A method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes

Abstract

This application proposes an intelligent extraction and structured transformation method for holographic features of open-pit mine slopes, belonging to the field of intelligent slope feature technology. The method includes: establishing a color-coded parameter mapping model; assigning corresponding color attributes to each layer according to the parameter mapping model to generate a parsable digital slope file; extracting the slope's geometric shape from the digital slope file and calculating the bounding box of the slope region; performing meshing processing on the bounding box of the slope region, converting the data in the meshed bounding box into fixed-size three-dimensional mesh data; identifying the external contour line of the slope using a contour extraction algorithm based on the fixed-size three-dimensional mesh data; calculating the local dip angle of each contour segment based on the coordinates of adjacent points in the discrete surface point set; and performing semantic segmentation along the external contour line of the slope based on the local dip angle of each contour segment. This method solves the problem that slope features are difficult for computers to efficiently identify and use for deep learning.

Description

Technical Field

[0001] This invention belongs to the field of intelligent evaluation and analysis technology of slope stability, specifically involving a method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes. Background Technology

[0002] In open-pit mining, slope stability analysis is a key technology for ensuring safe production, optimizing mining plans, and preventing geological disasters. Existing analysis methods primarily rely on manually interpreting DXF format files to obtain slope geometry, lithology, and geomechanical parameters (including cohesion c, internal friction angle φ, and unit weight γ), which are then imported into slope stability analysis software for modeling and calculation. However, the slope features extracted by this method are typically in a non-standardized, discrete form, making them difficult for computers to efficiently identify, manage uniformly, and reuse, thus hindering the development of intelligent slope evaluation technology based on deep learning. Therefore, there is an urgent need for an intelligent extraction and structured transformation method for holographic features of open-pit mine slopes. This method would automatically and completely extract key features such as slope geometry, lithology, critical slip surfaces, and their corresponding stability coefficients from DXF format files, integrating them into a structured data system that can be deeply recognized and processed by computers, providing a standardized and structured data foundation for intelligent slope stability evaluation. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this application proposes a method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes.

[0004] In a first aspect, the present invention provides a method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes, comprising:

[0005] The slope is divided into multiple closed polygons based on the lithology of the strata, and different layers are assigned to each polygon and the critical slip surface of the slope.

[0006] In each layer, the holographic features of the open-pit mine slope are obtained. The holographic features of the open-pit mine slope include: the slope geometry, stratigraphic lithology, and geotechnical parameters, wherein the geotechnical parameters include: cohesion, internal friction angle, and unit weight.

[0007] Based on the slope's geometry, lithology, and geotechnical parameters, slope stability analysis software was used to analyze the slope and obtain the morphological parameters of the critical slip surface and the minimum stability coefficient of the slope.

[0008] Based on the preset ranges of cohesion, internal friction angle, and bulk density, a parameter mapping model for color coding is established by linearly mapping different colors in each layer.

[0009] Based on the color-coded parameter mapping model, geotechnical parameters, and the minimum stability coefficient of the slope, each layer is assigned a corresponding color attribute, and a parsable digital slope file is generated. The digital slope file includes: the slope geometry, strata lithology, color coding of geotechnical parameters, morphological parameters of the critical slip surface, and color coding of the minimum stability coefficient of the slope.

[0010] Extract the slope geometry from the parseable digitized slope file and calculate the bounding box of the slope region based on the slope geometry.

[0011] Mesh the bounding box in the slope area, and then convert the data in the meshed bounding box into fixed-size three-dimensional mesh data;

[0012] Based on three-dimensional mesh data of fixed size, a contour extraction algorithm is used to identify the external contour line of the slope, which includes a discrete set of surface points.

[0013] Calculate the local tilt angle of each contour segment based on the coordinates of adjacent points in the discrete surface point set;

[0014] Along the outer contour line of the slope, semantic segmentation is performed based on the local dip angle of each contour segment. Line segments with a local dip angle less than or equal to a preset threshold are marked as "horizontal flat plate", and line segments with a local dip angle greater than the preset threshold are marked as "stepped slope".

[0015] The parameter mapping model for the color encoding is calculated as follows:

[0016] ;

[0017] ;

[0018] ;

[0019] Where R is the red component in the color-coded parametric mapping model, G is the green component in the color-coded parametric mapping model, and B is the blue component in the color-coded parametric mapping model. It is the internal friction angle. For cohesion, For density.

[0020] The parameter mapping model based on color encoding, geotechnical parameters, and the minimum stability coefficient of the slope assigns a corresponding color attribute to each layer, calculated as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] in, This is the minimum stability coefficient of the slope. The red component represents the critical slip surface. The green component of the critical slip surface. The blue component represents the critical slip surface. For the red component of the i-th lithology layer, For the green component of the i-th lithology layer, For the blue component of the i-th lithology layer, Let i be the internal friction angle of the i-th lithology. For the cohesion of the i-th lithology, Let be the unit weight of the i-th lithology.

[0026] Based on the geometry of the slope, the bounding box of the slope region is calculated using the following formula:

[0027] ;

[0028] in, This is the bounding box for the slope area, where... The x-axis coordinate is the minimum value in the polygon vertex coordinate system of the slope. The polygon vertex coordinate system takes the lowest point on one side of the slope profile as the origin, defines the horizontal direction as the x-axis, and the vertical direction as the y-axis. This represents the minimum y-axis coordinate in the coordinate system of all polygon vertices on the slope. This represents the maximum y-axis coordinate in the coordinate system of all polygon vertices on the slope. It represents the maximum x-axis coordinate in the coordinate system of all polygon vertices of the slope.

[0029] The process of meshing the bounding box in the slope area, and converting the data in the meshed bounding box into fixed-size three-dimensional mesh data, includes:

[0030] Within the bounding box of the slope region, establish a linear mapping from the continuous physical coordinate system to the discrete pixel coordinate system;

[0031] The set of polygons in the bounding box of the slope region is converted into a slope physical domain, which is the Boolean union of all polygons;

[0032] In the discrete pixel coordinate system, the slope physical domain is rasterized using the scan line fill algorithm to generate a slope region mask.

[0033] Based on the slope area mask, the geotechnical parameters are mapped to the corresponding feature channels;

[0034] The Bresenham algorithm is used to rasterize the curve of the critical slip surface, calculate the best approximation value of the path point set of the critical slip surface in the three-dimensional grid, and generate the critical slip surface probability based on the best approximation value.

[0035] The row coordinates of the discrete pixel coordinate system, the column coordinates of the discrete pixel coordinate system, and the feature channels constitute a three-dimensional grid data of fixed size. The feature channels include: geotechnical parameters, slope area mask, and critical slip surface probability.

[0036] In the discrete pixel coordinate system, the slope physical domain is rasterized using a scan line fill algorithm to generate a slope region mask. The calculation formula is as follows:

[0037] ;

[0038] in, In discrete pixel coordinate system The lower slope area is covered by a mask. This refers to the physical domain of the slope.

[0039] The geotechnical parameters are mapped to the corresponding feature channels based on the slope area mask, and the calculation formula is as follows:

[0040] exist In the region where =1, ;

[0041] in, It is the internal friction angle. For cohesion, For density, In the discrete pixel coordinate system The tensor element value at the ch-th feature channel. This is a scalar of lithological physical properties obtained based on RGB color space mapping. This is the feature channel index.

[0042] The local inclination angle of each contour segment is calculated based on the coordinates of adjacent points in the discrete surface point set, using the following formula:

[0043] ;

[0044] ;

[0045] ;

[0046] in, In discrete pixel coordinate system The lower slope area is covered by a mask. It is a function of the slope surface height. For a discrete surface point set, Let (u) be the local tilt angle of the i-th contour segment. i ,v i ) is the first adjacent point p i Discrete pixel coordinates, (u i+1 ,v i+1 ) is the second adjacent point p i+1 Discrete pixel coordinates For surface node pairs, represent a specific mesh coordinate point. This represents the total number of nodes or pixels in the horizontal direction of the structured mesh.

[0047] The outer contour line along the slope is semantically segmented based on the local dip angle of each contour segment, and the calculation formula is as follows:

[0048] When the local tilt angle of the i-th contour segment is less than the first preset threshold, and the length of pixels that continuously satisfy this condition is greater than or equal to the second preset threshold, the current region is marked as "horizontal flat".

[0049] When the local tilt angle is greater than or equal to the first preset threshold, the current area is marked as a "stepped slope".

[0050] Secondly, this application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes.

[0051] Beneficial effects:

[0052] This application provides a method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes, aiming to achieve standardized and structured transformation from DXF format files (i.e., parsable digital slope files) to machine learning-usable data. A parameter mapping model is established based on color encoding, mapping the RGB color components of the lithological stratigraphic layer to the internal friction angle, cohesion, and unit weight, respectively, and mapping the color of the critical slip surface layer to the corresponding stability coefficient. Using a Python script, geometric information such as slope contour, lithological zoning, and critical slip surface is intelligently extracted from the DXF format file and converted into three-dimensional mesh data. The feature channels of the mesh data include the internal friction angle, cohesion, unit weight, slope area mask, and the probability distribution of the critical slip surface generated by rasterization based on the Bresenham algorithm. Multi-channel data fusion and visualization techniques are employed to generate heat maps, probability maps, and binary mask maps, which are stored in compressed binary format, providing a standardized and structured data foundation for intelligent slope stability evaluation. Attached Figure Description

[0053] Figure 1 Flowchart of a method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes according to an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of slope geometry according to an embodiment of the present invention;

[0055] Figure 3 Thermographic diagram of internal friction angle distribution in an embodiment of the present invention;

[0056] Figure 4 Thermographic diagram of cohesion distribution in an embodiment of the present invention;

[0057] Figure 5 Heat map of bulk density distribution according to an embodiment of the present invention;

[0058] Figure 6 Slip surface probability diagram of an embodiment of the present invention;

[0059] Figure 7 Binary mask image of the stepped slope region in this embodiment of the invention;

[0060] Figure 8 A binary mask image of the flat disk region in an embodiment of the present invention. Detailed Implementation

[0061] The specific implementation methods of this application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0062] Example 1:

[0063] This embodiment proposes a method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes, such as... Figure 1 As shown, it includes:

[0064] Step S1: Divide the slope into multiple closed polygons according to the lithology of the strata, and assign different layers to each polygon and the critical slip surface of the slope;

[0065] In this embodiment, this step is existing technology and will not be described again.

[0066] Step S2: In each layer, obtain the holographic features of the open-pit mine slope. The holographic features of the open-pit mine slope include: the slope's geometric shape, stratigraphic lithology, and geotechnical parameters. The geotechnical parameters include: cohesion, internal friction angle, and unit weight.

[0067] In this embodiment, as Figure 2 As shown, the overall slope angle is 19° and the slope height is 111m. The physical and mechanical parameters of each soil and rock layer (Quaternary, Tertiary, and mudstone-sandstone interbedded) are shown in Table 1.

[0068] Table 1. Physical and mechanical parameters;

[0069] ;

[0070] Step S3: Based on the slope's geometry, lithology, and geotechnical parameters, slope stability analysis software is used to analyze the slope and determine the morphological parameters and stability coefficient of the critical slip surface.

[0071] In this embodiment, based on the slope geometry, lithology, and soil-rock mechanical parameters obtained in S2, stability analysis is performed in slope stability analysis software to obtain the morphological parameters of the critical slip surface. Figure 2 And calculate its corresponding minimum stability coefficient F. s =2.127.

[0072] Step S4: Based on the preset range of cohesion, internal friction angle and bulk density, linearly map the different colors in each layer to establish a parameter mapping model for color encoding.

[0073] In this embodiment, a parameter mapping model based on color coding is established. Through linear mapping, the red (R), green (G), and blue (B) components of the formation layer color are respectively associated with the preset numerical ranges of cohesion c, internal friction angle φ, and unit weight γ.

[0074] The parameter mapping model for the color encoding is calculated as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] Where R is the red component in the color-coded parametric mapping model, G is the green component in the color-coded parametric mapping model, and B is the blue component in the color-coded parametric mapping model. It is the internal friction angle. For cohesion, For density, the values ​​of its channel components (R, G, B) range from 0 to 255 integers.

[0079] Step S5: Based on the color-coded parameter mapping model, geotechnical parameters, and the stability coefficient of the critical slip surface, assign corresponding color attributes to each layer and generate a parsable slope digitization file. The slope digitization file includes: slope geometry, stratum lithology, color coding of geotechnical parameters, morphological parameters of the critical slip surface, and color coding of the stability coefficient.

[0080] The parameter mapping model based on color encoding, geotechnical parameters, and the stability coefficient of the critical slip surface assigns a corresponding color attribute to each layer, calculated as follows:

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] in, This is the minimum stability coefficient of the slope. The red component of the critical slip surface layer. The green component of the critical slip surface layer. The blue component of the critical slip surface layer. For the red component of the i-th lithology layer, For the green component of the i-th lithology layer, For the blue component of the i-th lithology layer, Let i be the internal friction angle of the i-th lithology. For the cohesion of the i-th lithology, Let be the unit weight of the i-th lithology, where each lithology has a corresponding layer.

[0086] In this embodiment, based on the determined slope rock and soil mechanical parameters and slope stability coefficient, and using a parameter mapping model, the color codes of each rock and soil layer (Quaternary, Tertiary, and mudstone-sandstone intergroup) and the critical slip surface layer are determined as shown in Table 2.

[0087] Table 2 Color Coding;

[0088] ;

[0089] Step S6: Extract the slope geometry from the parseable slope digitization file, and calculate the bounding box of the slope region based on the slope geometry.

[0090] In this embodiment, based on the slope's geometric shape, stratigraphic lithology, critical slip surface, and layer color encoding information determined in S1~S5, a parsable digital slope file is generated and saved in a DXF format file. A Python program calls the ezdxf library to extract the slope's geometric shape from the DXF file, including features such as the slope outline, lithological filling area, and critical slip surface, and calculates the bounding box of the slope region, including:

[0091] Let the original slope geological model be a vector set D. dxfThe lithological polygon set E={e1, e2, ..., e} is extracted by parsing the ezdxf interface of the Python program. n}. Each polygon e i Includes geometric coordinate data G i With layer property L i .

[0092] In this embodiment, the original slope geological model is assumed to be a vector set D. dxf The lithological polygon set E={e1, e2, ..., e} is extracted by parsing the ezdxf interface of the Python program. n}. Each polygon e i Includes geometric coordinate data G i With layer property L i .

[0093] Geometric bounding box B calculation:

[0094] Traverse the vertex set O = {o1, o2, ..., o} of all lithological polygons E m The axis-aligned bounding box B of the slope region is calculated to constrain the spatial extent of the slope geometry. The calculation formula is as follows:

[0095] ;

[0096] in, This is the bounding box for the slope area, where... The x-axis coordinate is the minimum value in the polygon vertex coordinate system of the slope. The polygon vertex coordinate system takes the lowest point on one side of the profile as the origin, defines the horizontal direction as the x-axis, and the vertical direction as the y-axis. This represents the minimum y-axis coordinate in the coordinate system of all polygon vertices on the slope. This represents the maximum y-axis coordinate in the coordinate system of all polygon vertices on the slope. The maximum x-axis coordinate in the coordinate system of all polygon vertices of the slope. =min(v x ), =max(v y ), =min(v y ), =max(v x ), v x For any point on the x-axis, v y Let be any point on the y-axis.

[0097] Step S7: Mesh the bounding box of the slope area and convert the data in the meshed bounding box into fixed-size 3D mesh data;

[0098] The process of meshing the bounding box in the slope area, and converting the data in the meshed bounding box into fixed-size three-dimensional mesh data, includes:

[0099] Step S7.1: In the bounding box of the slope region, establish a linear mapping from the continuous physical coordinate system to the discrete pixel coordinate system;

[0100] Step S7.2: Convert the set of polygons in the bounding box of the slope region into a slope physical domain, wherein the slope physical domain is the Boolean union of all polygons;

[0101] Step S7.3: In the discrete pixel coordinate system, the slope physical domain is rasterized using the scan line fill algorithm to generate a slope region mask;

[0102] In this embodiment, the continuous vector geometric space D is... dxf Discretize into a fixed-size structured mesh tensor X∈R H×W×C Where H and W are the height and width of the grid, and C is the number of feature channels. The construction process follows the following transformation logic.

[0103] Coordinate space transformation: Establish a linear mapping M from the continuous physical coordinate system (x, y) to the discrete pixel coordinate system (u, v) to maintain the aspect ratio of the slope geometry.

[0104] ;

[0105] Construction of mask channels in slope areas: To accurately define the topological extent of slope entities, a binary mask channel M is constructed. slope ∈{0,1} H×W , Let H be the total number of nodes or pixels in the structured mesh in the horizontal direction, and let H be the total number of nodes or pixels in the structured mesh in the vertical direction. Suppose the slope model contains a set of lithological polygons P = {P1, P2, …, P}. n}, slope physical domain Ω slope Defined as the Boolean union of all polygons:

[0106] ;

[0107] In the discrete pixel coordinate system, the slope physical domain is rasterized using a scanline fill algorithm to generate a slope region mask. The calculation formula is as follows:

[0108] ;

[0109] in, In discrete pixel coordinate system The lower slope area is covered by a mask. This refers to the physical domain of the slope.

[0110] In this embodiment, the channel serves as the geometric reference for subsequent physical assignment and contour extraction in S8.

[0111] Step S7.4: Based on the slope area mask, map the geotechnical parameters to the corresponding feature channels;

[0112] In this embodiment, based on the slope area mask M slope This maps lithological parameters to corresponding feature channels. For M slope For regions where (u, v)=1, values ​​are assigned based on the properties of their respective lithological layers k, and the calculation formula is as follows:

[0113] exist In the region where =1, ;

[0114] in, It is the internal friction angle. For cohesion, For density In the discrete pixel coordinate system The tensor element value at the ch-th feature channel. This is a scalar of lithological physical properties obtained based on RGB color space mapping. This is a feature channel index, containing cohesion. internal friction angle , bulk density .

[0115] Step S7.5: Use the Bresenham algorithm to rasterize the curve of the critical slip surface, calculate the best approximation value of the path point set of the critical slip surface in the three-dimensional grid, and generate the critical slip surface probability based on the best approximation value.

[0116] In this embodiment, the critical slip surface probability field discretization is performed using the Bresenham algorithm on the vector slip surface curve S. c Rasterize the data. Calculate the slip surface path point set L. slip The best approximation on the grid generates probability channels P. slip :

[0117] ;

[0118] in, This represents the critical slip surface probability.

[0119] Step S7.6: The row coordinates of the discrete pixel coordinate system, the column coordinates of the discrete pixel coordinate system, and the feature channels form a three-dimensional grid data of fixed size. The feature channels include: geotechnical parameters, slope area mask, and critical slip surface probability.

[0120] Step S8: Based on the fixed-size three-dimensional mesh data, use the contour extraction algorithm to identify the outer contour line of the slope, which includes a discrete surface point set;

[0121] Step S9: Calculate the local tilt angle of each contour segment based on the coordinates of adjacent points in the discrete surface point set;

[0122] In this embodiment, the slope area mask channel M generated based on S7 slope Morphological analysis was used to extract the "step-flat" features of open-pit mine slopes, enabling semantic segmentation of the slope surface.

[0123] Slope surface contour extraction: First, extract the top envelope of the slope. For each column u of the grid, define the slope surface height function h(u) as the ordinate of the first non-zero lithology pixel in that column.

[0124] Based on the coordinates of adjacent points in the discrete surface point set, the local inclination angle of each contour segment is calculated using the following formula:

[0125] ;

[0126] ;

[0127] ;

[0128] in, In discrete pixel coordinate system The lower slope area is covered by a mask. It is a function of the slope surface height. For a discrete surface point set, Let (u) be the local tilt angle of the i-th contour segment. i ,v i ) is the first adjacent point p i Discrete pixel coordinates, (u i+1 ,v i+1 ) is the second adjacent point p i+1 Discrete pixel coordinates For surface node pairs, represent a specific mesh coordinate point. This represents the total number of nodes or pixels in the horizontal direction of the structured mesh.

[0129] Step S10: Along the outer contour line of the slope, perform semantic segmentation based on the local dip angle of each contour segment. Mark the line segment area with a local dip angle less than or equal to a preset threshold as "horizontal plate" and the line segment area with a local dip angle greater than the preset threshold as "stepped slope".

[0130] The outer contour line along the slope is semantically segmented based on the local dip angle of each contour segment, including:

[0131] When the local tilt angle of the i-th contour segment is less than the first preset threshold, and the length of pixels that continuously satisfy this condition is greater than or equal to the second preset threshold, the current region is marked as "horizontal flat".

[0132] When the local tilt angle is greater than or equal to the first preset threshold, the current area is marked as a "stepped slope".

[0133] In this embodiment, image processing and multi-channel data fusion methods are used to draw heat maps of the internal friction angle distribution (e.g., Figure 3 (as shown), cohesion distribution heatmap (such as) Figure 4 As shown), a heat map of bulk density distribution (as shown) Figure 5 As shown), the probability diagram of the slip surface (as shown) Figure 6 As shown), and the stepped slope area (as shown) Figure 7 (as shown) and the flat area (as shown) Figure 8 The binary mask image shown is shown below.

[0134] In this embodiment, the three-dimensional mesh data volume, slope area mask, stepped slope mask, and horizontal flat disk mask are stored in compressed binary format to form a standardized structured dataset that can be directly used by machine learning models.

[0135] In summary, the intelligent extraction and structured conversion method for holographic features of open-pit mine slopes provided in this embodiment establishes a parameter mapping model based on color encoding. This model maps the RGB color components of the lithological stratigraphic layer to the internal friction angle, cohesion, and unit weight, respectively, and maps the color of the critical slip surface layer to the corresponding stability coefficient. Using a Python script, geometric information such as slope contours, lithological zones, and critical slip surfaces is intelligently extracted from DXF format files and converted into three-dimensional mesh data. The feature channels of the mesh data include the internal friction angle, cohesion, unit weight, slope area mask, and the probability distribution of the critical slip surface generated by rasterization based on the Bresenham algorithm. Image processing and multi-channel data fusion methods are employed to generate heatmaps, probability maps, and binary mask maps, which are stored in compressed binary format, providing a standardized and structured data foundation for intelligent slope stability evaluation.

[0136] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art to meet different specific practical needs. However, it will be obvious to those skilled in the art that these specific details are not necessary to implement this invention. In other instances, to avoid obscuring this invention, well-known components, structures, or parts are not specifically described, and all are within the scope of protection defined by the technical solutions of this invention.

[0137] Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this invention should be within the protection scope of this invention. In the above description, numerous specific details have been set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to implement the invention. In other instances, to avoid obscuring the invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0138] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0139] Example 2:

[0140] This embodiment proposes an electronic device, including: one or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the aforementioned method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes.

[0141] The electronic device can be a mobile phone, computer, or tablet computer, etc., and includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements a method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes as described in the embodiments. It is understood that the electronic device may also include an input / output (I / O) interface and communication components.

[0142] The processor is used to execute all or part of the steps in the intelligent extraction and structured conversion method for holographic features of open-pit mine slopes as described in the above embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in an electronic device, as well as application-related data.

[0143] The processor can be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes described in the above embodiments.

[0144] Example 3:

[0145] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0146] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the intelligent extraction and structured conversion method for holographic features of open-pit mine slopes as described in the various embodiments of this application.

[0147] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory), random access memory (RAM), static random-access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes. These media store computer programs, which, when executed by a processor, can implement the various steps of the aforementioned method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes.

[0148] Example 4:

[0149] This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned method for intelligent extraction and structured conversion of holographic features of open-pit mine slopes.

[0150] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0151] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0152] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of equivalent technology of this disclosure, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A method for intelligent extraction and structured conversion of holographic features of an open-pit mine slope, characterized in that, include: The slope is divided into multiple closed polygons based on the lithology of the strata, and different layers are assigned to each polygon and the critical slip surface of the slope. In each layer, the holographic features of the open-pit mine slope are obtained. The holographic features of the open-pit mine slope include: the slope geometry, stratigraphic lithology, and geotechnical parameters, wherein the geotechnical parameters include: cohesion, internal friction angle, and unit weight. Based on the slope's geometry, lithology, and geotechnical parameters, slope stability analysis software was used to analyze the slope and obtain the morphological parameters of the critical slip surface and the minimum stability coefficient of the slope. Based on the preset ranges of cohesion, internal friction angle, and bulk density, a parameter mapping model for color coding is established by linearly mapping different colors in each layer. Based on the color-coded parameter mapping model, geotechnical parameters, and the minimum stability coefficient of the slope, each layer is assigned a corresponding color attribute, and a parsable digital slope file is generated. The digital slope file includes: the slope geometry, strata lithology, color coding of geotechnical parameters, morphological parameters of the critical slip surface, and color coding of the minimum stability coefficient of the slope. Extract the slope geometry from the parseable digitized slope file and calculate the bounding box of the slope region based on the slope geometry. Mesh the bounding box in the slope area, and then convert the data in the meshed bounding box into fixed-size three-dimensional mesh data; Based on three-dimensional mesh data of fixed size, a contour extraction algorithm is used to identify the external contour line of the slope, which includes a discrete set of surface points. Calculate the local tilt angle of each contour segment based on the coordinates of adjacent points in the discrete surface point set; Along the outer contour line of the slope, semantic segmentation is performed based on the local dip angle of each contour segment. Line segments with a local dip angle less than or equal to a preset threshold are marked as "horizontal flat plate", and line segments with a local dip angle greater than the preset threshold are marked as "stepped slope". The parameter mapping model for the color encoding is calculated as follows: ； ； ； Where R is the red component in the color-coded parametric mapping model, G is the green component in the color-coded parametric mapping model, and B is the blue component in the color-coded parametric mapping model. It is the internal friction angle. For cohesion, For density; The parameter mapping model based on color encoding, geotechnical parameters, and the minimum stability coefficient of the slope assigns a corresponding color attribute to each layer, calculated as follows: ； ； ； ； in, This is the minimum stability coefficient of the slope. The red component of the critical slip surface layer. The green component of the critical slip surface layer. The blue component of the critical slip surface layer. For the red component of the i-th lithology layer, For the green component of the i-th lithology layer, The blue component of the i-th lithology layer. Let i be the internal friction angle of the i-th lithology. For the cohesion of the i-th lithology, Let be the unit weight of the i-th lithology.

2. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 1, characterized in that, Based on the geometry of the slope, the bounding box of the slope region is calculated using the following formula: ； in, This is the bounding box for the slope area, where... The x-axis coordinate is the minimum value in the polygon vertex coordinate system of the slope. The polygon vertex coordinate system takes the lowest point on one side of the slope profile as the origin, defines the horizontal direction as the x-axis, and the vertical direction as the y-axis. This represents the minimum y-axis coordinate in the coordinate system of all polygon vertices on the slope. This represents the maximum y-axis coordinate in the coordinate system of all polygon vertices on the slope. It represents the maximum x-axis coordinate in the coordinate system of all polygon vertices of the slope.

3. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 1, characterized in that, The process of meshing the bounding box in the slope area, and converting the data in the meshed bounding box into fixed-size three-dimensional mesh data, includes: Within the bounding box of the slope region, establish a linear mapping from the continuous physical coordinate system to the discrete pixel coordinate system; The set of polygons in the bounding box of the slope region is converted into a slope physical domain, which is the Boolean union of all polygons; In the discrete pixel coordinate system, the slope physical domain is rasterized using the scan line fill algorithm to generate a slope region mask. Based on the slope area mask, the geotechnical parameters are mapped to the corresponding feature channels; The Bresenham algorithm is used to rasterize the curve of the critical slip surface, calculate the best approximation value of the path point set of the critical slip surface in the three-dimensional grid, and generate the critical slip surface probability based on the best approximation value. The row coordinates of the discrete pixel coordinate system, the column coordinates of the discrete pixel coordinate system, and the feature channels constitute a three-dimensional grid data of fixed size. The feature channels include: geotechnical parameters, slope area mask, and critical slip surface probability.

4. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 3, characterized in that, In the discrete pixel coordinate system, the slope physical domain is rasterized using a scanline fill algorithm to generate a slope region mask. The calculation formula is as follows: ； in, In discrete pixel coordinate system The lower slope area is covered by a mask. This refers to the physical domain of the slope.

5. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 3, characterized in that, The geotechnical parameters are mapped to the corresponding feature channels based on the slope area mask, and the calculation formula is as follows: exist In the region where =1, ; in, It is the internal friction angle. For cohesion, For density, In the discrete pixel coordinate system The tensor element value at the ch-th feature channel. This is a scalar of lithological physical properties obtained based on RGB color space mapping. This is the feature channel index.

6. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 1, characterized in that, The local inclination angle of each contour segment is calculated based on the coordinates of adjacent points in the discrete surface point set, using the following formula: ； ； ； in, In discrete pixel coordinate system The lower slope area is covered by a mask. It is a function of the slope surface height. For a discrete surface point set, Let (u) be the local tilt angle of the i-th contour segment. i ,v i ) is the first adjacent point p i Discrete pixel coordinates, (u i+1 ,v i+1 ) is the second adjacent point p i+1 Discrete pixel coordinates For surface node pairs, represent a specific mesh coordinate point. This represents the total number of nodes or pixels in the horizontal direction of the structured mesh.

7. The method for intelligent extraction and structured transformation of holographic features of open-pit mine slopes according to claim 1, characterized in that, The outer contour line along the slope is semantically segmented based on the local dip angle of each contour segment, and the calculation formula is as follows: When the local tilt angle of the i-th contour segment is less than the first preset threshold, and the length of pixels that continuously satisfy this condition is greater than or equal to the second preset threshold, the current region is marked as "horizontal flat". When the local tilt angle is greater than or equal to the first preset threshold, the current area is marked as a "stepped slope".

8. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the intelligent extraction and structured conversion method for holographic features of open-pit mine slopes as described in any one of claims 1 to 7.

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