Rock slope key block identification method and device based on three-dimensional point cloud

Abstract

The application provides a rock slope key block identification method and device based on a three-dimensional point cloud, relates to the technical field of rock mass engineering data processing, and comprises the following steps: obtaining first information; processing the slope point cloud through a density-based clustering algorithm and a random sample consensus algorithm; searching the point cloud data corresponding to the structural surface by using a candidate block search algorithm; determining the block formed by the intersecting structural surface set through the finiteness theorem and the mobility theorem in the deterministic block theory; calculating the block safety factor of the finite movable block, and preliminarily confirming the key block according to the block safety factor; and performing probability analysis on the formation of the preliminarily confirmed key block based on the semi-deterministic block theory considering the structural surface size, so as to finally identify the rock slope key block. The application not only solves the labor quantity and danger of traditional manual operation, but also solves the problem of long running time in the process of establishing a grid.

Description

Technical Field

[0001] This invention relates to the field of rock mass engineering data processing technology, and more specifically, to a method and apparatus for identifying key blocks of rock slopes based on three-dimensional point clouds. Background Technology

[0002] The presence and intersecting relationships of structural planes in rock slopes create in-situ blocks of varying shapes and sizes within the rock mass. Among these, the less stable blocks are termed critical blocks. Critical blocks are a major source of rockfalls and, under the influence of gravity and triggering factors (earthquakes, torrential rain, and strong winds), are easily separated from the parent rock, posing a serious threat to transportation corridors and human safety along the slope. Therefore, accurately and efficiently identifying critical blocks on rock slopes is a crucial issue. However, current critical block identification methods typically require converting 3D point clouds into meshes before subsequent identification, a process that often takes a considerable amount of time. Furthermore, current critical block identification methods rely on numerous assumptions, such as assuming that structural planes extend completely through the rock mass, while the actual size of structural planes within the rock mass is limited. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for identifying key blocks in rock slopes based on three-dimensional point clouds, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for identifying key blocks in rock slopes based on three-dimensional point clouds, including:

[0005] Obtain first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud;

[0006] The slope point cloud is processed by density-based clustering algorithm and random sampling consensus algorithm to obtain the structural plane equation, the corresponding point cloud data of the structural plane, and the structural plane trace length. The structural plane is a preset infinitely extended structural plane.

[0007] A candidate block search algorithm is used to search the point cloud data corresponding to the structural surfaces to obtain a set of intersecting structural surfaces of the blocks;

[0008] The block formed by the set of intersecting structural surfaces is determined by the finiteness theorem and the mobility theorem in the deterministic block theory, thus identifying the finite movable block;

[0009] The safety factor of the finite movable block is calculated based on the failure mode analysis method in block theory, the normal vector, the sliding force analysis method, and the physical parameters. The critical blocks are preliminarily identified based on the safety factor.

[0010] Based on the length of the structural surface trace, a probabilistic analysis is performed on the formation of the initially identified key blocks using the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key blocks of the rock slope, and the block volume of the key blocks of the rock slope is calculated based on the plane equation of the structural surface.

[0011] Secondly, this application also provides a key block identification device for rock slopes based on three-dimensional point clouds, comprising:

[0012] The acquisition module is used to acquire first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud.

[0013] The processing module is used to process the slope point cloud using a density-based clustering algorithm and a random sampling consensus algorithm to obtain the structural plane equation of the structural surface, the corresponding point cloud data of the structural surface, and the trace length of the structural surface. The structural surface is a preset infinitely extending structural plane.

[0014] The search module is used to search the point cloud data corresponding to the structural surface using a candidate block search algorithm to obtain a set of intersecting structural surfaces of the block.

[0015] The judgment module is used to judge the block formed by the set of intersecting structural surfaces using the finiteness theorem and mobility theorem in the deterministic block theory, and to determine the finite movable block.

[0016] The calculation module is used to calculate the block safety factor of the finite movable block based on the failure mode analysis method in block theory, the normal vector, the sliding force analysis method and the physical parameters, and to preliminarily identify the key blocks based on the block safety factor;

[0017] The identification module is used to perform a probability analysis on the formation of the initially confirmed key blocks based on the length of the structural surface trace and the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key blocks of the rock slope, and to calculate the block volume of the key blocks of the rock slope based on the plane equation of the structural surface.

[0018] Thirdly, this application also provides a key block identification device for rock slopes based on three-dimensional point clouds, including:

[0019] Memory, used to store computer programs;

[0020] A processor is used to implement the steps of the method for identifying key blocks of rock slopes based on three-dimensional point clouds when executing the computer program.

[0021] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for identifying key blocks of rock slopes based on three-dimensional point clouds.

[0022] The beneficial effects of this invention are as follows: By realizing a complete technical process from three-dimensional point cloud data of rock slopes to key block identification and stability analysis, this invention not only solves the labor and danger of traditional manual operations, but also solves the problem of long running time in the grid establishment process.

[0023] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the process for identifying key blocks of rock slopes based on three-dimensional point clouds as described in this embodiment of the invention.

[0026] Figure 2 This is a block classification diagram in the block theory described in the embodiments of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of the rock slope key block identification device based on three-dimensional point cloud as described in an embodiment of the present invention.

[0028] The markings in the diagram are: 800, Key block identification device for rock slopes based on 3D point cloud; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] Example 1:

[0032] This embodiment provides a method for identifying key blocks of rock slopes based on three-dimensional point clouds.

[0033] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400, S500 and S600.

[0034] Step S100: Obtain first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud.

[0035] Step S100 specifically includes:

[0036] Obtain the physical and mechanical parameters of the target rock slope, including the unit weight of the rock mass, the friction angle of the structural surface, and the cohesion of the structural surface;

[0037] Aerial photography of the target rock slope area was carried out using UAVs. Flight routes were designed according to the actual conditions of the study area. Under the premise of ensuring overlap and resolution, images of the slope at different angles containing position and attitude information were obtained to obtain image information.

[0038] The feature points of the image information are extracted using the structure-reconstruction-motion algorithm, and pairwise matching is performed between the images. The multiple images are then connected based on the common feature points obtained from the matching to form a trajectory, and the slope point cloud is reconstructed.

[0039] Step S200: The slope point cloud is processed by density-based clustering algorithm and random sampling consensus algorithm to obtain the structural plane equation of the structural surface, the corresponding point cloud data of the structural surface, and the trace length of the structural surface. The structural surface is a preset infinitely extended structural plane.

[0040] Step S200 specifically includes:

[0041] The nearest neighbor search algorithm is used to search for the nearest neighbor set of each point in the slope point cloud, and then the normal vector of each point is calculated based on the principal component analysis algorithm.

[0042] The specific calculation process is as follows:

[0043] Calculate the covariance matrix of point pi:

[0044]

[0045] In the above formula, ∑ i Let be the covariance matrix of the neighborhood. To fit the centroid vector of the local plane, where k is the number of neighboring points, then for point p... i The covariance matrix is ​​solved to calculate its eigenvalues ​​and corresponding eigenvectors, as shown in the following equation:

[0046]

[0047] In the above formula, λ q For v q The corresponding eigenvalue, v q Let v0 represent the eigenvector, and assuming λ0 < λ1 < λ2, then v0 is the normal vector n of the plane nearest to the measuring point k. pi The normal vector at point pi can be approximated by the normal vector of the local plane, thus obtaining the normal vector of each point.

[0048] The slope point cloud is segmented using a density-based clustering algorithm to obtain individual structural surfaces;

[0049] In this step, the density-based spatial clustering algorithm first needs to specify the neighborhood radius r and the density threshold T. d Two hyperparameters are used to divide regions with sufficient density into single structural planes. The specific steps are as follows: If the number of points within the r range of a certain point exceeds T... d The point is considered the core point. Points within the range of r belong to the same cluster as the core point. Multiple consecutive clusters within the same region are considered a single structural surface. Points that cannot be connected within the range of any core point are considered noise points and do not belong to any cluster.

[0050] Based on the random sampling consensus algorithm, each of the structural surfaces is fitted to a plane to obtain the structural surface plane equation and the corresponding point cloud data of the structural surface.

[0051] In this step, the random sampling consensus algorithm is an iterative mathematical algorithm that can fit a set of 2D or 3D spatial data containing outliers (or noise) into a specific geometric model, such as a plane.

[0052] Calculate the maximum Euclidean distance between any two feature points within the structure plane to obtain the structure plane trace length.

[0053] The calculation formula is as follows:

[0054] T l =max(ed(p) i ,p j ))=max(||p i -p j ||2)

[0055] In the formula T l For the length of the structural surface trace, ed(p) i ,p j ) represents feature point p i With p j The Euclidean distance between them.

[0056] Step S300: Use a candidate block search algorithm to search the point cloud data corresponding to the structure surface to obtain a set of intersecting structure surfaces of the block;

[0057] Step S300 specifically includes:

[0058] If the distance between two feature points from two structural surfaces is less than a preset value, the two structural surfaces are determined to intersect, resulting in a set storing a pair of intersecting structural surfaces. The preset value is the maximum Euclidean distance between the two intersecting structural surfaces. This preset value is introduced to specify the maximum distance between the two intersecting structural surfaces. If the distance between two points from two structural surfaces is less than the preset value, the two structural surfaces are considered to intersect, thus obtaining a set storing a pair of intersecting structural surfaces. Typically, the preset value is chosen to be slightly larger than the original resolution, i.e., the point spacing.

[0059] A loop search is performed on the set storing a pair of intersecting structural surfaces. If there are three structural surfaces that intersect each other, a set storing three intersecting structural surfaces is obtained. Considering that at least three intersecting structural surfaces are required to identify block vertices, a loop search is performed on the set storing a pair of intersecting structural surfaces. If there are three structural surfaces that intersect each other, they are added to a new set. Finally, a set storing three intersecting structural surfaces is obtained.

[0060] A cyclic search is performed on the set storing the three intersecting structural surfaces. If there are two or more identical structural surfaces in two structural surface combinations, then the two structural surface combinations are merged into a new structural surface combination to obtain a new set of intersecting structural surfaces.

[0061] Repeat the loop search operation on the new set of intersecting structural surfaces until no two combinations of structural surfaces contain two or more identical structural surfaces, and finally obtain the set of all intersecting structural surfaces that can form blocks.

[0062] Step S400: The block formed by the set of intersecting structural surfaces is judged by the finiteness theorem and the mobility theorem in the deterministic block theory to determine the finite movable block;

[0063] In block theory, each structural plane or free face can divide a complete space into an upper and lower half-space. When multiple structural planes and free faces exist, a spatial block is composed of the intersection of multiple half-spaces. Translating the planes constituting the spatial block through the origin forms a pyramid. Table 1 shows the pyramid classification in block theory, and a schematic diagram of the block classification is shown below. Figure 2 .

[0064]

[0065] Step S400 specifically includes:

[0066] The blocks are formed by the set of intersecting structural surfaces of the blocks;

[0067] Based on the finiteness theorem, the block is subjected to finiteness determination to obtain a finite block that satisfies the finiteness theorem, which is as follows:

[0068]

[0069] Based on the mobility theorem, the mobility of the finite block satisfying the finiteness theorem is determined, resulting in a finite movable block that simultaneously satisfies both the finiteness theorem and the mobility theorem. The mobility theorem is as follows:

[0070]

[0071] Step S500: Calculate the block safety factor of the finite movable block based on the failure mode analysis method in block theory, the normal vector, the sliding force analysis method, and the physical parameters; and preliminarily identify the key blocks based on the block safety factor.

[0072] Step S500 specifically includes:

[0073] The failure mode of the finite movable block is determined based on the failure mode analysis method and the normal vector.

[0074] The safety factor of the movable block of the finite movable block is calculated based on the failure mode and sliding force analysis method of the finite movable block.

[0075] Classical block theory considers three failure modes: fall, single-sided (planar) sliding, and double-sided (wedge-shaped) sliding. The failure mode of a block is determined through failure mode analysis. If a block does not meet a certain failure mode, it is a stable block; otherwise, it is a potentially unstable block or a critical block. The critical block needs to be further determined through sliding force analysis.

[0076] In this step, the fall failure mode refers to the failure of the movable block without any interaction with the structural surfaces. In this case, the direction of the block's motion is the direction of the resultant vector of the active forces, requiring only one kinematic condition: the resultant vector of the active forces causes the block to separate from all structural surfaces. These can be expressed as follows:

[0077] r·v l >0

[0078] In the formula, r is the resultant force vector of the principal force, which can be expressed as (0,0,-G) when only gravity is considered, and v l Let l be the unit normal vector pointing from the structural surface to the interior of the block.

[0079] The single-sliding-surface failure mode refers to a movable block sliding only along structural surface i'. In this mode, the block's motion direction is the orthographic projection of the resultant vector of the primary forces onto structural surface i'. It requires satisfying the following two kinematic conditions: the resultant vector of the primary forces keeps the block in contact with structural surface i'; and the resultant vector of the primary forces separates the block from other structural surfaces. These can be expressed as follows:

[0080]

[0081] In the formula v i' Let s be the unit normal vector pointing from structural plane i' to the interior of the block. i' Single-slip surface failure mode

[0082] The direction of block motion under the formula, n i' Let i be the upward unit normal vector of the structural surface i'.

[0083] The double-sliding failure mode refers to a movable block that can slide along adjacent structural surfaces i' and j'. In this mode, the block's motion direction is the undulation direction of the intersection line of the structural surfaces, and the following two kinematic conditions must be satisfied: the resultant vector of the active forces causes the block to contact structural surfaces i' and j'; the resultant vector of the active forces causes the block to separate from the other structural surfaces. These can be expressed as follows:

[0084]

[0085] In the formula s i'j' The direction of motion in the double-slip surface failure mode.

[0086] In this step, the formula for calculating the safety factor of the movable block using sliding force analysis is as follows:

[0087]

[0088] In the formula F s φ is the safety factor for the movable block. i' and φ j' The internal friction angles of structural surfaces i' and j' are respectively, N i' and N j' T represents the support forces of structural surfaces i' and j', respectively. i' The anti-slip force provided by T to structural surface i' i'j' The anti-slip forces provided for structural surfaces i' and j', single-slip surface failure mode: N i' =|r·n i' |,T i' =|r×n i' |;Dual-slip surface failure mode:

[0089] Determine whether the safety factor of the movable block is less than 1. If so, preliminarily determine that the movable block is a critical block.

[0090] Step S600: Based on the length of the structural surface trace, a probability analysis is performed on the formation of the preliminarily confirmed key block based on the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key block of the rock slope, and the block volume of the key block of the rock slope is calculated according to the plane equation of the structural surface.

[0091] Step S600 specifically includes:

[0092] The structural surface is transformed into a disk-shaped structural surface with a defined diameter, and the diameter distribution function of the structural surface is derived based on the probability density function of the trace length of the structural surface.

[0093] The cumulative distribution function is obtained based on the diameter distribution function;

[0094] Based on the cumulative distribution function and the diameter value required for the structural surface to form the initially determined key block, the probability that the diameter of a single structural surface is greater than the required diameter value is calculated.

[0095] The probability of forming the initially determined key block is calculated based on the probability that the diameter of a single structural surface is greater than the required diameter value;

[0096] The preliminary key block is further determined based on the block safety factor and the probability of forming the preliminary key block. If the ratio of the block safety factor to the probability of forming the preliminary key block is less than 1.3, the preliminary key block is identified as the key block of the rock slope.

[0097] The diameter distribution function of the disk-shaped structural surface is derived from the probability density function of the trace length of the structural surface, as shown in the following formula:

[0098]

[0099] In the formula h A (y) is the probability density function of the trace length of the structure surface, m represents the average diameter of the disk-shaped structure surface, x represents the diameter of the disk-shaped structure surface, and y represents the trace length of the structure surface. The m is obtained by the following formula:

[0100]

[0101] The calculation of the two formulas above is an iterative process. Substituting the initial value of m into the calculation formula of the diameter distribution function of the disk-shaped structure surface, we get g(x). Substituting g(x) into the calculation formula of the average value of the diameter of the disk-shaped structure surface, we get a new value of m, which is denoted as m1. We repeat the iterative process until |m1-m| is less than the preset convergence limit value, and then we obtain the final diameter distribution function of the disk-shaped structure surface g(x). Based on g(x), we obtain the cumulative distribution function.

[0102] The probability calculation formula for the diameter of a single structural surface being greater than the required value is as follows:

[0103] P(D>D i' ) = 1 - CDF(D i' )

[0104] In the formula, D is the diameter of the disk-shaped structural surface. i' Let i' be the diameter required for the structural plane to form a block, and CDF represent the cumulative distribution function.

[0105] Assuming the block is formed by k' structural planes, the formula for calculating the probability of the formation of the key block is as follows:

[0106] P=(1-CDF(D1))×(1-CDF(D2))…×(1-CDF(D k' ))

[0107] The semi-deterministic block theory, which takes into account the dimensions of structural surfaces, is used to further determine the previously identified key blocks. The determination formula is as follows:

[0108]

[0109] In the formula F s Let f be the safety factor of the block, and P be the probability of block formation. s If the value is less than 1.3, it is considered a key block that has been identified in the end.

[0110] By combining the structural plane equations of each set of structural surfaces that form the finite movable block, the vertex coordinates of each finite movable block are obtained, and the vertex coordinates are used to generate the block polyhedron model.

[0111] The block polyhedron model is divided into multiple convex tetrahedra, and the volume of a single convex tetrahedron is calculated based on the Euler tetrahedron formula, which is as follows:

[0112]

[0113] In the formula: V t To represent the volume of a tetrahedron, (x i” ,t i” ,z i” ) represents the coordinates of the i”th vertex of the tetrahedron.

[0114] The volume of the block polyhedron model is calculated based on the volume of a single convex tetrahedron.

[0115] The volume of a polyhedral block is calculated using the following formula:

[0116]

[0117] In the formula: V represents the volume of the polyhedron block, and n is the number of convex tetrahedrons that the polyhedron is divided into.

[0118] Example 2:

[0119] This embodiment provides a key block identification device for rock slopes based on three-dimensional point clouds. The device includes:

[0120] The acquisition module is used to acquire first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud.

[0121] The processing module is used to process the slope point cloud using a density-based clustering algorithm and a random sampling consensus algorithm to obtain the structural plane equation of the structural surface, the corresponding point cloud data of the structural surface, and the trace length of the structural surface. The structural surface is a preset infinitely extending structural plane.

[0122] The search module is used to search the point cloud data corresponding to the structural surface using a candidate block search algorithm to obtain a set of intersecting structural surfaces of the block.

[0123] The judgment module is used to judge the block formed by the set of intersecting structural surfaces using the finiteness theorem and mobility theorem in the deterministic block theory, and to determine the finite movable block.

[0124] The calculation module is used to calculate the block safety factor of the finite movable block based on the failure mode analysis method in block theory, the normal vector, the sliding force analysis method and the physical parameters, and to preliminarily identify the key blocks based on the block safety factor;

[0125] The identification module is used to perform a probability analysis on the formation of the initially confirmed key blocks based on the length of the structural surface trace and the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key blocks of the rock slope, and to calculate the block volume of the key blocks of the rock slope based on the plane equation of the structural surface.

[0126] The acquisition module includes:

[0127] The parameter acquisition module is used to acquire the physical and mechanical parameters of the target rock slope, including the unit weight of the rock mass, the friction angle of the structural surface, and the cohesion of the structural surface.

[0128] The image acquisition module is used to conduct aerial photography of the target rock slope area using a UAV. The flight path is designed according to the actual situation of the study area. Under the premise of ensuring overlap and resolution, the image information containing position and attitude information of the slope at different angles is obtained.

[0129] The matching module is used to extract feature points of the image information using the structure-reconstruction-motion algorithm, perform pairwise matching between images, and connect multiple images based on the common feature points obtained from the matching to form a trajectory and reconstruct the slope point cloud.

[0130] The processing module includes:

[0131] The normal vector calculation module is used to search the nearest neighbor set of each point in the slope point cloud based on the nearest neighbor search algorithm, and then calculate the normal vector of each point based on the principal component analysis algorithm.

[0132] A segmentation module is used to segment the slope point cloud using a density-based clustering algorithm to obtain individual structural surfaces;

[0133] A plane fitting module is used to perform plane fitting on each of the structural surfaces based on a random sampling consensus algorithm to obtain the structural surface plane equation and the corresponding point cloud data of the structural surface.

[0134] The structural surface trace length calculation module is used to calculate the maximum Euclidean distance between any two feature points within the structural surface, thereby obtaining the structural surface trace length of the structural surface.

[0135] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0136] Example 3:

[0137] Corresponding to the above method embodiments, this embodiment also provides a key block identification device for rock slopes based on three-dimensional point clouds. The key block identification device for rock slopes based on three-dimensional point clouds described below and the key block identification method for rock slopes based on three-dimensional point clouds described above can be referred to in correspondence.

[0138] Figure 3 This is a block diagram illustrating a key block identification device 800 for rock slopes based on a three-dimensional point cloud, according to an exemplary embodiment. Figure 3 As shown, the rock slope key block identification device 800 based on three-dimensional point cloud may include: a processor 801 and a memory 802. The rock slope key block identification device 800 based on three-dimensional point cloud may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0139] The processor 801 controls the overall operation of the 3D point cloud-based rock slope key block identification device 800 to complete all or part of the steps in the 3D point cloud-based rock slope key block identification method described above. The memory 802 stores various types of data to support the operation of the 3D point cloud-based rock slope key block identification device 800. This data may include, for example, instructions for any application or method operating on the 3D point cloud-based rock slope key block identification device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the 3D point cloud-based rock slope key block identification device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0140] In an exemplary embodiment, the rock slope key block identification device 800 based on three-dimensional point cloud can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described rock slope key block identification method based on three-dimensional point cloud.

[0141] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the above-described method for identifying key blocks of rock slopes based on three-dimensional point clouds. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the rock slope key block identification device 800 based on three-dimensional point clouds to complete the above-described method for identifying key blocks of rock slopes based on three-dimensional point clouds.

[0142] Example 4:

[0143] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the rock slope key block identification method based on three-dimensional point cloud described above.

[0144] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method embodiment described above for identifying key blocks of rock slopes based on three-dimensional point clouds.

[0145] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying key blocks in rock slopes based on three-dimensional point clouds, characterized in that, include: Obtain first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud; The slope point cloud is processed by density-based clustering algorithm and random sampling consensus algorithm to obtain the structural plane equation, the corresponding point cloud data of the structural plane, and the structural plane trace length. The structural plane is a preset infinitely extended structural plane. A candidate block search algorithm is used to search the point cloud data corresponding to the structural surfaces to obtain a set of intersecting structural surfaces of the blocks; The block formed by the set of intersecting structural surfaces is determined by the finiteness theorem and the mobility theorem in the deterministic block theory, thus identifying the finite movable block; The block safety factor of the finite movable block is calculated based on the failure mode analysis method, normal vector, sliding force analysis method and physical parameters in block theory, and the key blocks are preliminarily identified based on the block safety factor; Based on the length of the structural surface trace, a probabilistic analysis is performed on the formation of the initially identified key blocks based on the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key blocks of the rock slope, and the block volume of the key blocks of the rock slope is calculated according to the plane equation of the structural surface. Specifically, a candidate block search algorithm is used to search the point cloud data corresponding to the structural surfaces to obtain a set of intersecting structural surfaces of the blocks, including: If the distance between two feature points from two structural surfaces is less than a preset value, then the two structural surfaces are determined to intersect, and a set of a pair of intersecting structural surfaces is obtained, wherein the preset value is the maximum Euclidean distance between the two intersecting structural surfaces; A cyclic search is performed on the set storing a pair of intersecting structural surfaces. If there are three structural surfaces that intersect each other, then a set storing the three intersecting structural surfaces is obtained. A cyclic search is performed on the set storing the three intersecting structural surfaces. If there are two or more identical structural surfaces in two structural surface combinations, then the two structural surface combinations are merged into a new structural surface combination to obtain a new set of intersecting structural surfaces. Repeat the loop search operation on the new set of intersecting structural surfaces until no two combinations of structural surfaces contain two or more identical structural surfaces, and finally obtain the set of all intersecting structural surfaces that can form blocks.

2. The method for identifying key blocks of rock slopes based on three-dimensional point clouds according to claim 1, characterized in that... Obtain first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud, including: Obtain the physical and mechanical parameters of the target rock slope, including the unit weight of the rock mass, the friction angle of the structural surface, and the cohesion of the structural surface; Aerial photography of the target rock slope area was carried out using UAVs. Flight routes were designed according to the actual conditions of the study area. Under the premise of ensuring overlap and resolution, images of the slope at different angles containing position and attitude information were obtained to obtain image information. The feature points of the image information are extracted using the structure-reconstruction-motion algorithm, and pairwise matching is performed between the images. The multiple images are then connected based on the common feature points obtained from the matching to form a trajectory, and the slope point cloud is reconstructed.

3. The method for identifying key blocks of rock slopes based on three-dimensional point clouds according to claim 2, characterized in that... The slope point cloud is processed using a density-based clustering algorithm and a random sampling consensus algorithm to obtain the structural plane equation, the corresponding point cloud data, and the structural trace length. The structural plane is a pre-defined, infinitely extending structural plane, including: The nearest neighbor search algorithm is used to search for the nearest neighbor set of each point in the slope point cloud, and then the normal vector of each point is calculated based on the principal component analysis algorithm. The slope point cloud is segmented using a density-based clustering algorithm to obtain individual structural surfaces; Based on the random sampling consensus algorithm, each of the structural surfaces is fitted to a plane to obtain the structural surface plane equation and the corresponding point cloud data of the structural surface. Calculate the maximum Euclidean distance between any two feature points within the structure plane to obtain the structure plane trace length.

4. The method for identifying key blocks of rock slopes based on three-dimensional point clouds according to claim 1, characterized in that... Based on the finiteness theorem and mobility theorem in deterministic block theory, the blocks formed by the set of intersecting structural surfaces of the blocks are determined, and finite movable blocks are identified, including: The blocks are formed by the set of intersecting structural surfaces of the blocks; The block is finite in nature based on the finiteness theorem to obtain a finite block that satisfies the finiteness theorem. Based on the mobility theorem, the mobility of the finite block that satisfies the finiteness theorem is determined, resulting in a finite movable block that simultaneously satisfies both the finiteness theorem and the mobility theorem.

5. The method for identifying key blocks of rock slopes based on three-dimensional point clouds according to claim 1, characterized in that... Based on the failure mode analysis method in block theory, the normal vector, the sliding force analysis method, and the physical parameters, the block safety factor of the finite movable block is calculated. Based on the block safety factor, the critical blocks are preliminarily identified, including: The failure mode of the finite movable block is determined based on the failure mode analysis method and the normal vector. The safety factor of the movable block of the finite movable block is calculated based on the failure mode and sliding force analysis method of the finite movable block. Determine whether the safety factor of the movable block is less than 1. If so, preliminarily determine that the movable block is a critical block.

6. The method for identifying key blocks of rock slopes based on three-dimensional point clouds according to claim 1, characterized in that... The semi-deterministic block theory, which considers the dimensions of structural planes, is used to perform a probabilistic analysis on the formation of the key blocks, resulting in the finally identified key blocks of the rock slope, including: The structural surface is transformed into a disk-shaped structural surface with a defined diameter, and the diameter distribution function of the structural surface is derived based on the probability density function of the trace length of the structural surface. The cumulative distribution function is obtained based on the diameter distribution function; Based on the cumulative distribution function and the diameter value required for the structural surface to form the initially determined key block, the probability that the diameter of a single structural surface is greater than the required diameter value is calculated. The probability of forming the initially determined key block is calculated based on the probability that the diameter of a single structural surface is greater than the required diameter value; The preliminary key block is further determined based on the block safety factor and the probability of forming the preliminary key block. If the ratio of the block safety factor to the probability of forming the preliminary key block is less than 1.3, the preliminary key block is identified as the key block of the rock slope.

7. A key block identification device for rock slopes based on three-dimensional point clouds, characterized in that, include: The acquisition module is used to acquire first information, which includes the physical and mechanical parameters of the target rock slope and the slope point cloud. The processing module is used to process the slope point cloud using a density-based clustering algorithm and a random sampling consensus algorithm to obtain the structural plane equation of the structural surface, the corresponding point cloud data of the structural surface, and the trace length of the structural surface. The structural surface is a preset infinitely extending structural plane. The search module is used to search the point cloud data corresponding to the structural surface using a candidate block search algorithm to obtain a set of intersecting structural surfaces of the block. The judgment module is used to determine the finite movable block by using the finiteness theorem and the mobility theorem in the deterministic block theory to determine the block formed by the set of intersecting structural surfaces. The calculation module is used to calculate the block safety factor of the finite movable block based on the failure mode analysis method, normal vector, sliding force analysis method and physical parameters in the block theory, and to preliminarily identify the key blocks based on the block safety factor; The identification module is used to perform a probability analysis on the formation of the initially confirmed key blocks based on the length of the structural surface trace and the semi-deterministic block theory that takes into account the size of the structural surface, to obtain the finally identified key blocks of the rock slope, and to calculate the block volume of the key blocks of the rock slope based on the plane equation of the structural surface. Specifically, a candidate block search algorithm is used to search the point cloud data corresponding to the structural surfaces to obtain a set of intersecting structural surfaces of the blocks, including: If the distance between two feature points from two structural surfaces is less than a preset value, then the two structural surfaces are determined to intersect, and a set of a pair of intersecting structural surfaces is obtained, wherein the preset value is the maximum Euclidean distance between the two intersecting structural surfaces; A cyclic search is performed on the set storing a pair of intersecting structural surfaces. If there are three structural surfaces that intersect each other, then a set storing the three intersecting structural surfaces is obtained. A cyclic search is performed on the set storing the three intersecting structural surfaces. If there are two or more identical structural surfaces in two structural surface combinations, then the two structural surface combinations are merged into a new structural surface combination to obtain a new set of intersecting structural surfaces. Repeat the loop search operation on the new set of intersecting structural surfaces until no two combinations of structural surfaces contain two or more identical structural surfaces, and finally obtain the set of all intersecting structural surfaces that can form blocks.

8. The rock slope key block identification device based on three-dimensional point cloud according to claim 7, characterized in that, The acquisition module includes: The parameter acquisition module is used to acquire the physical and mechanical parameters of the target rock slope, including the unit weight of the rock mass, the friction angle of the structural surface, and the cohesion of the structural surface. The image acquisition module is used to conduct aerial photography of the target rock slope area using a UAV. The flight path is designed according to the actual situation of the study area. Under the premise of ensuring overlap and resolution, the image information containing position and attitude information of the slope at different angles is obtained. The matching module is used to extract feature points of the image information using the structure-reconstruction-motion algorithm, perform pairwise matching between images, and connect multiple images based on the common feature points obtained from the matching to form a trajectory and reconstruct the slope point cloud.

9. The rock slope key block identification device based on three-dimensional point cloud according to claim 8, characterized in that, The processing module includes: The normal vector calculation module is used to search the nearest neighbor set of each point in the slope point cloud based on the nearest neighbor search algorithm, and then calculate the normal vector of each point based on the principal component analysis algorithm. A segmentation module is used to segment the slope point cloud using a density-based clustering algorithm to obtain individual structural surfaces; A plane fitting module is used to perform plane fitting on each of the structural surfaces based on a random sampling consensus algorithm to obtain the structural surface plane equation and the corresponding point cloud data of the structural surface. The structural surface trace length calculation module is used to calculate the maximum Euclidean distance between any two feature points within the structural surface, thereby obtaining the structural surface trace length of the structural surface.

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