A rock slope sliding surface identification method and system

Abstract

The present application relates to the technical field of slope sliding surface identification, and provides a method and system for identifying sliding surfaces of rock slopes. The method comprises: obtaining geometric and mechanical model data of a target rock slope; generating a total displacement cloud map, wherein the total displacement cloud map is obtained by calculating the geometric and mechanical model data using finite element-strength reduction software; obtaining multiple block binary images; obtaining a slider area, wherein the slider area is obtained by marking each connected domain in the multiple block binary images and calculating its area, and screening according to the area calculation results; using the alpha shapes algorithm to extract the boundary of the slider area and generate a slider boundary; removing the slope boundary in the slider boundary to obtain the sliding surface of the target rock slope. The present application solves the problem that the existing slope sliding surface identification method has poor recognition on slopes, especially on rock slopes, and achieves the technical effect of improving the accuracy of sliding surface identification.

Description

Technical Field

[0001] The present application relates to the technical field of slope sliding surface identification, and in particular to a method and system for identifying a rock slope sliding surface. Background Art

[0002] In landslide risk assessment, the scale of landslide is directly controlled by the position and shape of the sliding surface. In addition, the position of the sliding surface determines the layout and structural parameters of reinforcement structures such as anchors. Only when the position of the sliding surface is determined in advance can the arrangement of reinforcement structures such as anti-sliding piles, anchors and retaining walls be carried out. Blindly reinforcing the slope without identifying the sliding surface or inaccurately identifying the sliding surface may fail to achieve the expected reinforcement effect due to the fact that the reinforcement structure does not pass through the sliding surface or the depth buried below the sliding surface is too shallow. For example, the slope of a certain project was damaged due to the incorrect determination of the sliding surface position, resulting in the anti-sliding piles not passing through the sliding surface; although the anti-sliding piles of another slope passed through the sliding surface, the slope was still damaged. The fundamental reason was that the depth of the anti-sliding piles buried below the sliding surface was only 0.5m, that is, the depth of the anti-sliding piles buried under the sliding surface was too shallow. Therefore, the correctness of the determination of the sliding surface position and its shape directly affects the effect of anti-sliding engineering and has important engineering significance. At present, the methods for obtaining sliding surfaces can be roughly divided into two categories. The first category is the sliding surface search method based on the limit equilibrium method. The second category is the sliding surface search method based on the finite element-strength reduction method.

[0003] In summary, the present application solves the problem that the existing slope sliding surface identification method has poor identification on slopes, especially on rock slopes. Summary of the invention

[0004] Based on this, it is necessary to provide a rock slope sliding surface identification method and system that can improve the accuracy of sliding surface identification in response to the above technical problems. This application solves the problem that the existing slope sliding surface identification method has poor recognition on slopes, especially on rock slopes, and achieves the technical effect of improving the accuracy of sliding surface identification.

[0005] In a first aspect, the present application provides a method for identifying sliding surfaces of rock slopes, the method also comprising: obtaining geometric and mechanical model data of a target rock slope, the geometric and mechanical model data being obtained by conducting geological surveys on the target rock slope; generating a total displacement cloud map, the total displacement cloud map being obtained by calculating the geometric and mechanical model data using finite element-strength reduction software; obtaining a plurality of block binary images, the block binary images being obtained by performing image segmentation and binarization on the total displacement cloud map; obtaining a slider area, the slider area being obtained by marking each connected domain in the plurality of block binary images and calculating its area, and screening according to the area calculation results; using an alphashapes algorithm to extract the boundary of the slider area to obtain a slider boundary, wherein the slider boundary includes a portion of the slope boundary; removing the slope boundary in the slider boundary to obtain the sliding surface of the target rock slope.

[0006] In a second aspect, the present application provides a rock slope sliding surface identification system, the system also includes: a rock slope geometry and mechanical model data acquisition module, the rock slope geometry and mechanical model data acquisition module is used to obtain the geometry and mechanical model data of the target rock slope, the geometry and mechanical model data is obtained by geological survey of the target rock slope; a total displacement cloud map generation module, the total displacement cloud map generation module is used to generate a total displacement cloud map, the total displacement cloud map is obtained by calculating the geometry and mechanical model data through finite element-strength reduction software; multiple block binary map acquisition modules, the multiple block binary map acquisition modules are used to obtain multiple block binary maps, the block binary maps are obtained by image segmentation and binarization of the total displacement cloud map; a slider area acquisition module, the slider area acquisition module is used to obtain a slider area, the slider area is obtained by marking each connected domain in the multiple block binary maps and calculating its area, and screening according to the area calculation result; a slider boundary extraction module, the slider boundary extraction module is used to use alpha The shapes algorithm extracts the boundary of the slider area to obtain the slider boundary, wherein the slider boundary includes part of the slope boundary; a rock slope sliding surface acquisition module is used to remove the slope boundary in the slider boundary to obtain the sliding surface of the target rock slope.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, the geometric and mechanical model data of the target rock slope are obtained, and the geometric and mechanical model data are obtained by geological survey of the target rock slope; secondly, a total displacement cloud map is generated, and the total displacement cloud map is obtained by calculating the geometric and mechanical model data through finite element-strength reduction software; then multiple block binary images are obtained, and the block binary images are obtained by image segmentation and binarization of the total displacement cloud map; next, the slider area is obtained, and the slider area is obtained by marking each connected domain in the multiple block binary images and calculating its area, and screening according to the area calculation results; then the alpha shapes algorithm is used to extract the boundary of the slider area to generate a slider boundary, wherein the slider boundary includes part of the slope boundary; finally, the slope boundary in the slider boundary is removed to obtain the sliding surface of the target rock slope. The present application solves the problem that the existing slope sliding surface recognition method has poor recognition on the slope, especially on the rock slope, and achieves the technical effect of improving the accuracy of sliding surface recognition.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a flow chart of a rock slope sliding surface identification method in one embodiment;

[0011] Figure 2 A schematic diagram of a process of using the ordered array of remaining sliding block boundary points as a sliding surface in a rock slope sliding surface identification method in an embodiment;

[0012] Figure 3 The figure is a structural block diagram of a rock slope sliding surface identification system in one embodiment.

[0013] Explanation of reference numerals: rock slope geometry and mechanical model data acquisition module 11, total displacement cloud map generation module 12, multiple block binary map acquisition module 13, slider area acquisition module 14, slider boundary extraction module 15, rock slope sliding surface acquisition module 16. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] like Figure 1 As shown, a method for identifying a sliding surface of a rock slope, the method further comprising:

[0017] Obtaining geometric and mechanical model data of a target rock slope, wherein the geometric and mechanical model data are obtained by conducting a geological survey on the target rock slope;

[0018] The sliding surface is an interface formed between the landslide body and the immovable body (parent body) when the landslide body moves, and the landslide slides down along it. This surface is called a sliding surface, or sliding surface for short. Since the current sliding surface identification methods are relatively poor, this application aims at the defects of the current sliding surface identification and provides a rock slope sliding surface search method based on the total displacement cloud map, which can solve the problem of poor identification of existing slope sliding surface identification methods, especially on rock slopes.

[0019] The target rock slope refers to a slope with various irregular joints and faults in the rock mass. The instability and failure of the rock slope is often closely related to the development of the sliding surface and the rock joints. In addition to sliding failure, rock slope failure also includes collapse-type failure and dumping-type failure. The main research in this application is the identification of sliding surfaces, rather than collapse-type failure and dumping-type failure. Geological survey refers to the use of geophysical exploration, drilling and pit exploration to survey the target rock slope. Geophysical exploration refers to the use of special instruments to measure the physical parameters of the rock mass density, conductivity, elasticity, etc. of the target slope; drilling and pit exploration use drilling rigs or tunnel excavation to directly expose the engineering geological conditions inside the target slope. The geometric and mechanical model data of the rock slope refers to the data obtained through geological survey of the target rock slope. By obtaining the geometric and mechanical model data of the target rock slope, data support is provided for the subsequent identification of the sliding surface of the target rock slope.

[0020] Generate a total displacement cloud map, wherein the total displacement cloud map is obtained by calculating the geometric and mechanical model data using finite element-strength reduction software;

[0021] The total displacement cloud map refers to the displacement of each point inside the target rock slope after it reaches the critical state after the finite element-strength reduction calculation is performed on the geometric and mechanical model of the target rock slope using the finite element-strength reduction software. Then, these displacements are represented on the slope model in different colors or shades to form an image with color representing the total displacement size, namely the total displacement cloud map. The finite element software described in the finite element-strength reduction software includes Phase 2 , FLAC3D, ANSYS, etc. The saved total displacement cloud map should be in ".jpg" format. When the total displacement cloud map is obtained, the safety factor of the rock slope can also be obtained, based on which a preliminary judgment can be made on the stability of the rock slope. The total displacement cloud map is obtained by calculating the geometric and mechanical model data according to the finite element-strength reduction software. The total displacement cloud map can be used to support the subsequent research on sliding surface identification of the rock slope.

[0022] Obtaining a plurality of block binary images, wherein the block binary images are obtained by performing image segmentation and binarization on the total displacement cloud image;

[0023] A binary image refers to an image in which each pixel has only two possible values, and is used for information hiding and image edge extraction. A block binary image refers to an image obtained by segmenting and binarizing the total displacement cloud image. Image segmentation refers to the technology and process of dividing an image into several specific regions with unique properties and proposing targets of interest. It is a key step from image processing to image analysis. Multiple block binary images are obtained by segmenting the total displacement cloud image, which paves the way for the subsequent calculation of the slider area.

[0024] Perform image segmentation on the total displacement cloud image based on K-means clustering image segmentation technology to obtain multiple block images;

[0025] The plurality of block images are subjected to image binarization processing to obtain a plurality of block binary images.

[0026] K-means clustering is an unsupervised learning algorithm used for clustering data. Its goal is to divide data points into K different clusters so that the data points in each cluster are similar, while the data points between different clusters are relatively different. The image segmentation technology based on K-means clustering can be implemented by programming in programming languages ​​such as Python; block image refers to the image of the "sliding block" obtained after image segmentation; image binarization processing means that after image segmentation, the image has only two colors left, and binarization is to set the darker pixel points to black and the lighter pixel points to white; obtain multiple block binary images. The total displacement cloud map is segmented according to the image segmentation technology of K-means clustering to obtain multiple block images, and the block image is binarized to obtain multiple block binary images, which provides data support for subsequent calculation work.

[0027] Read the color information of each pixel in the total displacement cloud map to construct a target pixel data set;

[0028] The distance between any two pixels in the target pixel data set is calculated, where the pixel distance calculation formula is:

[0029] Among them, d ij Represents the distance between two pixels, p i =(r i ,g i ,b i )(i=[1,2,…,N]) represents the color information of the i-th pixel;

[0030] Randomly select 2 pixel points from the N pixel points of the target pixel data set as initial clustering centers;

[0031] The distance d between each pixel and the two cluster centers ij As the basis for division, each pixel is divided into the nearest cluster;

[0032] Recalculate the cluster center of each cluster;

[0033] Iterative clustering is continuously performed until the cluster center no longer changes, thereby completing the image segmentation of the total displacement cloud map.

[0034] A pixel is the smallest image unit. A picture is composed of a large number of pixels. The color information of each pixel in the total displacement cloud map is read and the color information of the pixel is combined into a target pixel data set. The distance between any two pixels in the target pixel data set is calculated, where the pixel distance calculation formula is: Among them, d ijRepresents the distance between two pixels, p i =(r i ,g i ,b i )(i=[1,2,…,N]) represents the color information of the ith pixel; the initial cluster center refers to the two pixels selected at the beginning of clustering, and the distance between each pixel and the two cluster centers is used as the basis for division, and each pixel is divided into the nearest cluster; the mean value μ(j) of all data in a cluster is usually called the centroid of this cluster, and j represents the jth cluster. In a two-dimensional plane, the abscissa of the centroid of a cluster of data points is the mean of the abscissas of the data points in this cluster, and the ordinate of the centroid is the mean of the ordinates of the data points in this cluster; the centroid of each cluster is recalculated; iterative clustering is continuously performed until the cluster center no longer changes, and the image segmentation of the total displacement cloud map is completed. By segmenting the image of the total displacement cloud map, multiple block binary images are obtained, which supports subsequent calculations.

[0035] Obtaining a slider region, wherein the slider region is obtained by marking each connected domain in the plurality of block binary images, calculating its area, and screening according to the area calculation results;

[0036] The slider area refers to the exposed part of the interface where the upper part of the landslide body is separated from the immovable body; the connected domain refers to a set of adjacent pixels with the same pixel value. A mark is set for each connected domain to complete the connected domain analysis. It is necessary to determine which connected domain a pixel belongs to; the connected domain is marked and the area of ​​the connected domain is calculated. The connected domain includes various types of blocks; screening refers to excluding the block with the largest area, inverting the colors of other blocks to remove other blocks and leave the slider; each connected domain in the binary map of multiple blocks is marked and its area is calculated, and the slider area is obtained by screening according to the area calculation results, which provides technical support for subsequent slider area identification.

[0037] The connected area mark refers to the marking of the black connected areas in the binary image of the block, and for the connected areas, their area A k The calculation method is as follows:

[0038]

[0039] In the formula,

[0040] Among them, C k Characterize the connected domain, A k Characterize the area of ​​the connected domain;

[0041] The color inversion refers to changing the color of a pixel from black (white) to white (black).

[0042] The opposite color is also called the complementary color. The complementary color of red is green, the complementary color of blue is orange, and the complementary color of yellow is purple. These three contrast relationships can lead to many contrasting opposite colors, such as black and white, red and green, etc. The opposite color is a color that can be turned into white when superimposed with the primary color, that is, white (RGB: 255, 255, 255) is used to represent the color of the primary color. For example, the opposite color of red (RGB: 255, 0, 0) is green (RGB: 0, 255, 255); the area of ​​the connected domain A K The calculation method is as follows:

[0043]

[0044] In the formula,

[0045] Among them, C k Characterize the connected domain, A k Characterize the area of ​​the connected domain; the inverted color mark refers to changing the color of the pixel from black (white) to white (black). By calculating the area of ​​the connected domain, it paves the way for subsequent convenient extraction.

[0046] Using an alpha shapes algorithm to extract the boundary of the slider area to generate a slider boundary, wherein the slider boundary includes a portion of the slope boundary;

[0047] The Alpha shapes algorithm is an algorithm for extracting the boundaries of two-dimensional or three-dimensional point sets. This algorithm is mainly used when calculating the geometric shapes of molecular structures, such as proteins and molecular models. In this application, it refers to calculating the geometric shapes of pixel points to obtain the slider area for boundary extraction; the slider boundary refers to the boundary of the geometric image of the slider area; the slider boundary includes part of the slope boundary, and the slope boundary refers to the geometric model boundary in the constructed target rock slope geometry and mechanical model; by obtaining the slider boundary, the subsequent rock slope sliding surface is paved.

[0048] The alpha shapes algorithm can be implemented by programming in programming languages ​​such as Python;

[0049] The slider boundary refers to an ordered array of slider boundary points.

[0050] A programming language can be simply understood as a language that can be recognized by both computers and humans. A computer language allows programmers to accurately define the data that the computer needs to use and precisely define the actions that should be taken in different situations. Each language has its specific purpose and different development trajectory. In this application, Python is used to implement the alpha shapes algorithm to obtain the slider boundary; the ordered array is obtained based on the slider boundary to provide data support for subsequent sliding surface identification.

[0051] The slope boundary in the sliding block boundary is removed to obtain the sliding surface of the target rock slope.

[0052] like Figure 2 As shown, the boundary point coordinates in the ordered array are traversed and compared with the slope boundary segment equation;

[0053] When the boundary points in the ordered array are on the slope boundary segment equation, the boundary points are removed to obtain the remaining slider boundary points;

[0054] The ordered array of the remaining slider boundary points is used as the sliding surface.

[0055] The method for removing the slope boundary is to compare the coordinates of the slider boundary point with the slope boundary line segment equation. If the slider boundary point is on the line segment equation, the slider boundary point is removed, and the ordered array of the remaining slider boundary points is the sliding surface. The application solves the problem that the existing slope sliding surface identification method has poor recognition on the slope, especially on the rock slope, and achieves the technical effect of improving the accuracy of sliding surface identification.

[0056] like Figure 3 As shown, the embodiment of the present application also provides a rock slope sliding surface identification system, the system also includes:

[0057] A rock slope geometry and mechanical model data acquisition module 11, which is used to obtain geometry and mechanical model data of a target rock slope, wherein the geometry and mechanical model data are obtained by conducting a geological survey on the target rock slope;

[0058] A total displacement cloud map generating module 12, the total displacement cloud map generating module 12 is used to generate a total displacement cloud map, the total displacement cloud map is obtained by calculating the geometric and mechanical model data using finite element-strength reduction software;

[0059] A plurality of block binary image acquisition modules 13, wherein the plurality of block binary image acquisition modules 13 are used to obtain a plurality of block binary images, wherein the block binary images are obtained by performing image segmentation and binarization on the total displacement cloud image;

[0060] A slider region obtaining module 14, the slider region obtaining module 14 is used to obtain a slider region, the slider region is obtained by marking each connected domain in the plurality of block binary images and calculating its area, and screening according to the area calculation result;

[0061] A slider boundary extraction module 15, wherein the slider boundary extraction module 15 is used to extract the boundary of the slider region using an alpha shapes algorithm to obtain a slider boundary, wherein the slider boundary includes a portion of a slope boundary;

[0062] The rock slope sliding surface obtaining module 16 is used to remove the slope boundary in the sliding block boundary to obtain the sliding surface of the target rock slope.

[0063] Furthermore, the embodiment of the present application also includes:

[0064] A plurality of block image acquisition modules, wherein the plurality of block image acquisition modules are used to perform image segmentation on the total displacement cloud image based on the image segmentation technology of K-means clustering to obtain a plurality of block images;

[0065] The block binary image acquisition module is used to perform image binarization processing on the plurality of block images to obtain the plurality of block binary images.

[0066] Furthermore, the embodiment of the present application also includes:

[0067] A target pixel data set construction module, wherein the target pixel data set construction module is used to read the color information of each pixel point in the total displacement cloud map to construct a target pixel data set;

[0068] The pixel distance calculation module is used to calculate the distance between any two pixels in the target pixel data set, wherein the pixel distance calculation formula is:

[0069] Among them, d ij Represents the distance between two pixels, p i =(r i ,g i ,b i )(i=[1,2,…,N]) represents the color information of the i-th pixel;

[0070] An initial cluster center acquisition module, wherein the initial cluster center acquisition module is used to randomly select 2 pixel points from the N pixel points in the target pixel data set as initial cluster centers;

[0071] A pixel point division module is used to divide each pixel point into two cluster centers based on the distance d between each pixel point and the two cluster centers. ij As the basis for division, each pixel is divided into the nearest cluster;

[0072] A recalculation module, the recalculation module is used to recalculate the cluster center of each cluster;

[0073] The image segmentation module is used to continuously perform iterative clustering until the cluster center no longer changes, thereby completing the image segmentation of the total displacement cloud map.

[0074] Furthermore, the embodiment of the present application also includes:

[0075] The connected domain area calculation module is used for the connected area marking, which means that the black connected domains in the block binary image are marked in reverse color, and for the connected domain, its area A K The calculation method is as follows:

[0076]

[0077] In the formula,

[0078] Among them, C k Characterize the connected domain, A k Characterize the area of ​​the connected domain;

[0079] The color inversion module is to change the color of the pixel from black (white) to white (black).

[0080] Furthermore, the embodiment of the present application also includes:

[0081] A programming implementation module, wherein the alpha shapes algorithm can be implemented by programming in a programming language such as Python;

[0082] The ordered array determination module, wherein the ordered array determination module is used for the slider boundary, refers to an ordered array of slider boundary points.

[0083] Furthermore, the embodiment of the present application also includes:

[0084] A traversal comparison module, the traversal comparison module is used to traverse and compare the boundary point coordinates in the ordered array with the slope boundary segment equation;

[0085] A remaining slider boundary point acquisition module, wherein the remaining slider boundary point acquisition module is used to remove the boundary points in the ordered array when the boundary points are on the slope boundary line segment equation, so as to obtain the remaining slider boundary points;

[0086] A sliding surface obtaining module is used to use the ordered array of the remaining slider boundary points as a sliding surface.

[0087] For a specific embodiment of a rock slope sliding surface identification system, please refer to the embodiment of a rock slope sliding surface identification method described above, which will not be repeated here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0088] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for identifying sliding surfaces of rock slopes, characterized in that: The method further comprises: Obtaining geometric and mechanical model data of a target rock slope, wherein the geometric and mechanical model data are obtained by conducting a geological survey on the target rock slope; Generate a total displacement cloud map, wherein the total displacement cloud map is obtained by calculating the geometric and mechanical model data of the target rock slope using finite element-strength reduction software; Obtaining a plurality of block binary images, wherein the block binary images are obtained by performing image segmentation on the total displacement cloud image and binarizing the image; Obtaining a slider region, wherein the slider region is obtained by marking each connected domain in the plurality of block binary images, calculating its area, and screening according to the area calculation results; Using an alpha shapes algorithm to extract the boundary of the slider area to obtain a slider boundary, wherein the slider boundary includes a portion of the slope boundary; The slope boundary in the sliding block boundary is removed to obtain the sliding surface of the target rock slope.

2. The method according to claim 1, characterized in that The obtaining of a plurality of block binary images, wherein the block binary images are obtained by performing image segmentation and binarization on the total displacement cloud image, further comprises: Perform image segmentation on the total displacement cloud image based on K-means clustering image segmentation technology to obtain multiple block images; The plurality of block images are subjected to image binarization processing to obtain a plurality of block binary images.

3. The method according to claim 2, characterized in that The image segmentation technology based on K-means clustering performs image segmentation on the total displacement cloud map, and further includes: Read the color information of each pixel in the total displacement cloud map to construct a target pixel data set; The distance between any two pixels in the target pixel data set is calculated, where the pixel distance calculation formula is: Among them, d ij Represents the distance between two pixels, p i =(r i ,g i ,b i )(i=[1,2,…,N]) represents the color information of the i-th pixel; Randomly select 2 pixel points from the N pixel points of the target pixel data set as initial clustering centers; The distance d between each pixel and the two cluster centers ij As the basis for division, each pixel is divided into the nearest cluster; Recalculate the cluster center of each cluster; Iterative clustering is continuously performed until the cluster center no longer changes, thereby completing the image segmentation of the total displacement cloud map.

4. The method according to claim 1, characterized in that The method further comprises: Connected region marking refers to marking the connected domain composed of black pixels in the binary image of the block, and for the connected domain, its area A k The calculation method is as follows: In the formula, Among them, C k Characterize the connected domain, A k Characterize the area of ​​the connected domain; The screening based on area is to invert the colors of all connected domains except the one with the largest area, and inverting the colors is to change the color of the pixel from black to white or from white to black.

5. The method according to claim 1, characterized in that The use of the alpha shapes algorithm to extract the boundary of the slider area to generate a slider boundary, wherein the slider boundary includes a portion of the slope boundary, further includes: The alpha shapes algorithm can be implemented by programming in Python programming language; The slider boundary refers to an ordered array of slider boundary points.

6. The method according to claim 5, characterized in that The step of removing the slope boundary in the sliding block boundary to obtain the sliding surface of the target rock slope further includes: Traversing and comparing the boundary point coordinates in the ordered array with the slope boundary line segment equation; When the boundary points in the ordered array are on the slope boundary segment equation, the boundary points are eliminated to obtain the remaining slider boundary points; The ordered array of the remaining slider boundary points is used as the sliding surface.

7. A rock slope sliding surface identification system, characterized in that: The system is used to perform the steps of any one of the rock slope sliding surface identification methods described in claims 1-6, and further comprises: A rock slope geometry and mechanical model data acquisition module, which is used to obtain geometry and mechanical model data of a target rock slope, wherein the geometry and mechanical model data are obtained by conducting a geological survey on the target rock slope; A total displacement cloud map generation module, the total displacement cloud map generation module is used to generate a total displacement cloud map, the total displacement cloud map is obtained by calculating the geometric and mechanical model data using finite element-strength reduction software; A plurality of block binary image acquisition modules, wherein the plurality of block binary image acquisition modules are used to obtain a plurality of block binary images, wherein the block binary images are obtained by performing image segmentation and binarization on the total displacement cloud image; A slider area acquisition module, the slider area acquisition module is used to obtain a slider area, the slider area is obtained by marking each connected domain in the plurality of block binary images and calculating its area, and screening according to the area calculation results; A slider boundary extraction module, the slider boundary extraction module is used to extract the boundary of the slider area using an alpha shapes algorithm to obtain a slider boundary, wherein the slider boundary includes a portion of a slope boundary; A rock slope sliding surface acquisition module is used to remove the slope boundary in the sliding block boundary to obtain the sliding surface of the target rock slope.

Images