026] determine the integrity coefficient of each of the sub-regions based on the average volume joint number of all the rock mass homogeneous zones in each of the sub-regions.

[0027] In some embodiments of the present application, the formula for calculating the integrity coefficient of the sub-region is:

[0028] ;

[0029] wherein, is a structural plane category correction coefficient, is the integrity coefficient, is the average volume joint number.

[0030] In some embodiments of the present application, the dangerous rock sensitivity index of each of the sub-regions is determined based on the maximum trace length, the minimum structural plane spacing, the shear strength, the uniaxial compressive strength, and the integrity coefficient in each of the sub-regions, including:

[0031] calculate the sum of the maximum trace length, the minimum structural plane spacing, the shear strength, the uniaxial compressive strength, and the integrity coefficient in the sub-region;

[0032] use the calculated sum as the dangerous rock sensitivity index of the corresponding sub-region.

[0033] In some embodiments of the present application, the expression of the first structural plane is: , the expression of the second structural plane is: , and the formula for calculating the structural plane spacing is ;

[0034] wherein, is a unit normal vector of the structural plane, and are constants for determining the positions of the first structural plane and the second structural plane in the spatial coordinates, respectively.

[0035] According to another aspect of the present application, there is also disclosed a dangerous rock mass risk source area identification system, comprising a processor, a memory and a computer program stored on the memory, the processor being configured to execute the computer program, and the system implementing the steps of the method according to any one of the above embodiments when the computer program is executed.

[0036] According to still another aspect of the present application, there is also disclosed a computer readable storage medium having stored thereon a computer program, the computer program being configured to implement the steps of the method according to any one of the above embodiments when executed by a processor.

[0037] The dangerous rock mass risk source area identification method and device disclosed in the above embodiments of the present application firstly acquire the geometric characteristic parameters of the structural plane of the dangerous rock mass, then determine the mechanical parameters of the dangerous rock mass, and the mechanical parameters include the shear strength of the control-type structural plane, the uniaxial compressive strength of the rock and the integrity coefficient of the sub-area, and finally combine the mechanical parameters and the geometric characteristic parameters to identify the risk source area of the dangerous rock mass, so that the geometric characteristic parameters and the mechanical parameters of the dangerous rock mass can be accurately obtained, and the accuracy of the identification result of the risk source area of the dangerous rock mass can be improved.

[0038] Additional advantages, objects, and features of the application will be set forth in part by the description that follows, and will become apparent to those skilled in the art upon examination of the following figures and detailed description thereof or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0039] It will be appreciated by those skilled in the art that the objects and advantages of the application can be fulfilled by the application not specifically described in the specification and claims and that the application can be practiced in a variety of ways not specifically described in the specification and claims. The application is not limited to the exact details shown and described, for purposes of illustration. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description, explain the principles of the application. The components in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. For purposes of clarity and understanding, it is expressly intended that some portions of the drawings be shown exaggerated in scale, or out of proportion, to illustrate aspects of the application. In the drawings:

[0041] Figure 1 A flowchart of a dangerous rock mass risk source area identification method according to an embodiment of the present application.

[0042] Figure 2 A flowchart of a dangerous rock mass risk source area identification method according to another embodiment of the present application.

[0043] Figure 3 A schematic diagram of a UAV ground-emulating flight according to an embodiment of the present application.

[0044] Figure 4 A schematic diagram of a dangerous rock mass geometric feature parameter statistical result according to an embodiment of the present application.

[0045] Figure 5 A flowchart of a Geomagic software processing dangerous rock mass point cloud data according to an embodiment of the present application.

[0046] Figure 6 A schematic diagram of a corresponding relationship between normal stress and shear strength according to an embodiment of the present application.

[0047] Figure 7 A dangerous rock mass risk source area identification result diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to embodiments and drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0049] It should be noted that, in order to avoid the present application being obscured by unnecessary details, only structures and / or processing steps closely related to the solutions according to the present application are shown in the drawings, and other details not closely related to the present application are omitted.

[0050] It should be emphasized that the term “comprises / comprising” is used herein to indicate the presence of a feature, element, step or component, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0051] It should be noted that, unless otherwise specified, the term “connection” used herein can not only mean direct connection, but also indirect connection with an intermediate object, and can not only mean wired connection, but also wireless connection, which can be changed based on actual application scenarios.

[0052] The geometric characteristic parameters of the structure surface of the dangerous rock mass mainly include the occurrence, trace length and interval of the structure surface, which are helpful to accurately describe the spatial structure and geometric shape of the dangerous rock mass. The mechanical parameters such as compressive strength, shear strength and integrity coefficient can reveal the strength, deformation characteristics and failure mechanism of the dangerous rock mass. The inventors find that the comprehensive consideration of the two factors can improve the accuracy of the identification result of the dangerous rock mass risk source area; therefore, the geometric parameters and mechanical parameters of the structure surface of the dangerous rock mass are jointly represented, and the stability of the dangerous rock mass is analyzed based on the two types of parameters.

[0053] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0054] Figure 1 The flowchart of the dangerous rock mass risk source area identification method of an embodiment of the present application is shown in FIG. 1, which at least includes steps S10 to S60. Figure 1

[0055] Step S10: obtaining a dangerous rock mass image, generating dangerous rock mass point cloud data based on the dangerous rock mass image, and determining the occurrence information, trace length data and structure surface interval of the structure surface of the dangerous rock mass based on the dangerous rock mass point cloud data.

[0056] In this step, the geometric characteristic information of the structure surface of the dangerous rock mass is extracted based on the point cloud data of the dangerous rock mass image. For example, when obtaining the dangerous rock mass image, the unmanned aerial vehicle (UAV) flies along the ground to obtain multiple near-range image images of the target area obtained based on the UAV photogrammetry technology, which satisfy the picture overlap rate and the ground resolution. When designing the flight route of the UAV, the contour lines can be generated by using the digital surface model (DSM) of the target area, and then the contour lines are simplified, offset, etc. to complete the design of the flight route of the UAV. The flight route of the UAV is shown in FIG. 2. Figure 3

[0057] For example, the dense dangerous rock mass point cloud data can be generated based on the structure from motion (SfM) technology. In addition, before generating the dangerous rock mass point cloud data, the multiple near-range image images obtained based on the UAV photogrammetry technology can be preprocessed, such as distortion correction, image alignment, etc., and then the preprocessed multiple near-range image images are input into the Agisoft Metashape software, so as to generate the dense point cloud data based on the structure from motion technology, thereby obtaining the refined topographic data of the target area.

[0058] ​​Further, based on the obtained point cloud data of the dangerous rock mass, geometric characteristic parameters of the structural plane are extracted, including occurrence information, trace length data, and structural plane spacing. The statistical results of the geometric characteristic parameters of the structural plane of the dangerous rock mass are shown in FIG. 8. Specifically, the generated point cloud data of the dangerous rock mass can be clustered and planarly segmented by combining unsupervised clustering technology and RANSAC model fitting. The point cloud data of each structural plane is fitted by the RANSAC model. The geometric parameters of the structural plane of the dangerous rock mass are represented based on geometric analysis, NTV, and other methods. Figure 4 Specifically, the generated point cloud data of the dangerous rock mass can be clustered and planarly segmented by combining unsupervised clustering technology and RANSAC model fitting. The point cloud data of each structural plane is fitted by the RANSAC model. The geometric parameters of the structural plane of the dangerous rock mass are represented based on geometric analysis, NTV, and other methods.

[0059] For example, when the occurrence information of the structural plane of the dangerous rock mass is extracted, the point cloud data of the dangerous rock mass can be preprocessed by down-sampling based on a voxel grid filter. Then, the point cloud data of the dangerous rock mass is spatially divided by a k-d tree, and k nearest neighbors are selected to form a covariance matrix. For example, the 3x3 covariance matrix of a sample point q is as follows: where A is the covariance matrix, is the local centroid of the neighborhood of point q, and k represents the number of nearest neighbors. Further, the normal vector of each point is estimated by singular value decomposition (SVD) of the covariance matrix. In this step, singular value decomposition (SVD) is performed on the covariance matrix, the eigenvector , and the corresponding eigenvalue to obtain , which corresponds to the eigenvector with the smallest eigenvalue is the unit normal vector of the sample point. Further, based on the principal clustering algorithm, the normal vectors of the points are clustered according to the similarity of the directions of the normal vectors. For example, the normal vectors obtained in the above steps are taken as the input information of the mean-shift clustering algorithm, so that vectors with similar directions are found based on the mean-shift clustering algorithm.

[0060] ​Further, the auxiliary clustering algorithm based on 3D XYZ coordinates can separate single planes in the clustering set; for example, starting from a random starting coordinate point q in the main cluster, a radial distance is defined to form a neighborhood around the selected starting point, if there are the least number of points within this radius, the point q is defined as a core point, and when the number of points in the neighborhood is less than the minimum number of points, the point is defined as a boundary point, all points within the core point and the radial distance are assigned to a cluster, and each core point not in the cluster forms a new cluster. A plane is fitted to each clustering set in the previous step using the RANSAC algorithm, and the discontinuous direction is calculated; specifically, first, three non-collinear points are randomly selected for the plane in the point cloud data, and the non-collinear points are used to define the plane equation parameters, next, adjacent points are iteratively checked to determine whether they fall within a defined threshold distance, points within this threshold are defined as an inlier set; otherwise, they will be treated as outliers; the algorithm fits multiple planes, only accepting the plane with the most points in the consensus set, therefore, the consensus set constitutes the first plane of the data; then the points corresponding to the first consensus set are omitted from the search domain, and the RANSAC algorithm is run again to find the next plane; repeat this process to identify all planes in the data. The plane equation produced by the RANSAC algorithm is in the form (mx +ny +lz +d = 0), where m, n, and l are the unit normal vector components of the best-fit plane; further, the inclination and inclination direction of the plane are calculated using the following equation:

[0061] ;

[0062] .

[0063] When extracting the trace length data of the structure surface of the dangerous rock mass, the following steps can be included:

[0064] (1) identifying trace feature points using grid vertices around the trace;

[0065] Define a triangular mesh represented by M = (P, L, V), is the set of vertices, L is the set of edges, is the set of faces. Each vertex is represented by a Cartesian coordinate, denoted as . The NTV method is used to classify the grid vertices, and the NTV of each vertex p is defined as:

[0066] ;

[0067] where, is the unit normal vector of , and is the single-ring adjacent face index set of point , and the weight coefficient is defined as:

[0068] ;

[0069] where is the area of triangle , is the maximum area of , is the centroid of triangle , is the edge length of the cube defining the neighborhood of each vertex.

[0070] Two thresholds a and b are defined to control the accuracy of the identification of corner and edge type points; threshold a should be large enough to avoid extracting too many false corners; threshold b is a fine tuning coefficient around a value to find a trade-off between detecting weak features and the number of extra noise points; the definition of a and b depends on the visual assessment of the number of edge and corner type vertices identified.

[0071] (2) Compressing the trace feature points on their point cloud skeletons;

[0072] Extracting the curve skeleton of the point cloud by Laplacian-based shrinking algorithm; the shrinking algorithm uses local Delaunay triangulation and topological refinement to aggregate feature points on the skeleton, thus obtaining the position of the three-dimensional trace.

[0073] (3) Connecting the trace feature points belonging to the same trace;

[0074] The above process generates shrunk feature points, which are clustered on their point skeletons; this step is to connect these points to generate tracking segments. The connection neighbors of the shrunk feature points are defined as the Delaunay neighbors within the connection radius. Since shrinking can expand the interval of feature points belonging to the same trace segment, through data testing, the connection radius is defined to be greater than the shrinking radius, which is 3 times the average edge length of the triangular mesh.

[0075] (4) Linearizing the trace segment segmented into linearized segments;

[0076] Since the feature point connection algorithm of the above process may incorrectly connect feature points of different traces, this step linearly segments the trace segment to generate linearized segments composed only of feature points of the same trace. Given a set of points , principal component analysis starts from calculating the covariance matrix:

[0077] ;

[0078] where c is the number of points, is the centroid coordinate of the point cloud. Because the matrix is symmetric and positive, it can be eigenvalue decomposed as:

[0079] ;

[0080] Its eigenvalues are Then the parameter q is used to measure the degree of linearization, defined as

[0081] ;

[0082] (5) The trace segments belonging to the same trace are connected.

[0083] The trace segment connection rule is defined as follows:

[0084] ① Axial distance is less than the threshold value D of controlling axial expansion, and when is less than is less than (defined as 3 times the average edge length of the triangular mesh through data testing);

[0085] ② Radial distance is less than the threshold value of controlling radial expansion;

[0086] ③ Angle is less than the threshold value of controlling trace curvature range;

[0087] ④ Let set S consist of trace segments that meet the above three conditions, then the line segment to be connected is the line segment with the smallest radial distance in S.

[0088] Exemplarily, the expression of the first structural plane is: , the expression of the second structural plane is: , and the calculation formula of the structural plane spacing is: ; wherein, is the unit normal vector of the structural plane, and are constants used to determine the positions of the first structural plane and the second structural plane in the spatial coordinates, and x, y, and z represent three directions of the spatial rectangular coordinate system.

[0089] Step S20: Based on the dangerous rock mass point cloud data, the dangerous rock mass is divided into multiple sub-regions, and based on the dangerous rock mass structure plane in each sub-region, the control type structure plane of each sub-region is determined.

[0090] In this step, in order to improve the calculation efficiency, the slope model area is divided according to the differences of local topography and rock mass structure development characteristics, such as Figure 7 ​As shown, the dangerous rock mass can be divided into 9 sub-regions. Further, the control-type structural plane of each sub-region is determined from all the control planes of the sub-region, and the control-type structural plane is the structural plane most prone to failure, which can be obtained by artificial observation.

[0091] Step S30: Determine the shear strength of each control-type structural plane based on the topographic parameters, wall strength, roughness coefficient, normal stress, and basic friction angle of the control-type structural plane.

[0092] In this step, the shear strength of the control-type structural plane is further determined based on the structural data of the control-type structural plane . The structural data of the control-type structural plane includes topographic parameters, wall strength, roughness coefficient, normal stress, and basic friction angle.

[0093] Before obtaining the structural data of the control-type structural plane, the point cloud data of the control-type structural plane in each sub-region can be obtained from the point cloud data of the dangerous rock mass obtained based on the unmanned aerial vehicle photogrammetry technology, and then the point cloud data of the control-type structural plane is imported into the Geomagic software to intercept a square region with a side length of 100 mm on the point cloud as a standard interface based on the Geomagic software, and further divide the standard interface into 101 horizontal and vertical section lines (interval 1 mm), and perform orthogonal interpolation processing on the point cloud data of the interface through MATLAB programming to provide data support for subsequent quantitative description of the topography of the control-type structural plane. The specific processing flow is as shown in Figure 5 .

[0094] In calculating the shear strength of the control-type structural plane, for example, first introduce the anisotropic topographic parameter M which can reflect the micro-slope distribution characteristics and shear mechanical effect of the control-type structural plane, to accurately characterize the topography of the control-type structural plane. The calculation formula of the anisotropic topographic parameter M is: , ; wherein M is the topographic parameter, L is the section length of the structural plane, is the number of rising sections of the profile, is the number of rising sections of each relief body, F is the relief parameter, F ij is the relief body parameter of the rising section of the jth profile, θ is the climbing slope angle, and h is the climbing height.

[0095] Further, based on the relationship between the rock specific gravity γ and the rebound value N, the wall strength JCS of the in-situ rock structural plane can be quickly estimated; in addition, based on the empirical relationship, the basic friction angle of the measured structural plane , , Wherein, N is the rebound value of impact test, JCS is the wall strength of rock structural plane, and γ is the rock density; in addition, in determining the rebound value N of impact test, the Schmidt rebound apparatus (L type, impact energy is 0.785 kN / m) can be used to apply force vertically to the relatively complete structural plane, and multiple impact tests are performed at one point to obtain the rebound value N.

[0096] In addition, according to the relative relationship between the topographic parameter M and the structural plane roughness coefficient JRC, combined with the widely used Barton-Bandis shear strength criterion of structural plane, a new empirical formula of rock mass structural plane shear strength (M-JCS model) is obtained:

[0097] ;

[0098] ;

[0099] ;

[0100] Wherein, is the normal stress of the structural plane, is the shear strength of the structural plane.

[0101] In the above embodiment, the shear strength can be obtained based on the following formula:

[0102] ;

[0103] ;

[0104] ;

[0105] Wherein, M is the topographic parameter, is the shear strength, is the normal stress, is the wall strength, γ is the rock density, and L is the length of the structural plane profile, is the number of rising sections of the profile, is the number of rising sections of each relief body, F ij is the relief body parameter of the rising section of the i-th relief body of the j-th profile, and N is the rebound value of impact test.

[0106] In addition, in order to further improve the accuracy of the obtained shear strength, the equivalent linear fitting principle can be used to fit the nonlinear M-JCS model by using the linear Mohr-Coulomb criterion, and the shear strength of the structural plane under different normal stresses can be quickly calculated, and the shear strength of each group of structural planes under different normal stresses (as shown in Figure 6 ) can be obtained by linear regression, that is, the final cohesion c and friction angle φ of the structural plane can be obtained.

[0107] Step S40: Obtain a temperature cloud image of the dangerous rock mass, and determine the uniaxial compressive strength of the rock in each of the sub-regions based on the temperature cloud image.

[0108] In this step, the uniaxial compressive strength of the rock in each of the sub-regions is determined by using the temperature cloud image. In order to obtain the temperature cloud image of the dangerous rock mass, the target object photographed by the unmanned aerial vehicle can be detected by using an infrared thermal imaging technology.

[0109] In an embodiment, the uniaxial compressive strength of the rock in each of the sub-regions is determined based on the temperature cloud image, which can specifically include the following steps: determining rock mass homogeneous regions in each of the sub-regions and a temperature rise curve of each of the rock mass homogeneous regions based on the temperature cloud image, and determining an initial temperature corresponding to each of the rock mass homogeneous regions and a heat flux density of a rock mass surface; calculating a heat absorption coefficient of the rock in each of the rock mass homogeneous regions in a temperature rise process based on the temperature rise curve, the initial temperature, and the heat flux density; and determining the uniaxial compressive strength of the rock in the sub-region based on an average heat absorption coefficient of a plurality of the rock mass homogeneous regions in the sub-region. For example, when the uniaxial compressive strength of the rock is obtained, a specified number of rock mass homogeneous regions and a temperature rise process curve of each of the rock mass homogeneous regions can be extracted from each of the sub-regions by using the temperature cloud image. When the rock mass homogeneous regions are determined, a region with good integrity and relatively uniform lithology can be selected as the rock mass homogeneous region. Further, the corresponding initial temperature is determined. At the same time, a heat flux density q of the rock mass surface corresponding to the same number of rock mass homogeneous regions is obtained. Further, the average heat absorption coefficient of the rock in the temperature rise process is calculated based on the heat flux density, the initial temperature, and a thermal excitation time. The heat absorption coefficient is defined as follows: where i is the number of the rock mass homogeneous region, λ is a rock thermal conductivity coefficient corresponding to the rock mass homogeneous region, ρ is a rock density corresponding to the rock mass homogeneous region, Ci is a rock specific heat capacity corresponding to the rock mass homogeneous region numbered i, is a first integral of a Gaussian error supplement function, is a temperature of the rock mass surface at time t. Further, the uniaxial compressive strength R C of the rock is determined by using the average heat absorption coefficient, which is specifically defined as follows: where R C is the uniaxial compressive strength, A and B are lithology influence factors, is the average heat absorption coefficient, and for example, the lithology influence factors A and B of sandstone are A=56 and B=1.51.

[0110] Step S50: Determine dominant structural plane groups in each of the sub-regions, obtain radii and normal densities of the dominant structural plane groups, and determine integrity coefficients of each of the sub-regions based on the radii and the normal densities of the structural planes in the dominant structural plane groups.

[0111] To further determine the dominant structural plane group in each sub-region, the geometric feature information of the structural planes in each sub-region can be counted, and the counted structural plane geometric feature information is plotted on the equal density graph by using the Dips software. According to the distribution density of the pole of the structural plane on the equal density graph, the structural planes are divided into different dominant groups by using the clustering method, so as to obtain the dominant structural plane group in each sub-region.

[0112] For example, when calculating the integrity coefficient of the sub-region, the specific steps can include: determining the volume joint number of each rock mass homogeneous region based on the radius and normal density of each dominant structural plane group; determining the integrity coefficient of each sub-region based on the average volume joint number of all rock mass homogeneous regions in each sub-region.

[0113] Specifically, in the three-dimensional point cloud model of the dangerous rock mass, a preset number of dangerous rock mass homogeneous regions are obtained, and the average value of the volume joint number of these regions is calculated. First, based on the trace length of each structural plane in the dominant structural plane group, the radius g corresponding to the dominant structural plane group is determined; and the following is defined: ; ; wherein, is the average trace length of the structural planes in the dominant structural plane group, is the number of structural planes in the dominant structural plane group. In this embodiment, the plurality of structural planes in each dominant structural plane group are a group of structural planes with similar occurrences, and therefore each sub-region includes at least one dominant structural plane group.

[0114] Further, based on the spacing between two adjacent dominant structural planes in the dominant structural plane group, the normal density of the dominant structural plane group is determined ; and the following is defined: , ; wherein, is the average spacing between two adjacent dominant structural planes in the dominant structural plane group, is the spacing between two adjacent structural planes, is the number of structural planes in the dominant structural plane group. Further, based on the radius and normal density corresponding to the dominant structural plane group, the volume joint number of the dangerous rock mass homogeneous region is determined ; wherein, is the preset number of rock mass homogeneous regions, i is the rock mass homogeneous region number, represents the volume joint number corresponding to the rock mass homogeneous region numbered i. Finally, the integrity coefficient of the sub-region is defined as: ; wherein, is the structural plane category correction coefficient, is the integrity coefficient, is the average volume joint number.

[0115] In an embodiment, the structural plane category correction coefficient is shown in the following table:

[0116]

[0117] Step S60: determining a dangerous rock sensitivity index of each of the sub-regions based on the maximum trace length, the minimum structural plane spacing, the shear strength, the uniaxial compressive strength and the integrity coefficient of each of the sub-regions, and determining a risk source area of the dangerous rock mass based on the dangerous rock sensitivity index of each of the sub-regions.

[0118] In this step, the risk source area of the dangerous rock mass is identified based on the combination of the geometric characteristic parameters and the mechanical parameters. In some embodiments, the maximum trace length can be used to quantify the extension of the main structural plane, indicating the degree of lack of support of the dangerous rock mass, while the minimum spacing between the same set of dominant structural planes is used as an evaluation index of the rock mass quality, and the uniaxial compressive strength Rc of the rock, the integrity coefficient of the rock mass , the shear strength of the potential sliding surface , are combined to complete the mechanical performance determination of the rock mass.

[0119] In an embodiment, the sum of the maximum trace length, the minimum structural plane spacing, the shear strength, the uniaxial compressive strength and the integrity coefficient in the sub-region can be calculated; and the sum calculated is used as the dangerous rock sensitivity index of the corresponding sub-region. In this embodiment, the dangerous rock sensitivity index , represents the maximum trace length, S represents the minimum structural plane spacing, R C represents the uniaxial compressive strength, represents the shear strength, represents the integrity coefficient.

[0120] After the dangerous rock sensitivity index of each of the sub-regions is calculated, the specific risk source area can be further determined. For example, the dangerous rock sensitivity index can divide the risk level of the sub-region into 0 to 8 levels, i.e., the risk gradually increases from 0 level to 8 level.

[0121] Furthermore, to facilitate the observation of the risk source areas of unstable rock masses, a large number of geometric and mechanical parameters of the rock mass structure can be input into a three-dimensional real-scene model to identify the risk source areas of unstable rock masses and ultimately obtain a spatial distribution map of the risk source areas. For example, a three-dimensional real-scene model of the unstable rock mass can be constructed as follows: First, in HyperMesh software, based on the geometric characteristics of the mountain structure in the study area, such as its location, scale, and attitude, corresponding control points are connected to obtain boundary lines, thus establishing a series of large-scale mountain structures. Then, based on the statistical results of the attitude, trace length, spacing, and geometric parameters of the rock mass structure surfaces, and the control of corresponding random variables, a multi-scale three-dimensional real-scene model of the rock mass structure is constructed in the study area using 3DEC software combined with Monte Carlo technology.

[0122] Figure 2 This is a flowchart illustrating another embodiment of the method for identifying risk source areas of dangerous rock masses according to the present invention, as shown below. Figure 2 As shown, the method includes the following steps: photographing and detecting the unstable rock mass based on UAV photogrammetry and infrared imaging technology; obtaining dense three-dimensional point cloud data of the unstable rock mass based on the motion recovery structure algorithm; performing cluster analysis on the structural surfaces of the unstable rock mass and extracting the geometric feature parameters of the structural surfaces in combination with intelligent algorithms; performing statistical analysis on the geometric parameters of the structural surfaces to obtain a probability distribution model of the geometric parameters of the structural surfaces; obtaining three mechanical parameters of the unstable rock mass: shear strength, compressive strength, and integrity coefficient; and constructing a three-dimensional real-scene model of the multi-scale rock mass structure to identify the risk source area of ​​the unstable rock mass.

[0123] As can be seen from the above embodiments, the method for identifying risk source areas of unstable rock masses disclosed in this invention first obtains the geometric characteristic parameters of the unstable rock mass structural planes, then determines the mechanical parameters of the unstable rock mass, including the shear strength of the controlling structural planes, the uniaxial compressive strength of the rock, and the integrity coefficient of the sub-regions. Finally, the risk source areas of the unstable rock mass are identified based on the combination of mechanical and geometric characteristic parameters. This method can not only effectively and accurately obtain the geometric characteristic parameters and mechanical parameters of unstable rock masses, but also improve the accuracy of the identification results of risk source areas of unstable rock masses.

[0124] According to another aspect of the present invention, a risk source zone identification system for dangerous rock masses is also disclosed. The system includes a processor, a memory, and a computer program stored in the memory. The processor is used to execute the computer program. When the computer program is executed, the system implements the steps of the method as described in any of the above embodiments.

[0125] The embodiments of the present application further provide a computer readable storage medium and a computer program product, and the computer program is stored on the computer readable storage medium and is executed by a processor to implement the steps of the method according to any of the above embodiments. The computer readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0126] Those skilled in the art should understand that the exemplary components, systems and methods described in connection with the embodiments disclosed herein can be implemented in hardware, software or a combination thereof. The decision to implement in hardware or software depends on the particular application and design constraints imposed on the technological solution. Those skilled in the art can use different methods to implement the described functions for each particular application, but such implementation should not be considered beyond the scope of the present application. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link.

[0127] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order of the steps, after understanding the spirit of the present application.

[0128] In the present application, the features described and / or illustrated for one embodiment can be used in the same way or in a similar way in one or more other embodiments, and / or in combination with or instead of features of other embodiments.

[0129] The above description is only preferred embodiments of the present application and is not intended to limit the present application. The embodiments of the present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for identifying risk source areas of unstable rock masses, characterized in that, The method for identifying the risk source area of ​​the unstable rock mass includes: Acquire images of unstable rock masses, generate point cloud data of unstable rock masses based on the images, and determine the attitude information, trace length data, and spacing of structural surfaces of unstable rock masses based on the point cloud data. Based on the point cloud data of the unstable rock mass, the unstable rock mass is divided into multiple sub-regions, and the controlling structural surface of each sub-region is determined based on the unstable rock mass structural surface in each sub-region. The shear strength of each of the control structural surfaces is determined based on the morphological parameters, wall strength, roughness coefficient, normal stress, and basic friction angle of each control structural surface. Obtain a temperature cloud map of the unstable rock mass; based on the temperature cloud map, determine the homogeneous rock mass region and the temperature rise curve of each homogeneous rock mass region within each sub-region; determine the initial temperature and heat flux density of the rock mass surface corresponding to each homogeneous rock mass region; calculate the heat absorption coefficient of the rock in each homogeneous rock mass region during the heating process based on the temperature rise curve, initial temperature, and heat flux density; determine the uniaxial compressive strength of the rock in the sub-region based on the average heat absorption coefficient of multiple homogeneous rock mass regions within the sub-region. Determine the dominant structural surface group within each sub-region, obtain the radius and normal density of each dominant structural surface group, determine the volume joint number of each homogeneous rock mass region based on the radius and normal density of each dominant structural surface group, and determine the integrity coefficient of each sub-region based on the average volume joint number of all homogeneous rock mass regions within each sub-region. The formula for calculating the integrity coefficient of the sub-region is as follows: ； in, This is a correction factor for the structural surface category. The integrity coefficient, The average volume joint number; Based on the maximum trace length, minimum spacing of structural planes, shear strength, uniaxial compressive strength, and integrity coefficient of each sub-region, the rockfall sensitivity index of each sub-region is determined, and the risk source area of ​​the rockfall mass is determined based on the rockfall sensitivity index of each sub-region.

2. The method for identifying risk source areas of unstable rock masses according to claim 1, characterized in that, The formula for calculating the shear strength is: ； ； ； in, For morphological parameters, For shear strength, For normal stress, For wall strength, The rock is of high density. The length of the structural section. The number of ascending segments in the cross-section. The number of rising segments for each undulating body. for The first cross section The parameters of the rising segment of an undulating body. This refers to the rebound value from the impact test.

3. The method for identifying risk source areas of unstable rock masses according to claim 1, characterized in that, The formula for calculating the uniaxial compressive strength is: ； in, Uniaxial compressive strength, , All are lithological influencing factors. The average heat absorption coefficient is denoted as .

4. The method for identifying risk source areas of unstable rock masses according to claim 1, characterized in that, Based on the maximum trace length, minimum spacing of structural planes, shear strength, uniaxial compressive strength, and integrity coefficient of each sub-region, the rock hazard sensitivity index of each sub-region is determined, including: Calculate the sum of the maximum trace length, minimum spacing between structural planes, shear strength, uniaxial compressive strength, and integrity factor within the sub-region; The calculated sum is used as the rock hazard sensitivity index for the corresponding sub-region.

5. The method for identifying risk source areas of unstable rock masses according to any one of claims 1 to 4, characterized in that, The expression for the first structural plane is: The expression for the second structural plane is: The formula for calculating the spacing between structural surfaces is: ; in, Let be the unit normal vector of the structural surface. and These represent constants.

6. A system for identifying risk source areas of unstable rock masses, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory, characterized in that the processor is used to execute the computer program, and when the computer program is executed, the system implements the steps of the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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