Method and device for identifying risk source area of dangerous rock mass

Abstract

The invention provides a dangerous rock mass risk source area identification method and device, and the method comprises the steps: obtaining a dangerous rock mass image, generating dangerous rock mass point cloud data, and determining the occurrence information, trace length data and structural plane spacing of a dangerous rock mass structural plane; dividing the dangerous rock body into a plurality of sub-regions, and determining a control type structural surface of each sub-region; determining the shear strength of each control type structural surface based on each morphology parameter, the wall surface strength, the roughness coefficient, the normal stress and the basic friction angle; determining the uniaxial compressive strength of the rock in each sub-region; determining the radius and normal density of the dominant structural plane group in each sub-region, and determining the integrity coefficient of each sub-region; and determining the dangerous rock sensitivity index of each sub-region based on the maximum trace length, the minimum spacing of the structural surface, the shear strength, the uniaxial compressive strength and the integrity coefficient in each sub-region, and determining the risk source region of the dangerous rock mass based on the dangerous rock sensitivity index of each sub-region. According to the invention, the accuracy of the dangerous rock body risk source area identification result is improved.

Description

Technical Field

[0001] The present invention 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 Art

[0002] The existence of dangerous rock masses has an important impact on human life. The occurrence of disasters such as rockfalls may cause casualties, infrastructure damage, and transportation facilities that cannot operate normally. At present, in order to identify the risk source area of ​​dangerous rock masses, it is generally necessary to measure the dangerous rock masses and then extract the structural information of the dangerous rock masses. Traditional measurement methods mainly include manual measurement, such as contact-type structural surface information collection technology such as geological compasses and tape measures. This traditional contact measurement technology is time-consuming, has low accuracy, is often inefficient, and has great safety hazards. In order to improve the efficiency of information collection, a series of intelligent measurement methods have emerged, such as photogrammetry technology and three-dimensional laser scanning technology. This type of technology can realize the collection of information on the structural surface of the study area through the collection of point cloud data and combined with intelligent algorithms. Specifically, in the Chinese patent with the publication number CN201711108494 and the invention name "A multi-angle identification method for the occurrence and properties of structural surfaces based on drones", a multi-angle identification method for the spatial distribution and properties of structural surfaces that are difficult to observe closely by drones is disclosed.

[0003] In the above-mentioned prior art, although intelligent measurement means 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 information such as the structural surface occurrence, trace length and spacing; however, the prior art does not reveal that the strength, deformation characteristics and failure mechanism of the dangerous rock mass will affect the instability of the dangerous rock mass, resulting in the method of judging the instability state of the dangerous rock mass based only on the geometric parameters of the structural surface of the dangerous rock mass in the prior art, which has the disadvantage of inaccurate judgment results, and thus cannot accurately realize the identification of the risk source area of ​​the dangerous rock mass. Therefore, how to improve the accuracy of the identification results of the risk source area of ​​the dangerous rock mass is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for identifying a dangerous rock mass risk source area, so as to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a method for identifying a dangerous rock mass risk source area, the method comprising: Acquire a dangerous rock mass image, generate dangerous rock mass point cloud data based on the dangerous rock mass image, and determine the occurrence information, trace length data, and structural surface spacing of the dangerous rock mass structure surface based on the dangerous rock mass point cloud data; Dividing the dangerous rock mass into a plurality of sub-regions based on the dangerous rock mass point cloud data, and determining the control type structural surface of each sub-region based on the dangerous rock mass structural surface in each sub-region; Determining the shear strength of each of the control-type structural surfaces based on the morphological parameters, wall strength, roughness coefficient, normal stress and basic friction angle of each of the control-type structural surfaces; Obtaining a temperature cloud map of the dangerous rock mass, and determining the uniaxial compressive strength of the rock in each of the sub-areas based on the temperature cloud map; Determine the dominant structural surface group in each of the sub-areas, obtain the radius and normal density of each of the dominant structural surface groups, and determine the integrity coefficient of each of the sub-areas based on the radius and normal density of each of the dominant structural surface groups; Based on the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength and integrity coefficient in each sub-area, the dangerous rock sensitivity index of each sub-area is determined, and based on the dangerous rock sensitivity index of each sub-area, the risk source area of ​​the dangerous rock mass is determined.

[0006] In some embodiments of the present invention, the calculation formula of the shear strength is: ; ; ; Where M is the morphology parameter, is the shear strength, is the normal stress, is the wall strength, γ is the rock density, L is the length of the structural surface section, is the number of ascending sections of the profile, is the number of rising sections of each undulating body, F ij is the undulation parameter of the rising section of the i-th undulation in the j-section, and N is the rebound value of the impact test.

[0007] In some embodiments of the present invention, determining the uniaxial compressive strength of the rock in each of the sub-areas based on the temperature cloud map includes: Determine the rock mass homogeneous area in each of the sub-areas and the temperature rise curve of each of the rock mass homogeneous areas based on the temperature cloud map, and determine the initial temperature corresponding to each of the rock mass homogeneous areas and the heat flux density on the rock mass surface; Calculate the heat absorption coefficient of the rock in each homogeneous zone of the rock mass during the heating process based on the heating curve, the initial temperature and the heat flux density; The uniaxial compressive strength of the rock in the sub-region is determined based on the average heat absorption coefficient of a plurality of homogeneous zones of the rock mass in the sub-region.

[0008] In some embodiments of the present invention, the calculation formula of the uniaxial compressive strength is: ; Among them, R C is the uniaxial compressive strength, A and B are the lithology influencing factors, is the average heat absorption coefficient.

[0009] In some embodiments of the present invention, determining the integrity coefficient of each sub-region based on the radius and normal density of each dominant structural surface group includes: Determine the number of volume joints in each homogeneous area of ​​the rock mass based on the radius and normal density of each dominant structural surface group; The integrity coefficient of each of the sub-regions is determined based on the average volumetric joint number of all rock mass homogeneous zones within each of the sub-regions.

[0010] In some embodiments of the present invention, the calculation formula of the integrity coefficient of the sub-region is: ; in, is the structural surface category correction factor, is the integrity coefficient, is the average volume joint number.

[0011] In some embodiments of the present invention, the dangerous rock sensitivity index of each sub-area is determined based on the maximum trace length, the minimum spacing of the structural surfaces, the shear strength, the uniaxial compressive strength and the integrity coefficient in each sub-area, including: Calculate the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength, and sum of integrity factors within the sub-area; The calculated sum is used as a dangerous rock sensitivity index for the corresponding sub-area.

[0012] In some embodiments of the present invention, the expression of the first structural plane is: , the expression of the second structural surface is: , the calculation formula of the structural surface spacing is ; in, is the unit normal vector of the structural surface, and are constants used to determine the positions of the first structural plane and the second structural plane in the space coordinates respectively.

[0013] According to another aspect of the present invention, a dangerous rock mass risk source area identification system is also disclosed, the system comprising a processor, a memory and a computer program stored in the memory, the processor is used to execute the computer program, and when the computer program is executed, the system implements the steps of the method described in any of the above embodiments.

[0014] According to yet another aspect of the present invention, a computer-readable storage medium is disclosed, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0015] The method and device for identifying the risk source area of ​​dangerous rock masses disclosed in the above-mentioned embodiments of the present invention first obtain the geometric characteristic parameters of the structural surface of the dangerous rock mass, and then determine the mechanical parameters of the dangerous rock mass, and the mechanical parameters include the shear strength of the control structural surface, the uniaxial compressive strength of the rock, and the integrity coefficient of the sub-region. Finally, the mechanical parameters and the geometric characteristic parameters are combined to identify the risk source area of ​​the dangerous rock mass. This method can not only accurately obtain the geometric characteristic parameters and mechanical parameters of the dangerous rock mass, but also improve the accuracy of the identification results of the risk source area of ​​the dangerous rock mass.

[0016] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0017] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. The components in the drawings are not drawn to scale, but are only for illustrating the principles of the present invention. In order to facilitate the illustration and description of some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, they may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings: Figure 1 The figure is a flow chart of a method for identifying a dangerous rock mass risk source area according to an embodiment of the present invention.

[0019] Figure 2 It is a schematic flow chart of a method for identifying dangerous rock mass risk source areas according to another embodiment of the present invention.

[0020] Figure 3 FIG. 1 is a schematic diagram of a UAV terrain-imitating flight according to an embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of the statistical results of the geometric characteristic parameters of the dangerous rock mass according to an embodiment of the present invention.

[0022] Figure 5Schematic diagram of the process of processing dangerous rock point cloud data using Geomagic software.

[0023] Figure 6 Schematic diagram of the corresponding relationship between normal stress and shear strength according to an embodiment of the present invention.

[0024] Figure 7 This is a result diagram of risk source area identification of dangerous rock masses according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0026] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0027] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0028] It should also be noted here that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection with an intermediary, not only to a wired connection, but also to a wireless connection, and the specific connection can be changed based on the actual application scenario.

[0029] The geometric characteristic parameters of the structural surface of the dangerous rock mass mainly include information such as the structural surface attitude, trace length and spacing, which are helpful to accurately describe the spatial structure and geometric morphology of the dangerous rock mass; while 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 found that comprehensively considering these two factors to judge the instability state of the dangerous rock mass can improve the accuracy of the identification results of the risk source area of ​​the dangerous rock mass; therefore, this application conducts a joint characterization study on the geometric parameters and mechanical parameters of the structural surface of the dangerous rock mass, and conducts stability analysis on the dangerous rock mass based on the two types of parameters.

[0030] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0031] Figure 1 FIG. 1 is a flow chart of a method for identifying a dangerous rock mass risk source area according to an embodiment of the present invention. Figure 1As shown, the method for identifying dangerous rock mass risk source areas at least includes steps S10 to S60.

[0032] Step S10: Acquire a dangerous rock mass image, generate dangerous rock mass point cloud data based on the dangerous rock mass image, and determine the occurrence information, trace length data, and structural surface spacing of the dangerous rock mass structural surface based on the dangerous rock mass point cloud data.

[0033] In this step, the geometric feature information of the structural surface of the dangerous rock body is extracted based on the point cloud data of the dangerous rock body image. Exemplarily, when acquiring the dangerous rock body image, a drone is used to simulate the terrain and obtain multiple close-up images of the target area at multiple positions and angles that meet the image overlap rate and ground resolution based on drone photogrammetry technology. When designing the flight route of the drone, the digital surface model (DSM) of the target area can be used to generate contour lines, and then the contour lines can be simplified, offset, and other processing to complete the design of the drone route. The flight route of the drone is as follows: Figure 3 shown.

[0034] For example, dense point cloud data of dangerous rock bodies can be generated based on the Structure from Motion (SfM) technology. In addition, before generating the point cloud data of dangerous rock bodies, multiple close-range images obtained based on the drone photogrammetry technology can be preprocessed, such as correcting distortion, aligning images, etc., and then the multiple close-range images after preprocessing are input into the Agisoft Metashape software, so as to generate dense point cloud data based on the Structure from Motion technology, thereby obtaining the refined terrain data of the target area under study.

[0035] Furthermore, the geometric characteristic parameters of the structural surface are extracted based on the obtained dangerous rock mass point cloud data. The geometric characteristic parameters include occurrence information, trace length data and structural surface spacing. The statistical results of the geometric characteristic parameters of the dangerous rock mass structural surface are as follows: Figure 4 Specifically, the unsupervised clustering technology and RANSAC model fitting can be combined to cluster the generated dangerous rock mass point cloud data, perform plane segmentation processing, and fit the point cloud data of each structural surface through the RANSAC model; the geometric parameters of the dangerous rock mass structural surface can be characterized based on geometric analysis, NTV and other methods.

[0036] For example, when extracting the occurrence information of the structural surface of a dangerous rock mass, the dangerous rock mass point cloud data can be preprocessed by downsampling based on the voxel grid filter; then the dangerous rock mass point cloud data is spatially divided by the kd tree, and k nearest neighbor points are selected to form a covariance matrix; for example, the sample point The 3×3 covariance matrix 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 neighbor points. Further, the covariance matrix is ​​subjected to singular value decomposition (SVD) to estimate the normal vector of each point; in this step, the covariance matrix, eigenvector And its corresponding eigenvalues ​​are subjected to singular value decomposition (SVD) to obtain , corresponding to the minimum eigenvalue The eigenvector of is the unit normal vector of the sample point. Further, based on the main clustering algorithm, the normal vectors of each point are clustered according to the similarity of the normal vector directions; illustratively, the normal vectors obtained in the above steps are used as input information of the mean-shift clustering algorithm to find vectors with similar directions based on the mean-shift clustering algorithm.

[0037] Furthermore, a single plane can be separated in the cluster set based on an auxiliary clustering algorithm of 3D XYZ coordinates; illustratively, 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, and if there is a minimum number of points within this radius, 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, the core point and all points within the radial distance are assigned to a cluster, and each core point that is not in the cluster forms a new cluster. The RANSAC algorithm is used to fit a plane to each cluster set in the previous step and calculate the discontinuity direction; specifically, first three non-collinear points are randomly selected for the plane in the point cloud data, and these non-collinear points are used to define the plane equation parameters. Next, neighboring points are iteratively checked to determine whether they fall within the defined threshold distance. Points within this threshold are defined as a set of inliers; otherwise, they are considered outliers; the algorithm fits multiple planes and only accepts the plane with the most points in the consensus set, so 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 generated by the RANSAC algorithm is in the form of (mx +ny+lz+d= 0), where m, n, and l are the unit normal vector components of the best fitting plane; the inclination and inclination direction of the plane are further calculated using the following equation: ; .

[0038] When extracting the trace length data of the dangerous rock mass structural surface, the following steps may be included: (1) Using the mesh vertices around the trace to identify the trace feature points; Define a triangular mesh as M=(P, L, V), is the set of vertices, L is the set of edges, is a collection of faces. Each vertex Expressed in Cartesian coordinates, it is The NTV method is used to classify mesh vertices. The NTV of each vertex p is defined as: ; in, yes The unit normal vector of For point The weight coefficient of the single ring adjacent face index set is defined as: ; in For triangle The area of for The maximum area, For triangle The center of mass, is the side length of the cube that defines the space adjacent to each vertex.

[0039] Two thresholds a and b are defined to control the recognition accuracy of corner and edge type points, respectively; 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 additional noise points; the definition of a and b depends on the visual assessment of the number of recognized edge type and corner type vertices.

[0040] (2) Compress the trace feature points on their point cloud skeleton; The curve skeleton of the point cloud is extracted by a Laplace-based shrinkage algorithm; the shrinkage algorithm aggregates the feature points on the skeleton using local Delaunay triangulation and topological refinement to obtain the position of the three-dimensional trace.

[0041] (3) Connection of trace feature points belonging to the same trace; The above process generates contracted feature points that are clustered on their point skeletons; this step is to connect these points to generate tracking segments. The connected neighbors of the contracted feature points are defined as the Delaunay neighbors within the connection radius. Since contraction can expand the interval of feature points belonging to the same trace segment, through data testing, the connection radius is defined to be larger than the contraction radius, which is 3 times the average side length of the triangle mesh.

[0042] (4) linearizing the trace segments divided into linearization segments; Since the feature point connection algorithm in the above process may incorrectly connect the feature points of different traces, this step linearly segments the trace segments to generate linearized segments consisting only of the feature points of the same trace. , principal component analysis starts with calculating the covariance matrix: ; Where c is the number of points, is the centroid coordinate of the point cloud. Because the matrix is symmetric and positive, and it can be decomposed by eigenvalue as: ; Its characteristic value is Then the parameter q is used to measure the degree of linearization, defined as ; (5) Trace segments belonging to the same trace are connected.

[0043] The trace segment connection rules are defined as follows: ①Axial distance is less than the threshold D for controlling axial expansion, and when hour Less than (defined by data testing as 3 times the average side length of the triangle mesh); ② Radial distance Less than the threshold for controlling radial expansion ; ③Angle Less than the threshold that controls the curvature range of the trace ; ④ Assume that the set S consists of trace segments that satisfy the above three conditions, then the line segments to be connected are the radial distances in S The smallest line segment.

[0044] Exemplarily, the expression of the first structural plane is: , the expression of the second structural surface is: , the calculation formula of the structural surface spacing is: ;in, is the unit normal vector of the structural surface, and are constants used to determine the positions of the first structural plane and the second structural plane in the spatial coordinates, respectively; x, y, z represent the three directions of the spatial rectangular coordinate system.

[0045] Step S20: dividing the dangerous rock mass into a plurality of sub-regions based on the dangerous rock mass point cloud data, and determining the control type structural surface of each sub-region based on the dangerous rock mass structural surface in each sub-region.

[0046] In this step, in order to improve the calculation efficiency, the slope model area is divided according to the differences in local topography and rock mass structure development characteristics, such as Figure 7 As shown, the dangerous rock mass can be divided into 9 sub-areas. Further, the control type structural surface of each sub-area is determined from all the control surfaces of each sub-area. The control type structural surface is the structural surface that is most likely to cause damage and can be obtained through manual observation.

[0047] Step S30: Determine the shear strength of each of the control-type structural surfaces based on the morphological parameters, wall strength, roughness coefficient, normal stress and basic friction angle of each of the control-type structural surfaces.

[0048] In this step, the shear strength of the control surface is further determined by the structural data of the control surface. The structural data of the controlled structural surface include morphological parameters, wall strength, roughness coefficient, normal stress and basic friction angle.

[0049] Before obtaining the structural data of the control-type structural surface, the point cloud data of the control-type structural surface in each sub-area can be obtained from the point cloud data of the dangerous rock mass obtained based on the UAV photogrammetry technology, and then the point cloud data of the control-type structural surface can be imported into the Geomagic software. A square area with a side length of 100 mm is intercepted on the point cloud based on the Geomagic software as the standard interface, and the standard interface is further divided into 101 horizontal and vertical section lines (with an interval of 1 mm). The point cloud data of the interface is orthogonally interpolated through MATLAB programming to provide data support for the subsequent quantitative description of the morphology of the control-type structural surface. The specific processing flow is as follows: Figure 5 shown.

[0050] When calculating the shear strength of the controlled structural surface, for example, the anisotropic morphology parameter M that can reflect the micro-slope distribution characteristics and shear mechanical effect of the controlled structural surface is first introduced to accurately describe the morphology of the controlled structural surface. The calculation formula of the anisotropic morphology parameter M is: , ; Where M is the morphological parameter, L is the length of the structural surface section, is the number of ascending sections of the profile, is the number of rising sections of each undulating body, F is the undulation parameter, and F ij is the undulation parameter of the rising section of the i-th undulation in the j-section, θ is the climbing slope angle, and h is the climbing height.

[0051] Furthermore, based on the relationship between rock density γ and rebound value N, the wall strength JCS of the in-situ rock structure surface can be quickly estimated; in addition, based on the empirical relationship, the basic friction angle of the measured structure surface can also be quickly obtained , , , where N is the impact test rebound value, JCS is the wall strength of the rock structure surface, and γ is the rock density; in addition, when determining the impact test rebound value N, a Schmidt rebound hammer (L type, impact energy of 0.785 kN / m) can be used to apply force perpendicular to the relatively complete structural surface, and multiple impact tests can be performed at one point to obtain the rebound value N.

[0052] In addition, according to the relative relationship between the morphological parameter M and the structural surface roughness coefficient JRC, combined with the widely used structural surface Barton-Bandis shear strength criterion, a new empirical formula for the shear strength of rock mass structural surface (M-JCS model) is improved: ; ; ; in, is the normal stress on the structural surface, is the shear strength of the structural surface.

[0053] In the above embodiment, the shear strength can be obtained based on the following formula: ; ; ; Where M is the morphology parameter, is the shear strength, is the normal stress, is the wall strength, γ is the rock density, L is the length of the structural surface section, is the number of ascending sections of the profile, is the number of rising sections of each undulating body, F ij is the undulation parameter of the rising section of the i-th undulation in the j-section, and N is the rebound value of the impact test.

[0054] In addition, in order to further improve the accuracy of the obtained shear strength, the linear Mohr-Coulomb criterion can be used to fit the nonlinear M-JCS model based on the equivalent linear fitting principle to quickly calculate the shear strength of the structural surface under different normal stresses. The shear strength of each group of structural surfaces under different normal stresses can be processed by linear regression (such as Figure 6 As shown), the final cohesion c and friction angle φ of the structural surface can be obtained.

[0055] Step S40: obtaining a temperature cloud map of the dangerous rock mass, and determining the uniaxial compressive strength of the rock in each of the sub-areas based on the temperature cloud map.

[0056] In this step, the uniaxial compressive strength of the rock in each sub-area is determined using the temperature cloud map. In order to obtain the temperature cloud map of the dangerous rock mass, infrared thermal imaging technology can be used to detect the target object photographed by the drone.

[0057] In one embodiment, the uniaxial compressive strength of the rock in each of the sub-regions is determined based on the temperature cloud map, which may specifically include the following steps: determining the rock homogeneous zone in each of the sub-regions and the heating curve of each of the rock homogeneous zones based on the temperature cloud map, and determining the initial temperature corresponding to each of the rock homogeneous zones and the heat flux density on the rock surface; calculating the heat absorption coefficient of the rock in each of the rock homogeneous zones during the heating process based on the heating curve, initial temperature and heat flux density; determining the uniaxial compressive strength of the rock in the sub-region based on the average heat absorption coefficient of the multiple rock homogeneous zones in the sub-region. Exemplarily, when obtaining the uniaxial compressive strength of the rock, the temperature cloud map may be used to extract a specified number of rock homogeneous zones and the heating process curve of each rock homogeneous zone from each sub-region. When determining the rock homogeneous zone, the zone with better integrity and more uniform lithology may be selected as the rock homogeneous zone; further, the corresponding initial temperature may be determined. ; At the same time, the heat flux density q on the rock surface corresponding to the same number of rock homogeneous areas is obtained. Further, based on the heat flux density, initial temperature and thermal excitation time, the average heat absorption coefficient of the rock during the heating process is obtained. The heat absorption coefficient is defined as follows: , where i is the rock mass homogeneous zone number, λ is the rock mass thermal conductivity corresponding to the rock mass homogeneous zone, is the rock mass density corresponding to the rock mass homogeneous area, is the specific heat capacity of the rock mass corresponding to the rock mass homogeneous zone numbered i, is the first integral of the Gaussian error complement function, Refers to the temperature of the rock surface at time t. Furthermore, the average heat absorption coefficient of the rock is used to determine the uniaxial compressive strength of the rock R C , specifically defined as: ; Among them, R C is the uniaxial compressive strength, A and B are the lithology influencing factors, is the average heat absorption coefficient, for example, the lithology influence factor of sandstone is A=56, B=1.51.

[0058] Step S50: determining the dominant structural surface group in each of the sub-regions, obtaining the radius and normal density of each of the dominant structural surface groups, and determining the integrity coefficient of each of the sub-regions based on the radius and normal density of the structural surface in each of the dominant structural surface groups.

[0059] In order to further determine the dominant structural surface groups in each sub-region, the geometric feature information of the structural surfaces in each sub-region can be statistically analyzed, and the statistically obtained geometric feature information of the structural surfaces can be plotted on the isodensity map through Dips software. According to the distribution density of the poles of the structural surface occurrence on the isodensity map, the structural surfaces can be divided into different dominant groups in combination with the clustering method, thereby obtaining the dominant structural surface groups in each sub-region.

[0060] Exemplarily, when calculating the integrity coefficient of a sub-region, the specific steps may include: determining the number of volume joints in each homogeneous rock zone based on the radius and normal density of each dominant structural surface group; determining the integrity coefficient of each sub-region based on the average number of volume joints in all homogeneous rock zones in each sub-region.

[0061] Specifically, in the three-dimensional point cloud model of the dangerous rock mass, a preset number of homogeneous areas of the dangerous rock mass are obtained, and the average value of the volume joint number of these areas is calculated. First, based on the trace length of each structural surface in the dominant structural surface group, the radius g corresponding to the dominant structural surface group is determined; it is defined as follows: ; ;in, is the average trace length of the structural surface in the dominant structural surface group, is the number of structural surfaces in the dominant structural surface group. In this embodiment, the multiple structural surfaces in each dominant structural surface group are a group of structural surfaces with similar occurrences, so each sub-region includes at least one dominant structural surface group.

[0062] Furthermore, based on the distance between two adjacent dominant structural surfaces in the dominant structural surface group, the normal density of the dominant structural surface group is determined. ; The definitions are as follows: , ;in, is the average distance between two adjacent dominant structural surfaces in the dominant structural surface group, is the distance between two adjacent structural surfaces, is the number of structural surfaces in the dominant structural surface group. Further, based on the radius and normal density corresponding to the dominant structural surface group, the volume joint number of the homogeneous zone of the dangerous rock mass is determined. ;in, is the preset number of rock mass homogeneous zones, i is the rock mass homogeneous zone number, represents the number of volume joints corresponding to the homogeneous zone of rock mass numbered i. Finally, the integrity coefficient of the sub-region is defined as: ;in, is the structural surface category correction factor, is the integrity coefficient, is the average volume joint number.

[0063] In one embodiment, the structural surface type correction coefficient The values ​​are shown in the following table: Step S60: Determine the dangerous rock sensitivity index of each sub-area based on the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength and integrity coefficient in each sub-area, and determine the risk source area of ​​the dangerous rock mass based on the dangerous rock sensitivity index of each sub-area.

[0064] In this step, the risk source area of ​​the dangerous rock mass is identified based on the combination of geometric characteristic parameters and mechanical parameters. In some embodiments, the maximum trace length can be used to quantify the extension of the main structural surface, indicating the degree of lack of support of the dangerous rock mass. At the same time, the minimum spacing between the same group of dominant structural surfaces is used as an evaluation index of the rock mass quality, and combined with the uniaxial compressive strength Rc of the rock, the integrity coefficient of the rock mass , the shear strength of the potential sliding surface , complete the determination of the mechanical properties of the rock mass.

[0065] In one embodiment, the sum of the maximum trace length, the minimum spacing between structural surfaces, the shear strength, the uniaxial compressive strength and the integrity coefficient in the sub-region can be calculated; and the calculated sum is used as the dangerous rock sensitivity index of the corresponding sub-region. , represents the maximum trace length, S represents the minimum spacing between structural surfaces, and R C is the uniaxial compressive strength, represents the shear strength, Indicates the integrity factor.

[0066] After calculating the dangerous rock sensitivity index of each sub-region, 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, that is, the risk gradually increases from 0 to 8.

[0067] In addition, in order to facilitate the observation of the risk source area of ​​the dangerous rock mass, a large number of geometric characteristic parameters and mechanical parameters of the rock mass structure can be further input into the three-dimensional real-life model to realize the identification of the risk source area of ​​the dangerous rock mass, and finally obtain the spatial distribution map of the risk source area of ​​the dangerous rock mass. Exemplarily, the three-dimensional real-life model of the dangerous rock mass can be constructed by the following method: first, in the HyperMesh software, according to the geometric characteristics such as the position, scale and occurrence of the mountain structure in the study area, the corresponding control points are connected to obtain the boundary line, thereby establishing a series of large-scale mountain structures; then, based on the above-mentioned statistical results of the occurrence, trace length, spacing, geometric parameters of the rock mass structure surface and the control of the corresponding random variables, a multi-scale three-dimensional real-life model of the rock mass structure is constructed in the study area using 3DEC software and combined with Monte Carlo technology.

[0068] Figure 2 FIG. 1 is a flow chart of a method for identifying a dangerous rock mass risk source area according to another embodiment of the present invention. Figure 2 As shown, the method includes the following steps: photographing and detecting dangerous rock masses based on UAV photogrammetry technology and infrared imaging technology; obtaining dense three-dimensional point cloud data of dangerous rock masses based on motion recovery structure algorithm; clustering analysis of dangerous rock mass structural surfaces and extracting geometric characteristic parameters of structural surfaces in combination with intelligent algorithms; statistics of structural surface geometric parameters to obtain a probability distribution model of structural surface geometric parameters; obtaining three mechanical parameters of dangerous rock masses, namely shear strength, compressive strength and integrity coefficient; constructing a three-dimensional real-life model of multi-scale rock mass structure to identify risk source areas of dangerous rock masses.

[0069] Through the above embodiments, it can be found that the method for identifying the risk source area of ​​dangerous rock mass disclosed in the present invention first obtains the geometric characteristic parameters of the structural surface of the dangerous rock mass, and then determines the mechanical parameters of the dangerous rock mass, and the mechanical parameters include the shear strength of the control type structural surface, the uniaxial compressive strength of the rock, and the integrity coefficient of the sub-region, and finally identifies the risk source area of ​​the dangerous rock mass based on the combination of mechanical parameters and geometric characteristic parameters. This method can not only effectively and accurately obtain the geometric characteristic parameters and mechanical parameters of the dangerous rock mass, but also improve the accuracy of the identification results of the risk source area of ​​the dangerous rock mass.

[0070] According to another aspect of the present invention, a dangerous rock mass risk source area identification system is also disclosed, the system comprising a processor, a memory and a computer program stored in the memory, the processor is used to execute the computer program, and when the computer program is executed, the system implements the steps of the method described in any of the above embodiments.

[0071] The embodiments of the present invention also provide a computer-readable storage medium and a computer program product, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented. 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 storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0072] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0073] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is 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 between the steps after understanding the spirit of the present invention.

[0074] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying dangerous rock mass risk source areas, characterized in that: The method for identifying the dangerous rock mass risk source area comprises: Acquire a dangerous rock mass image, generate dangerous rock mass point cloud data based on the dangerous rock mass image, and determine the occurrence information, trace length data, and structural surface spacing of the dangerous rock mass structure surface based on the dangerous rock mass point cloud data; Dividing the dangerous rock mass into a plurality of sub-regions based on the dangerous rock mass point cloud data, and determining the control type structural surface of each sub-region based on the dangerous rock mass structural surface in each sub-region; Determining the shear strength of each of the control-type structural surfaces based on the morphological parameters, wall strength, roughness coefficient, normal stress and basic friction angle of each of the control-type structural surfaces; Obtaining a temperature cloud map of the dangerous rock mass, and determining the uniaxial compressive strength of the rock in each of the sub-areas based on the temperature cloud map; Determine the dominant structural surface group in each of the sub-areas, obtain the radius and normal density of each of the dominant structural surface groups, and determine the integrity coefficient of each of the sub-areas based on the radius and normal density of each of the dominant structural surface groups; Based on the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength and integrity coefficient in each sub-area, the dangerous rock sensitivity index of each sub-area is determined, and based on the dangerous rock sensitivity index of each sub-area, the risk source area of ​​the dangerous rock mass is determined.

2. The method for identifying dangerous rock mass risk source areas according to claim 1, characterized in that: The calculation formula of the shear strength is: ； ； ； Where M is the morphology parameter, is the shear strength, is the normal stress, is the wall strength, γ is the rock density, L is the length of the structural surface section, is the number of ascending sections of the profile, is the number of rising sections of each undulating body, F ij is the undulation parameter of the rising section of the i-th undulation in the j-section, and N is the rebound value of the impact test.

3. The method for identifying dangerous rock mass risk source areas according to claim 1, characterized in that: Determining the uniaxial compressive strength of the rock in each of the sub-areas based on the temperature cloud map includes: Determine the rock mass homogeneous area in each of the sub-areas and the temperature rise curve of each of the rock mass homogeneous areas based on the temperature cloud map, and determine the initial temperature corresponding to each of the rock mass homogeneous areas and the heat flux density on the rock mass surface; Calculate the heat absorption coefficient of the rock in each homogeneous zone of the rock mass during the heating process based on the heating curve, the initial temperature and the heat flux density; The uniaxial compressive strength of the rock in the sub-region is determined based on the average heat absorption coefficient of a plurality of homogeneous zones of the rock mass in the sub-region.

4. The method for identifying dangerous rock mass risk source areas according to claim 3, characterized in that: The calculation formula of the uniaxial compressive strength is: ； Among them, R C is the uniaxial compressive strength, A and B are the lithology influencing factors, is the average heat absorption coefficient.

5. The method for identifying dangerous rock mass risk source areas according to claim 3, characterized in that: Determining the integrity coefficient of each of the sub-regions based on the radius and the normal density of each of the dominant structural surface groups includes: Determine the number of volume joints in each homogeneous area of ​​the rock mass based on the radius and normal density of each dominant structural surface group; The integrity coefficient of each of the sub-regions is determined based on the average volumetric joint number of all rock mass homogeneous zones within each of the sub-regions.

6. The method for identifying dangerous rock mass risk source areas according to claim 5, characterized in that: The calculation formula of the integrity coefficient of the sub-region is: ； in, is the structural surface category correction factor, is the integrity coefficient, is the average volume joint number.

7. The method for identifying dangerous rock mass risk source areas according to claim 1, characterized in that: Based on the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength and integrity coefficient in each of the sub-areas, the dangerous rock sensitivity index of each of the sub-areas is determined, including: Calculate the maximum trace length, minimum spacing between structural surfaces, shear strength, uniaxial compressive strength, and sum of integrity factors within the sub-area; The calculated sum is used as a dangerous rock sensitivity index for the corresponding sub-area.

8. The method for identifying dangerous rock mass risk source areas according to any one of claims 1 to 7, characterized in that: The expression of the first structural surface is: , the expression of the second structural surface is: , the calculation formula of the structural surface spacing is ; in, is the unit normal vector of the structural surface, and Respectively represent constants.

9. A dangerous rock mass risk source area identification system, characterized in that: The system comprises a processor, a memory and a computer program stored in the memory, wherein the processor is used to execute the computer program, and when the computer program is executed, the system implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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