Mine three-dimensional visualization method and system

Abstract

The invention relates to the technical field of mine visualization, in particular to a three-dimensional visualization method and system for a mine. The method comprises the following steps: establishing a three-dimensional terrain model of a mine; designing exploration planning space distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operation through the exploration planning space distribution parameters; mine point rock stratum lithology data are collected according to exploration drilling operation; mapping the mine point location rock stratum lithology data to the mine three-dimensional terrain model to obtain optimized mine point location rock stratum lithology mapping data; analyzing mine rock stratum boundary structure data and rock stratum lithology fusion characteristic data according to the optimized mine point location rock stratum lithology mapping data, and performing mine rock stratum lithology structure analysis to generate mine rock stratum lithology structure data; and based on the lithologic structure data of the mine rock stratum, lithologic fusion differentiation rendering three-dimensional modeling of the mine rock stratum is carried out, and a three-dimensional visual rendering model of the mine rock stratum is generated. According to the invention, accurate visual analysis of the internal structure of the mine is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of mine visualization, and particularly to a three-dimensional mine visualization method and system. Background Art

[0002] In the process of traditional mine resource exploration and development, the acquisition, analysis, and expression of mine geological information mainly rely on two-dimensional drawings and text materials, which have problems such as limited information carrying capacity, difficult spatial understanding, and poor dynamic update ability. With the increasing tension of mineral resources and the complexity of the mining environment, traditional methods are difficult to meet the requirements of fine, three-dimensional, and real-time expression of ore body structures, seriously restricting the efficiency and accuracy of mine exploration, design, and safety management work. However, existing mine three-dimensional visualization introduces geographic information system (GIS), remote sensing (RS) technology, and computer-aided design (CAD) tools to digitally model and manage mining areas, but these technical means still have significant deficiencies in data fusion, spatial matching accuracy, lithological structure modeling, and three-dimensional visualization expression. Especially in the expression of the relationship between rock layer lithological characteristics and spatial distribution, there is a lack of a three-dimensional visualization method that can integrate remote sensing monitoring, geological exploration, and spatial modeling, making it difficult to achieve an intuitive understanding and accurate analysis of mine geological structures and lithological distributions. Summary of the Invention

[0003] Based on this, the present invention provides a three-dimensional mine visualization method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional mine visualization method includes the following steps: Step S1: Use monitoring equipment to collect multi-source mine monitoring data of a mine area; construct a three-dimensional model for multi-source mine monitoring based on the multi-source mine monitoring data to obtain a three-dimensional mine terrain model; Step S2: Design the spatial distribution parameters of an exploration plan based on the three-dimensional mine terrain model, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; obtain the spatial data of corresponding exploration drilling points according to the exploration drilling operations; perform analysis on the rock layer lithology of mine points based on the spatial data of exploration drilling points to generate rock layer lithology data of mine points; Step S3: Map the rock layer lithology data of mine points to the three-dimensional mine terrain model to obtain mapped rock layer lithology data of mine points; perform optimization processing on the mapping spatial accuracy of the mapped rock layer lithology data of mine points to generate optimized mapped rock layer lithology data of mine points; Step S4: Conduct a mining strata lithology blending orientation analysis based on the optimized mapping data of the strata lithology at the mining points to generate mining strata lithology blending orientation data; conduct a mining strata boundary structure analysis through the optimized mapping data of the strata lithology at the mining points to generate mining strata boundary structure data; conduct a strata lithology blending feature analysis on the mining strata boundary structure data according to the mining strata lithology blending orientation data to generate strata lithology blending feature data; conduct a mining strata lithology structure analysis based on the strata lithology blending feature data and the mining strata boundary structure data to generate mining strata lithology structure data; Step S5: Conduct a three-dimensional modeling process of lithology blending differentiation rendering for the mining strata based on the mining strata lithology structure data to generate a three-dimensional visualization rendering model of the mining strata.

[0005] Furthermore, the monitoring equipment described in Step S1 includes satellite equipment, UAV aerial survey equipment, laser scanning equipment, and sensor monitoring equipment. Step S1 includes the following steps: Step S11: Use satellite equipment to collect mine hydrographic remote sensing data for the mining area to generate mine hydrographic remote sensing data; Step S12: Use UAV aerial survey equipment to collect mine surface image data for the mining area to generate mine surface image data; Step S13: Use laser scanning equipment to collect mine laser data for the mining area to generate mine laser data; Step S14: Use sensor monitoring equipment to collect mine sensor monitoring data for the mining area to generate mine sensor monitoring data; Step S15: Mark the mine hydrographic remote sensing data, mine surface image data, mine laser data, and mine sensor monitoring data as preliminary mine multi-source monitoring data; conduct data preprocessing on the preliminary mine multi-source monitoring data to obtain mine multi-source monitoring data; Step S16: Conduct a mining monitoring spatial matching process on the mine multi-source monitoring data to generate mining monitoring spatial matching data; Step S17: Conduct a matching and integration process on the mine multi-source monitoring data through the mining monitoring spatial matching data to generate mine multi-source monitoring fusion data; Step S18: Establish a mine surface triangular grid based on the mine multi-source monitoring fusion data, and conduct elevation interpolation processing on the triangular vertices of the mine surface triangular grid to obtain mine surface interpolation data; conduct mine terrain surface reconstruction processing according to the mine surface interpolation data to obtain mine terrain surface modeling data; Step S19: Conduct mine terrain surface optimization processing on the mine terrain surface modeling data to establish a mine three-dimensional terrain model.

[0006] Further, step S2 includes the following steps: Step S21: Analyze the complex terrain features of the mine based on the 3D mine terrain model to generate complex terrain feature data of the mine; Step S22: Design the spatial distribution parameters of the exploration plan according to the complex terrain feature data of the mine, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; Step S23: Obtain the corresponding spatial data of the exploration drilling points according to the exploration drilling operations; Step S24: Collect the logging data and geophysical exploration data of the exploration drilling points respectively through the spatial data of the exploration drilling points to obtain the logging data and geophysical exploration data of the exploration drilling points; Step S25: Analyze the spatial distribution of the exploration drilling rock formations based on the spatial data of the exploration drilling points to generate spatial distribution data of the exploration drilling rock formations; Step S26: Use the logging data, geophysical exploration data and spatial distribution data of the exploration drilling rock formations of the exploration drilling points to analyze the lithology of the rock formations at the mine points, and generate lithology data of the rock formations at the mine points.

[0007] Further, step S3 includes the following steps: Step S31: Map the spatial data of the exploration drilling points to the 3D mine terrain model for mapping conversion of the exploration drilling points to generate mapping data of the exploration drilling points; Step S32: Perform processing on the mapping data of the exploration drilling points for conversion of the planar projection points of the exploration drilling to generate conversion data of the planar projection points of the exploration drilling; Step S33: Perform adjustment calculation with elevation constraints on the exploration drilling according to the conversion data of the planar projection points of the exploration drilling to generate adjustment data with elevation constraints of the exploration drilling; Step S34: Perform spatial calibration processing on the spatial data of the exploration drilling points according to the adjustment data with elevation constraints of the exploration drilling to generate calibrated spatial data of the exploration drilling points; Step S35: Transmit the lithology data of the rock formations at the mine points to the 3D mine terrain model for lithology mapping processing of the rock formations at the mine points to generate mapping data of the lithology of the rock formations at the mine points, and perform mapping space accuracy optimization processing on the mapping data of the lithology of the rock formations at the mine points through the calibrated spatial data of the exploration drilling points to generate optimized mapping data of the lithology of the rock formations at the mine points.

[0008] Further, step S4 includes the following steps: Step S41: Perform discretization processing on the lithology of the rock formations at the mine points according to the optimized mapping data of the lithology of the rock formations at the mine points to generate discretized data of the lithology of the rock formations at the mine points; Step S42: Conduct analysis on the heterogeneous lithology characteristics of the rock strata based on the discretized data of the rock strata lithology at the mine points to generate heterogeneous rock strata lithology characteristic data; Step S43: Conduct analysis on the fusion orientation of the mine rock strata lithology based on the heterogeneous rock strata lithology characteristic data to generate mine rock strata lithology fusion orientation data; Step S44: Extract the heterogeneous rock strata lithology differential nodes within the mine points according to the heterogeneous rock strata lithology characteristic data to obtain heterogeneous rock strata lithology differential node data. Conduct fitting processing on the heterogeneous rock strata morphology according to the heterogeneous rock strata lithology differential node data to generate heterogeneous rock strata morphology fitting data; Conduct extraction processing on the mine rock strata boundary structure for the discretized data of the mine point rock strata lithology to generate mine rock strata boundary structure data; Step S45: Conduct analysis on the fusion characteristics of the rock strata lithology based on the mine rock strata lithology fusion orientation data to generate rock strata lithology fusion characteristic data; Step S46: Conduct analysis on the mine rock strata lithology structure based on the rock strata lithology fusion characteristic data and the mine rock strata boundary structure data to generate mine rock strata lithology structure data.

[0009] Furthermore, Step S45 includes the following steps: Step S451: Conduct analysis on the specificity of the lithology fusion transition according to the heterogeneous rock strata lithology characteristic data to generate lithology fusion transition specificity data; Step S452: Establish a mapping relationship of the rock strata lithology fusion orientation through the discretized data of the mine point rock strata lithology and the lithology fusion transition specificity data to generate a rock strata lithology fusion orientation model; Step S453: Conduct lithology orientation analysis on each discretized data of the discretized data of the mine point rock strata lithology to generate discretized lithology orientation data; Conduct analysis on the lithology orientation gradient characteristics according to the discretized lithology orientation data to generate lithology orientation gradient characteristic data; Step S454: Transmit the discretized data of the mine point rock strata lithology and the lithology orientation gradient characteristic data to the rock strata lithology fusion orientation model for conducting analysis on the mine rock strata lithology fusion orientation to generate mine rock strata lithology fusion orientation data.

[0010] Furthermore, Step S452 includes the following steps: Conduct analysis on the gradual change fusion transition coefficient and the mutation fusion transition coefficient of the lithology characteristics according to the lithology fusion transition specificity data to respectively obtain the gradual change fusion transition coefficient of the lithology characteristics and the mutation fusion transition coefficient of the lithology characteristics; Calculate the difference in adjacent lithology physical parameters according to the discretized data of the mine point rock strata lithology; An isomeric lithology transition and blending tree structure is established based on the difference in adjacent lithophysical parameters, and the blending transition intensity of lithological characteristics is analyzed according to the gradual blending transition coefficient and the sudden blending transition coefficient of lithological characteristics to generate the blending transition intensity data of lithological characteristics. Through the isomeric lithology transition and blending tree structure and the blending transition intensity data of lithological characteristics, the mapping relationship analysis of the lithology blending orientation of the rock stratum is carried out to establish a lithology blending orientation model of the rock stratum.

[0011] Further, the mine rock stratum lithology structure data described in step S46 includes the mine surface soil layer structure, the mine ore body layer structure, the mine semi-weathered layer structure, the mine weathered ore-free zone structure, the mine bedrock layer structure, and the mine groundwater structure.

[0012] Further, step S5 includes the following steps: Perform preliminary layered visualization 3D modeling processing of the mine rock stratum using the mine rock stratum boundary structure data to generate a mine rock stratum visualization 3D model; Perform lithology blending differential rendering processing on the mine rock stratum visualization 3D model according to the lithology blending characteristic data to generate a mine rock stratum 3D visualization rendering model.

[0013] This specification provides a mine 3D visualization system for executing the mine 3D visualization method as described above. The mine 3D visualization system includes: A mine 3D terrain construction module for collecting multi-source monitoring data of the mine area using monitoring equipment; constructing a 3D model of multi-source monitoring of the mine based on the multi-source monitoring data of the mine to obtain a mine 3D terrain model; A mine exploration operation module for designing exploration planning spatial distribution parameters based on the mine 3D terrain model and performing exploration drilling operations through the exploration planning spatial distribution parameters; obtaining corresponding exploration drilling point spatial data according to the exploration drilling operations; performing mine point rock stratum lithology analysis based on the exploration drilling point spatial data to generate mine point rock stratum lithology data; A mine point rock stratum analysis module for mapping the mine point rock stratum lithology data to the mine 3D terrain model to obtain mine point rock stratum lithology mapping data; performing mapping space accuracy optimization processing on the mine point rock stratum lithology mapping data to generate optimized mine point rock stratum lithology mapping data; The mine rock stratum lithology structure analysis module is used to conduct the mine rock stratum lithology blending orientation analysis based on the optimized mine point position rock stratum lithology mapping data to generate the mine rock stratum lithology blending orientation data; conduct the mine rock stratum boundary structure analysis through the optimized mine point position rock stratum lithology mapping data to generate the mine rock stratum boundary structure data; conduct the lithology blending feature analysis of the mine rock stratum boundary structure data according to the mine rock stratum lithology blending orientation data to generate the lithology blending feature data; conduct the mine rock stratum lithology structure analysis based on the lithology blending feature data and the mine rock stratum boundary structure data to generate the mine rock stratum lithology structure data; The mine three-dimensional visualization module is used to conduct the three-dimensional modeling processing of the lithology blending differentiation rendering of the mine rock stratum based on the mine rock stratum lithology structure data to generate the mine rock stratum three-dimensional visualization rendering model.

[0014] The beneficial effects of this application are as follows. Through the method of multi-source monitoring data collection and three-dimensional model construction for mines, the present invention can effectively integrate various monitoring means such as satellite remote sensing, UAV aerial survey, and laser scanning, comprehensively obtaining hydrological, surface, and spatial structure data of the mine area. It not only improves the coverage and accuracy of data collection, but also realizes the efficient integration of multi-source heterogeneous data through spatial matching and fusion processing, and can accurately construct a three-dimensional terrain model of the mine, providing refined and three-dimensional basic data support for subsequent exploration planning and geological analysis, significantly enhancing the accuracy and visualization ability of mine spatial information expression. Based on the terrain feature analysis of the mine three-dimensional terrain model, the spatial distribution parameters of exploration boreholes are scientifically designed to ensure the pertinence and efficiency of exploration operations. At the same time, combined with well logging data, geophysical exploration data, and the analysis results of the spatial distribution of rock formations at the borehole points, the accurate identification and multi-dimensional analysis of the rock formation structure and lithological characteristics of the mine are realized. It can effectively reflect the spatial variation law of underground geological structures, improve the accuracy and reliability of geological exploration, and provide a solid data foundation and decision-making support for the fine exploration and rational development of mineral resources. The lithological data of rock formations at mine points are accurately mapped to the mine three-dimensional terrain model to realize the spatial integrated expression of underground lithological information and the surface terrain model, enhancing the integrity and realism of the mine three-dimensional visualization model. This mapping process can not only intuitively present complex borehole geological information in three-dimensional space, but also provide more vivid and intuitive data support for subsequent ore body modeling, resource volume assessment, and mining design. Through the high-precision spatial mapping and spatial coordinate adjustment and optimization of exploration borehole points, the spatial registration accuracy and vertical elevation consistency of the mapped data are improved. Especially in the process of elevation constraint adjustment and spatial calibration, the borehole point errors caused by factors such as acquisition errors and terrain undulations are effectively eliminated, so that in the finally generated optimized lithological mapping data of mine points, a high degree of fit between underground lithological data and the terrain model is ensured, enhancing the spatial accuracy and analysis reliability of the three-dimensional visualization system. Further analysis of the optimized lithological mapping data of mine points is carried out to extract the lithological transition characteristics and boundary structure information between rock formations, significantly enhancing the structural authenticity and spatial analysis ability of the three-dimensional model in lithological expression. It not only realizes the extension analysis from point data to the overall structure, but also improves the accuracy and systematicness of mine geological modeling in lithological identification, structure division, and heterogeneous performance through multi-dimensional extraction and synthesis of lithological blending characteristics and lithological boundaries. Through methods such as lithological discretization, heterogeneous feature analysis, and blending-oriented modeling, a rock formation analysis process from micro differences to macro structures is synergistically established.The quantitative analysis process based on lithology-guided gradient and lithology blending intensity not only reveals the transitional relationship between gradual and abrupt lithology changes but also expresses the logical topological relationship between lithology structures through a tree-like structure model, providing a theoretical basis and algorithm support for subsequent optimization of 3D geological models, intelligent stratification, ore body identification, and automatic boundary tracking. Overall, it significantly improves the application value and intelligent level of the mine 3D visualization model in geological interpretation and decision-making support. Effectively converting the lithology structure data of mine rock strata into a 3D visualization model with visual expression ability significantly enhances the intuitive presentation effect and spatial analysis ability of geological information. During the 3D modeling process, preliminary stratified modeling is achieved by leveraging the rock stratum boundary structure data, effectively dividing the structural hierarchical relationship between rock strata, enabling the model to have a clear geological zoning expression, and providing a real and accurate structural basis for geological exploration, ore body identification, and engineering decision-making. Implementing differential rendering based on lithology blending feature data can accurately reflect the gradual and abrupt characteristics between different lithologies, thereby realizing the visual differential expression of mine rock strata. This rendering method not only enhances the resolution and detail depth of the model in representing heterogeneous geological structures but also effectively improves the practicality and intelligent level of the mine 3D visualization system in multiple scenarios such as geological interpretation, intelligent exploration decision-making, and visualization demonstration.

[0015] Therefore, the mine 3D visualization method of the present invention can effectively obtain and express the spatial distribution and physical properties of rock strata by constructing a lithology analysis mechanism based on the spatial data of borehole positions and integrating logging and geophysical exploration data. Especially through lithology blending-guided modeling, lithology differential discretization, and lithology transition structure analysis, it has strong advantages in the associated modeling of lithology characteristics and spatial structures, solving the problem that existing methods are difficult to accurately express the complex structures at the boundaries and transition zones of rock strata. Through multi-level 3D modeling and rendering, a 3D visualization model of mine rock strata that can feedback internal information of the mine is constructed. This model can not only intuitively display the detailed information of lithology content in the mine space, achieving a clearer and more realistic display of mine geological structures, but also greatly improve the 3D visualization effect and the ability to understand geological structures. Brief Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of the steps of a mine 3D visualization method of the present invention; Figure 2 is Figure 1 a detailed implementation step flow chart of step S4 in Figure 3 is Figure 2 a detailed implementation step flow chart of step S45 in The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with embodiments with reference to the drawings. Detailed Embodiments

[0017] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a three-dimensional visualization method for mines. In the embodiments of the present invention, please refer to Figure 1 shown in the figure, which is a schematic flowchart of the steps of a three-dimensional visualization method for mines according to the present invention. The three-dimensional visualization method for mines includes the following steps: To achieve the above object, a three-dimensional visualization method for mines includes the following steps: Step S1: Use monitoring devices to collect multi-source monitoring data of the mine area; construct a three-dimensional model for multi-source monitoring of the mine based on the multi-source monitoring data of the mine to obtain a three-dimensional terrain model of the mine; In the embodiments of the present invention, according to the specificities of various types of monitoring devices, such as satellite devices, unmanned aerial vehicle (UAV) aerial survey devices, laser scanning devices, and sensor monitoring devices, systematic management of the entire life cycle of calibration, maintenance, and usage monitoring is carried out, thereby ensuring quality control and safe operation during the use of the devices and ensuring the accuracy and reliability of measurement data. In a rare earth mine area (with an area of approximately 5 km² and mainly mountainous terrain), satellite devices are used to collect hydrographic remote sensing data of the mine to identify the distribution of surface water bodies and vegetation coverage; UAV aerial survey devices (flight altitude of 200 m and flight line spacing of 150 m) are used to obtain surface image data of the mine, covering the terrain texture and geomorphic features of the entire mining area; laser scanning devices (point cloud density of 50 points / m²) are used to collect laser data of the mine to accurately obtain the terrain undulation and slope structure. When using sensor monitoring devices to collect mine sensor monitoring data in the mine area, high-precision displacement sensors and settlement monitoring sensors can be reasonably arranged at key positions in the mine area, such as slopes, mine entrances, and above goaf areas. These sensors continuously monitor the displacement and settlement conditions of the locations where they are located at set time intervals. After uniformly labeling the four types of data as multi-source mine monitoring data, a geographic information system (GIS) is used for spatial matching processing, and the position deviation between data sources is eliminated through homologous point matching and coordinate transformation (such as the seven-parameter method). Finally, multi-source mine monitoring fusion data with a resolution of 0.5 m is generated through fusion, and a three-dimensional mine terrain model containing terrain elevation, surface texture, and hydrographic elements is constructed based on this data to visually present the mountain shape, road distribution, and potential water accumulation areas in the mining area.

[0020] Step S2: Design the spatial distribution parameters of the exploration plan based on the three-dimensional mine terrain model, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; obtain the corresponding spatial data of the exploration drilling points according to the exploration drilling operations; perform analysis of the rock lithology at the mine points based on the spatial data of the exploration drilling points to generate the rock lithology data of the mine points; In the embodiments of the present invention, based on the 3D mine terrain model, the complex terrain features of the mine are analyzed. The slope, aspect, and terrain roughness are calculated through GIS spatial analysis tools, and steep areas (accounting for 25% of the mining area) with a slope greater than 30° and gentle areas (slope < 15%, accounting for 40%) are identified. For the steep areas, a densified exploration borehole grid (spacing 50m × 50m) is designed, and a conventional grid (spacing 100m × 100m) is used for the gentle areas. Inspection holes are additionally arranged at terrain mutation points (such as ridges and valleys). The GPS-RTK device (positioning accuracy ±1 cm) is used for lofting the borehole positions, and a vehicle-mounted drill rig is used to perform the borehole operation. The borehole depth is preset to 80 - 150 m according to geological data. During the borehole process, the borehole trajectory is monitored in real time through an inclinometer (accuracy ±0.1°) to ensure that the deviation slope < 1%; core samples are collected and the depth is recorded, and well logging operations (natural gamma, resistivity, acoustic travel time) and surface geophysical exploration (seismic refraction wave method) are carried out simultaneously to obtain the physical parameter data of the borehole positions. Based on the borehole spatial coordinates, well logging curves, and geophysical inversion results, combined with the core logging data, the lithology of the rock strata at each position is analyzed. For example, a rare earth mineralized layer is identified at a depth of 100 - 120 m, which shows an obvious difference from the upper and lower sandstone layers (natural gamma value 80 - 120 API), and mine position rock stratum lithology data including lithology categories, physical parameters, and spatial coordinates are generated.

[0021] Step S3: Map the mine position rock stratum lithology data to the 3D mine terrain model to obtain the mapped data of the mine position rock stratum lithology; perform optimization processing on the spatial accuracy of the mapped data of the mine position rock stratum lithology to generate optimized mapped data of the mine position rock stratum lithology; In the embodiments of the present invention, the mine position rock stratum lithology data is mapped to the 3D terrain model. The borehole positions are converted from the geographic coordinate system to the local coordinate system of the mining area through coordinate transformation, and the lithology data is projected onto the terrain surface using the bilinear interpolation method to generate the initial mapped data. To improve the accuracy, plane projection transformation (Gauss forward calculation) and elevation constraint adjustment are performed on the mapped data: Three known leveling points are selected in the mining area as the control reference, and the elevation residuals of the borehole positions are calculated using the least squares method. The elevation mean square error after correction ≤ ±5 cm. Through the calibrated borehole position data, the spatial calibration of the lithology mapped data is carried out, and it is assigned to the surrounding voxels through Kriging interpolation method, and finally the optimized mapped data of the mine position rock stratum lithology is generated to ensure that the lithology distribution fits precisely with the terrain surface.

[0022] Step S4: performing a mine rock stratum lithology blending guidance analysis based on the optimized mine point rock stratum lithology mapping data to generate mine rock stratum lithology blending guidance data; performing a mine rock stratum boundary structure analysis by optimizing the mine point rock stratum lithology mapping data to generate mine rock stratum boundary structure data; performing a rock stratum lithology blending feature analysis on the mine rock stratum boundary structure data based on the mine rock stratum lithology blending guidance data to generate rock stratum lithology blending feature data; performing a mine rock stratum lithology structure analysis based on the rock stratum lithology blending feature data and the mine rock stratum boundary structure data to generate mine rock stratum lithology structure data; In the embodiment of the present invention, the optimized lithology mapping data is discretized, and the continuous lithology parameters are divided into 5 categories (topsoil, sandstone, mineralized layer, granite, and weathered layer), and each voxel (0.5m×0.5m×0.5m) is assigned a unique lithology code. By calculating the lithology complexity index and the conversion probability of adjacent voxels, the contact zone between the mineralized layer and the sandstone is identified as a high blending area. The B-spline surface is used to fit the contact zone morphology, and the rock layer boundary structure (triangular mesh accuracy 0.5m) is extracted by combining the Marching Cubes algorithm to generate a three-dimensional contour model of the mineralized layer. By analyzing the blending intensity (such as the rate of change of lithology parameters near the contact zone) and the guiding direction (mineralized fluid diffusion trend), the lithology blending characteristic data is established. For example, at the coordinates (3200,2150,-60), the blending intensity is 0.8 and the guiding direction is 30° northeast. Based on the above data, a lithological structure model of the mining rock strata including six types of structures such as topsoil, mineralized layer and bedrock was constructed to clearly display the spatial distribution and contact relationship of each rock stratum.

[0023] Step S5: Based on the lithology structure data of the mine rock strata, a three-dimensional modeling process of lithology blending and differentiation rendering of the mine rock strata is performed to generate a three-dimensional visualization rendering model of the mine rock strata.

[0024] In the embodiment of the present invention, the rock layer boundary structure data is used to perform preliminary layered modeling on the lithology structure model: the topsoil layer is rendered as a yellow transparent grid, the mineralized layer is a red entity, and the bedrock is a gray translucent body, and the spatial form of the ore body is highlighted by setting the layered transparency. For the lithology blending area (such as the contact zone between the mineralized layer and the sandstone), differential rendering is performed according to the blending characteristic data: in the area where the blending intensity is >0.7, a gradient texture (red to gray transition) is used to simulate the mixing effect of the mineral components; in the area with the same guiding direction, a streamlined texture is added to represent the migration path of the mineralized fluid. At the same time, sharp boundary rendering is used for sudden changes such as faults, which is in contrast to the gradient blending area. The final generated three-dimensional visualization model supports multi-perspective browsing, attribute query (such as lithology coding and physical parameters at any point) and section analysis. Geologists can intuitively evaluate the spatial distribution, reserve scale and mining feasibility of rare earth ore bodies through the model, providing a dynamic visualization decision-making basis for mine planning.

[0025] Furthermore, the monitoring device described in step S1 includes satellite equipment, UAV aerial survey equipment, laser scanning equipment, and sensor monitoring equipment. Step S1 includes the following steps: Step S11: Use satellite equipment to collect mine hydrographic remote sensing data for the mine area and generate mine hydrographic remote sensing data; Step S12: Use UAV aerial survey equipment to collect mine surface image data for the mine area and generate mine surface image data; Step S13: Use laser scanning equipment to collect mine laser data for the mine area and generate mine laser data; Step S14: Use sensor monitoring equipment to collect mine sensor monitoring data for the mine area and generate mine sensor monitoring data; Step S15: Mark the mine hydrographic remote sensing data, mine surface image data, mine laser data, and mine sensor monitoring data as preliminary mine multi-source monitoring data; perform data preprocessing on the preliminary mine multi-source monitoring data to obtain mine multi-source monitoring data; Step S16: Perform mine monitoring spatial matching processing on the mine multi-source monitoring data to generate mine monitoring spatial matching data; Step S17: Perform matching and integration processing on the mine multi-source monitoring data through the mine monitoring spatial matching data to generate mine multi-source monitoring fusion data; Step S18: Establish a mine surface triangular grid based on the mine multi-source monitoring fusion data, and perform elevation interpolation processing on the triangular vertices of the mine surface triangular grid to obtain mine surface interpolation data; perform mine terrain surface reconstruction processing based on the mine surface interpolation data to obtain mine terrain surface modeling data; Step S19: Perform mine terrain surface optimization processing on the mine terrain surface modeling data to establish a mine three-dimensional terrain model.

[0026] In the embodiments of the present invention, according to the specificities of various types of monitoring devices, such as satellite devices, unmanned aerial vehicle (UAV) aerial survey devices, laser scanning devices, and sensor monitoring devices, systematic management of the entire life cycle of calibration, maintenance, and usage monitoring is carried out, thereby ensuring quality control and safe operation during the device usage process and ensuring the accuracy and reliability of measurement data. The satellite device selects an optical remote sensing satellite with a resolution better than 0.5 meters, which is applied to the large-scale remote sensing monitoring of the hydrographic environment in the rare earth mine area. By setting an orbital coverage period of 3 consecutive days and combining the multi-spectral images transmitted back by the satellite, the river flow direction, water body boundary, runoff path, and vegetation distribution of the target mining area are collected, and the original remote sensing images are processed through georeferencing correction and radiometric correction to generate mine hydrographic remote sensing data. This data is used for the auxiliary construction of hydrographic factors in the subsequent three-dimensional terrain modeling of the mining area. The UAV aerial survey device uses a vertical takeoff and landing fixed-wing UAV system equipped with RGB sensors and near-infrared sensors, such as DJI M300 RTK, to perform the task of collecting surface images in the mining area. The flight altitude is set to 120 meters, and the flight line overlap is 80% horizontally and 75% vertically to ensure that the acquired images have high spatial continuity and accurate geolocation. Through the implementation of image rectification processing with ground control points (GCPs), the obtained image data is registered according to the unified coordinate reference system (CGCS2000) to output the mine surface image data, which is used for the texture mapping of the surface morphology in the three-dimensional model. The laser scanning device adopts a combination of lidar (LiDAR) and a ground tripod-mounted laser scanner to complete the high-precision three-dimensional point cloud acquisition of the terrain in the open-pit sand and gravel mine area. The lidar on the UAV is used to scan and obtain the point cloud data of large-scale scenes such as the slopes and transportation roads in the mining area during large-scale movement, and the tripod-mounted device is arranged at the key positions of the mine pit edge and the yard to obtain the micro-topographic information such as ore accumulation and pit wall profiles. All point cloud data is spatially filtered and coordinate projection transformed with a unified density accuracy of 1 cm / point and output as the mine laser data in a standard format. When using the sensor monitoring device to collect the mine sensor monitoring data in the mining area, high-precision displacement sensors and settlement monitoring sensors can be reasonably arranged at the key positions in the mining area, such as slopes, mine entrances, and above the goaf. These sensors continuously monitor the displacement and settlement conditions of the locations where they are located at set time intervals. The above four types of data are uniformly incorporated into the multi-source monitoring data framework. By setting a clear data field tag structure, the source device, collection time, spatial range, and data resolution parameters of each type of data are identified to form a unified data structure system and construct a preliminary mine multi-source monitoring data set, including: GPS measurement data: represented as a point set , including longitude, latitude, and elevation information; lidar point cloud data: represented as a point set , where is the reflection intensity; UAV aerial photography image data: generated into a point set through photogrammetry ; Sensor monitoring data: represented as a point set , recording displacement or settlement , and timestamp. These data sources have different spatial resolutions, accuracies, and temporal characteristics. The fusion process needs to ensure consistency and complementarity to preprocess the preliminary multi-source mine monitoring data set and label it as multi-source mine monitoring data. Spatial alignment of all data sources is performed based on a reference coordinate system. By using feature recognition technology, the same landmark features (such as mine boundary stakes, ore conveyor belts, road intersections, etc.) in each data are extracted, and spatial registration and correction processing between images - remote sensing - point clouds are implemented. Multi-source data fusion aims to integrate GPS, lidar, and UAV point cloud data to generate a high-precision and spatially consistent point set. The fusion process includes two stages: spatial registration and weighted average. Spatial registration uses the Iterative Closest Point (ICP) algorithm to align data by minimizing the error function between two point clouds: , where , is the corresponding point pair in the two point clouds, that is , represents a point cloud subset of two of GPS measurement data, lidar point cloud data, or UAV aerial image data is a 3×3 rotation matrix is a 3×1 translation vector. The ICP algorithm iteratively optimizes and until the error converges. For the point clouds in the overlapping area, a weighted average method based on accuracy is used for fusion: , where is the point of the th data source is the standard deviation of the measurement error is the weight. The weight is inversely proportional to the error variance, ensuring that high-precision data contributes more to the fusion result. The fused point cloud has higher spatial resolution and accuracy. Sensor data does not directly participate in point cloud fusion but is used for subsequent dynamic adjustment of the model elevation to reflect terrain changes (such as settlement). Content fusion processing is performed on the spatially matched multi-source data. First, a preliminary terrain model is constructed based on the laser point cloud. For example, terrain surface reconstruction is based on the fused point cloud to generate a continuous three-dimensional terrain surface. First, project the point cloud onto the plane to construct a Delaunay triangulation network that satisfies the empty circle property: the circumcircle of triangle does not contain other points . This property ensures the optimality and geometric stability of the triangulation network and generates a three-dimensional grid , where are vertices are edges Take it as a plane. Subsequently, for the internal points of the triangle Perform elevation interpolation using the barycentric coordinate method: , where are the elevations of the three vertices of the triangle, are the barycentric coordinates, is elevation interpolation data. Barycentric coordinate interpolation ensures the linear continuity of elevation and computational efficiency. The output is the initial three-dimensional terrain grid model . Model optimization improves the surface smoothness and terrain feature fidelity through grid smoothing and boundary constraints. Grid smoothing uses the Laplacian operator to adjust the vertex positions: , where and are the vertex coordinates before and after smoothing respectively, is the vertex 's neighborhood vertex set, is the smoothing factor (usually taken as 0.1 - 0.5). This method reduces surface noise through neighborhood averaging. To preserve key terrain features (such as slopes, roads), constraints are imposed on the vertices on the boundaries and feature lines: , if boundary, this constraint ensures that important terrain features are not distorted due to smoothing. The optimized model has both smoothness and feature fidelity. Sensor data is used to dynamically correct the model elevation at this stage. For the point , according to the after time synchronization, adjust the elevation of the corresponding vertex: , this step ensures that the model reflects the latest terrain changes. Subsequently, overlay the watershed boundaries and flow paths identified by remote sensing hydrological data and combine with the surface image texture for texture mapping to form a complete three-dimensional model of the mine surface and hydrological structure. Verify the model accuracy and reliability through quantitative indicators. Error assessment uses an independent validation point set to calculate the root mean square error between the model elevation and the actual elevation : , provides a quantitative indicator of the overall accuracy of the model. Terrain feature verification includes slope angle inspection, and the calculation formula is: , where is the coordinate difference between adjacent points. If or the feature error exceeds the threshold, then return to the terrain surface reconstruction and model optimization steps for iterative optimization.

[0027] Furthermore, step S2 includes the following steps: Step S21: Analyze the complex terrain features of the mine based on the three-dimensional terrain model of the mine to generate complex terrain feature data of the mine; Step S22: Design the spatial distribution parameters of the exploration plan according to the complex terrain feature data of the mine, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; Step S23: Obtain the spatial data of the corresponding exploration drilling points according to the exploration drilling operations; Step S24: Respectively collect the logging data of the exploration drilling points and the geophysical exploration data of the exploration drilling points through the spatial data of the exploration drilling points to obtain the logging data of the exploration drilling points and the geophysical exploration data of the exploration drilling points; Step S25: Analyze the spatial distribution of the exploration drilling rock formations according to the spatial data of the exploration drilling points to generate the spatial distribution data of the exploration drilling rock formations; Step S26: Use the logging data of the exploration drilling points, the geophysical exploration data of the exploration drilling points, and the spatial distribution data of the exploration drilling rock formations to analyze the lithology of the rock formations at the mine points, and generate the lithology data of the rock formations at the mine points.

[0028] In the embodiment of the present invention, in the rare earth mine scenario, based on the constructed three-dimensional mine terrain model (resolution 0.5m×0.5m), terrain complexity feature analysis is performed. First, terrain slope calculation is carried out. The third-order inverse distance squared weight difference method is adopted, and the formula is: slope = where and are respectively and the elevation change rates in the directions. At the same time, terrain curvature is calculated. The second-order difference method is adopted, including planar curvature and profile curvature, which are used to identify the terrain undulation change rate. Further, terrain feature points are extracted, including mountaintop points (local elevation maximum values), valley points (local elevation minimum values), and saddle points (extreme points in two directions), which are realized by the 8-neighborhood elevation comparison method. The fractal dimension is used to calculate the terrain complexity, and the box counting method is used. The formula is: where The number of boxes. Finally, generate mine terrain complex feature data including slope, aspect, curvature, terrain undulation degree, and fractal dimension, and store it as raster data (32-bit floating-point type, coordinate system is CGCS2000). Based on the terrain complex feature data, design the spatial distribution parameters of exploration boreholes. For areas with a slope greater than 35°, adopt a dense grid borehole layout strategy with a borehole spacing of 50m×50m; for gentle areas with a slope less than 15°, adopt a conventional grid borehole layout with a spacing of 100m×100m. At the same time, add inspection holes in areas with drastic changes in terrain curvature (absolute value of curvature greater than 0.05), and the hole spacing is encrypted to 30m. Use the three-dimensional geostatistical method to determine the borehole depth parameters, and establish an ore body thickness variogram model according to the known distribution law of rare earth mineralization zones. For shallow oxidized ore layers, the borehole depth parameter is set to 80m below the terrain elevation; for deep primary ore layers, the borehole depth extends to 150m. Use a total station (angle measurement accuracy ±2″, distance measurement accuracy ±(2mm + 2ppm)) for borehole position lofting, and use GPS-RTK technology (plane accuracy ±1cm + 1ppm, elevation accuracy ±2cm + 1ppm) to monitor the borehole verticality in real time to ensure that the borehole deviation rate is less than 1%. During the borehole operation, use a borehole inclinometer (measurement range 0°~180°, accuracy ±0.1°) to measure the borehole inclination and azimuth every 5m of drilling, and use the three-point inclinometry calculation method to obtain the three-dimensional coordinates of the borehole trajectory. After the borehole is completed, use an underground TV camera system (resolution 1920×1080, illuminance ≥2000lux) to obtain the borehole wall image, and use an image stitching algorithm to generate a continuous unfolded borehole wall diagram. At the same time, record the core recovery rate during the borehole process. For intact rock layers, the core recovery rate is required to be greater than 90%, and for fractured rock layers, it is required to be greater than 70%. Use a laser rangefinder (measurement range 0~50m, accuracy ±3mm) to measure the core length, and combine with the borehole depth record to establish the corresponding relationship between the core sample and the borehole depth. Finally, generate exploration borehole position spatial data including borehole coordinates (X, Y, Z), inclination, azimuth, core recovery rate, and core sample depth, and store it as a three-dimensional point-line data set (GeoJSON format). Conduct geophysical logging operations in the borehole, and use a multi-parameter logging tool (measurement parameters include natural gamma, resistivity, acoustic travel time, etc.). Natural gamma logging uses a scintillation counter (measurement range 0~300API, accuracy ±5API) to measure the radioactive intensity of the formation to identify radioactive abnormal horizons containing rare earth minerals. Resistivity logging uses a dual laterolog device (detection depths are 0.3m and 1.2m respectively), and the measurement range is 0.1~10000 , used to distinguish the electrical property differences between ore bodies and surrounding rocks. Sonic logging uses a single-shot dual-receiver transducer (transmitting frequency 20 kHz) to measure the propagation speed of sound waves in rocks (measurement range 1000 - 6000 m / s, accuracy ±10 m / s), which is used to analyze the compactness and porosity of rocks. At the same time, seismic exploration points are arranged around the borehole, and the shallow seismic refraction method (the seismic source is a hammering seismic source, sampling interval 0.5 ms, recording length 2 s) is used to obtain the velocity stratification information of the underground geological structure. The electromagnetic induction method (frequency range 10 Hz - 100 kHz) is used to measure the conductivity distribution of underground media, which is used to delineate the spatial range of rare earth mineralized bodies. Based on the spatial data of borehole positions and logging geophysical exploration data, the spatial distribution analysis of rock strata is carried out. First, the logging data is standardized using the Z-score standardization formula: , where is the original logging value, is the mean value, is the standard deviation. Then, the clustering analysis method (K-means algorithm, the number of clusters = 5) is used to classify the lithology of the standardized logging data to determine the logging response characteristics of different lithologies. The Kriging interpolation method is used to establish the rock stratum interface model, and the variogram model selects the Gaussian model, and the formula is: , where is the semi-variance, is the nugget value, is the sill value, is the range. According to the borehole core logging data and logging interpretation results, the top and bottom elevation of different rock strata are determined, and a three-dimensional rock stratum interface is generated through Kriging interpolation. Calculate the rock stratum thickness distribution, and use three-dimensional geological modeling software (such as GOCAD) to construct the spatial distribution volume model of rock strata, and the voxel size is set to 5 m × 5 m × 2 m. Comprehensively use the borehole position logging data, geophysical exploration data and rock stratum spatial distribution data for lithology analysis. First, establish a lithology interpretation template. For the rare earth mineralized layer, the natural gamma value is greater than 150 API, the resistivity is greater than 500 , and the acoustic wave transit time is less than 250 μs / m; for the granite layer, the natural gamma value is 50 - 100 API, the resistivity is 1000 - 5000 , and the acoustic wave transit time is 200 - 240 μs / m; for the sandstone layer, the natural gamma value is 30 - 60 API, the resistivity is 200 - 1000 , and the acoustic wave transit time is 240 - 300 μs / m. The fuzzy comprehensive evaluation method is used for lithology identification, and an evaluation index system is established (logging parameter weights: natural gamma 0.4, resistivity 0.3, acoustic wave transit time 0.3), and the membership degree of each measurement point belonging to different lithologies is calculated. The formula is: , where is the membership degree, is the lithology standard value, For adjusting parameters. Combining the seismic exploration inversion results (wave impedance values) and electromagnetic induction data (conductivity values) to verify and correct the lithology identification results. Finally, generate the rock lithology data of the mine site strata including lithology categories (such as rare earth mineralized layers, granite, sandstone, claystone, etc.), lithology probabilities (confidence level ≥ 0.8), and lithology physical property parameters (porosity, permeability, density), and store them as three-dimensional attribute volume data (SEG-Y format) for subsequent rock lithology mapping and visualization analysis.

[0029] Further, step S3 includes the following steps: Step S31: Map the spatial data of the exploration borehole points to the three-dimensional terrain model of the mine for exploration borehole point mapping conversion to generate exploration borehole point mapping data; Step S32: Perform exploration borehole plane projection point conversion processing on the exploration borehole point mapping data to generate exploration borehole plane projection point conversion data; Step S33: Perform exploration borehole elevation constraint adjustment calculation based on the exploration borehole plane projection point conversion data to generate exploration borehole elevation constraint adjustment data; Step S34: Perform exploration borehole point spatial calibration processing on the exploration borehole point spatial data according to the exploration borehole elevation constraint adjustment data to generate calibrated exploration borehole point spatial data; Step S35: Transmit the rock lithology data of the mine site strata to the three-dimensional terrain model of the mine for rock lithology mapping processing of the mine site strata to generate rock lithology mapping data of the mine site strata, and perform mapping space accuracy optimization processing on the rock lithology mapping data of the mine site strata through the calibrated exploration borehole point spatial data to generate optimized rock lithology mapping data of the mine site strata.

[0030] In the embodiment of the present invention, in the rare earth mine scenario, based on the spatial data of the exploration borehole points, perform mapping conversion. First, unify the coordinate systems, and convert the geodetic coordinates (B, L, H) of the borehole points to the local coordinate system (X, Y, Z) of the three-dimensional terrain model of the mine. Project the borehole points onto the surface of the terrain model. For each borehole point (X, Y), determine the grid cell where it is located in the terrain model grid data (resolution 0.5m × 0.5m), and use bilinear interpolation to calculate the terrain elevation of this point : , where are the elevation values of the four vertices of the grid, is the corresponding weight (determined by the inverse ratio of the distance from the point position to the vertex). Finally, the mapping data of the exploration borehole point positions containing the drilling plane positions (X, Y) and the terrain-matched elevation are generated. Based on the mapping data of the exploration borehole point positions, the plane projection point position conversion is carried out, and the direct Gaussian projection is executed to convert the geodetic coordinates (B, L) into the Gaussian plane rectangular coordinates (x, y). Then, the projection deformation correction is carried out. According to the deformation situation at the edge of the projection zone where the mining area is located (the length deformation rate ≤ 0.00025), the zone projection or the method of compensating elevation surface is adopted. For the area where the length deformation exceeds the threshold, the central meridian is reselected or the compensating elevation surface is set : , where is the average elevation of the surveyed area, is the length deformation rate, represents the increment or difference of the spatial coordinates, represents the original value or target value of the spatial coordinates, is the average radius of curvature of the earth (6371 km). Finally, the conversion data of the exploration borehole plane projection point positions with the Gaussian plane coordinates (x, y) and the corresponding geodetic height (h) are generated. Based on the conversion data of the plane projection point positions, the elevation constrained adjustment calculation is carried out. First, the error equation is constructed, and the indirect adjustment model is adopted: , where is the vector of the observed value corrections, is the coefficient matrix, is the vector of the parameters to be solved (the height anomaly value), is the vector of the observed values (the difference between the GPS geodetic height and the leveling elevation). The weight matrix of the observation equation is determined according to the observation accuracy: , where is the unit weight variance, is the covariance matrix of the observed values. The least squares principle is used to solve the parameters: ; the mean square error of the unit weight after adjustment: , where is the number of observed values, is the number of necessary observations. For the rare earth mine, known leveling points (≥ 3) are set as the elevation constraints, and the constraint weight ratio is taken as 10:1. Finally, the exploration borehole elevation constrained adjustment data containing the adjusted elevation values and the accuracy evaluation index (the mean square error ≤ ±0.05 m) are generated and stored as a weighted elevation data set (XYZQ format, Q is the accuracy factor). Based on the elevation constrained adjustment data, the spatial data of the borehole point positions are calibrated. First, the plane position is calibrated, then the adjusted elevation value replaces the original point position elevation, and at the same time, the parameters of the point position calibration error ellipse are calculated. Finally, the calibrated spatial data of the exploration borehole point positions are generated. Based on the rock lithology data of the mine point positions and the calibrated spatial data of the borehole point positions, the lithology mapping and accuracy optimization are carried out. First, the lithology data interpolation is carried out, and the ordinary Kriging interpolation method is adopted: , where is the lithology value of the point to be estimated, represents the coordinate vector of the target spatial point for the lithology value to be estimated, is the known lithology value of the th exploration borehole point, represents the three-dimensional spatial coordinate components of the th exploration borehole point, is the weight coefficient, is the number of calibration borehole points, satisfying . The weight coefficient is obtained by solving the variogram equation system: , where is the variogram, is the Lagrange multiplier, represents the same type of coordinate component of the th exploration borehole point. Using the spatial calibration data of the borehole points as control points, the mapping accuracy of the interpolation result is optimized, and finally the optimized mapping data of the rock layer lithology at the mine points is generated, including lithology category, confidence level, and spatial accuracy index (point position error ≤ ±0.3 m).

[0031] Furthermore, as an embodiment of the present invention, referring to Figure 2 shown, it is a detailed step flow diagram of step S4 in Figure 1 . In this embodiment, step S4 includes the following steps: Step S41: Discretize the rock layer lithology of the optimized mine points to generate discretized data of the rock layer lithology of the mine points; In the embodiment of the present invention, based on the optimized mapping data of the rock layer lithology at the mine points (three-dimensional grid data with a resolution of 0.5 m × 0.5 m × 0.5 m), the discretization process is performed. First, lithology category coding is carried out, and continuous lithology parameters (such as density, resistivity, natural gamma value) are mapped to discrete lithology categories. The threshold segmentation method is used to establish the correspondence between lithology parameters and coding, and then spatial discretization is carried out. The voxelization method is used to divide the three-dimensional space into regular grids. Each voxel (side length 0.5 m) is assigned a unique lithology code and confidence level value, and finally, discretized data of the rock layer lithology of the mine points including lithology code, confidence level, and spatial coordinates is generated.

[0032] Step S42: Analyze the heterogeneous lithology characteristics of the rock layer based on the discretized data of the rock layer lithology of the mine points to generate heterogeneous lithology characteristic data of the rock layer; In the embodiment of the present invention, based on the discretized data of the rock layer lithology of the mine points, the heterogeneous lithology characteristics of the rock layer are analyzed. First, the spatial distribution characteristics of the lithology are extracted, and the lithology complexity index is calculated, where is the lithology category The proportion within a local area (3×3×3 voxel window), is the total number of lithology categories.

[0033] Then analyze the lithology contact relationship, and use the co-occurrence matrix method to statistically analyze the lithology conversion probability between adjacent voxels. For the discretized lithology data of the rock strata at the mine point, the lithology conversion probability of its neighboring voxels is defined as: , where is the lithology and the lithology The number of times adjacent. Further calculate the lithology spatial autocorrelation function , where is the position The lithology code at, is the regional average lithology value, is the spatial lag vector, is the number of sample pairs. Finally, generate a feature data set containing the complexity index, conversion probability matrix, and autocorrelation function parameters, and store it as a multi-dimensional attribute table.

[0034] Step S43: Conduct a lithology blending orientation analysis based on the heterogeneous lithology feature data of the rock strata to generate lithology blending orientation data for the mine rock strata; In the embodiment of the present invention, based on the heterogeneous lithology feature data, a lithology blending orientation analysis is conducted. First, construct a lithology blending field and define the blending intensity , where is the proportionality coefficient (take 1.0), is the lithology conversion probability, is the lithology interface distance, is the characteristic length (take 3m). Then calculate the blending direction vector , where is the lithology complexity gradient vector, is the lithology interface normal vector. Establish a blending orientation model and use the finite element method to solve the blending field equation: , where is the blending potential function, is the blending conduction coefficient matrix, is the source term (related to the lithology conversion probability). Discretize the equation through the variational principle and use the conjugate gradient method to iteratively solve. Finally, generate a guiding data volume containing the blending intensity, direction vector, and blending potential function, and store it as a vector field file.

[0035] Step S44: Extract the differential nodes of the heterogeneous rock lithology within the mine site based on the heterogeneous rock lithology characteristic data to obtain the differential node data of the heterogeneous rock lithology. Perform fitting processing on the heterogeneous rock layer morphology according to the differential node data of the heterogeneous rock lithology to generate the fitting data of the heterogeneous rock layer morphology; perform extraction processing on the boundary structure of the mine rock layer for the discretized data of the mine rock layer lithology to generate the boundary structure data of the mine rock layer. In the embodiment of the present invention, differential node extraction is carried out based on the heterogeneous rock lithology characteristic data. Taking the contact area between a rare earth mineralization zone and granite as an example, the spatial mutation area is first identified through the lithology complexity index. If the lithology complexity index of a certain voxel is significantly higher than the surrounding area (such as more than twice the average value), it is marked as a differential node. For example, at the edge of the mineralization zone, since the lithology rapidly transitions from single granite to a complex mineralization mixture, the lithology complexity index increases sharply, and the voxels at these positions are preferentially extracted as differential nodes. Subsequently, fitting of the heterogeneous rock layer morphology is performed. The extracted differential nodes are used as control points, and B-spline surfaces are used for interpolation. At the contact interface between the mineralization zone and the surrounding rock, by adjusting the weights and positions of the control points, the B-spline surface is closely fitted to the actual lithology change trend. For example, in three-dimensional space, the distribution density of the control points is dynamically adjusted according to the severity of the lithology change: the distribution of control points is sparser within the mineralization zone, while it is denser at the junction with the surrounding rock, ensuring that the surface can accurately represent the complex contact morphology. At the same time, boundary structure extraction is performed on the discretized data. Taking an open-pit mining area as an example, the Marching Cubes algorithm is used to traverse each voxel. When the lithology code of a voxel crosses a preset threshold (such as changing from granite code 1 to mineralized layer code 3), the position of the boundary point is determined by linear interpolation. For example, in an 8×8×8 voxel cube, if the two endpoints of a certain edge are granite and the mineralized layer respectively, the point on this edge where the lithology code is equal to the threshold (such as 2) is found as the boundary point. Connecting all boundary points forms a triangular mesh, and finally, a complete rock layer boundary structure is constructed, such as the three-dimensional contour of the mineralization zone, the interface of different lithology layers, etc.

[0036] Step S45: Analyze the characteristics of the rock lithology blend according to the rock lithology blend guiding data of the mine rock layer to generate the characteristic data of the rock lithology blend. In the embodiments of the present invention, based on the lithological blending orientation data, the blending characteristics are analyzed. In the contact area between the rare earth mineralization zone and the sandy shale, the blending transition specificity index is calculated. By measuring the spatial change rate of the blending intensity (i.e., the blending intensity gradient) and combining it with the change rate of the blending potential function in the normal direction of the interface, the severity of the lithological blending is evaluated. For example, in the area where the mineralized fluid invades the sandy shale, if the blending intensity gradient is large and the change of the blending potential function is significant, it indicates that the lithological blending in this area is active and a high-grade mineralization zone may be formed. When establishing the blending orientation mapping relationship, the correlations among parameters such as lithological complexity, blending intensity, and contact angle are analyzed. For example, at the contact interface between the mineralization zone and the surrounding rock, if the contact angle is close to vertical and the blending intensity is high, it often corresponds to the main migration channel of the mineralized fluid; while a gentle contact interface may imply the lateral diffusion of mineralization. By statistically analyzing the parameter relationships of a large number of sample points, the blending characteristic patterns under different lithological combinations are summarized. When performing lithological orientation gradient analysis, the gradient amplitude and direction angle of the blending potential function in three-dimensional space are calculated. In the extension direction of a certain ore vein, the area where the gradient amplitude of the blending potential function is large and the direction angle is consistent indicates the dominant expansion direction of mineralization. For example, if the gradient direction is mainly from northeast to southwest, it is speculated that the ore vein may extend along this direction, providing spatial guidance for subsequent exploration.

[0037] Step S46: Based on the lithological blending characteristic data of the rock strata and the boundary structure data of the mine rock strata, the lithological structure of the mine rock strata is analyzed to generate the lithological structure data of the mine rock strata.

[0038] In the embodiments of the present invention, lithologic structure analysis is carried out based on the blended feature data and the boundary structure data. In a large rare earth mining area, the rock strata are first stratified. The areas with similar blended features are divided into the same lithologic layer. For example, the voxels with similar mineralization intensity, blending direction and transition specificity index are clustered into the mineralized layer. In this way, the entire mining area is divided into different layers such as the overburden layer, the mine ore body layer, the semi-weathered layer, the weathered ore-free zone and the bedrock layer. When constructing the lithologic structure model, the geostatistical simulation method is adopted. Based on the known borehole data, a continuous lithologic attribute field is generated through sequential Gaussian simulation. For example, for the grade of rare earth elements, with the measured data of boreholes as the constraint conditions, the grade distribution is simulated in three-dimensional space, and the grade isosurface is generated to intuitively display the spatial distribution of high-grade mineralized bodies. When calculating the attitude parameters of the structural plane, the triangular mesh of the extracted rock stratum boundary is analyzed. Through the spherical projection method, the normal vector of each triangular face is projected onto the unit sphere, and the dip angle and dip direction are calculated. For example, for the contact surface between the mineralized zone and the surrounding rock, the attitude parameters of a large number of triangular faces are statistically analyzed, and the average dip angle of this contact surface is 65°, and the dip direction is 30° northeast. These parameters are of great guiding significance for understanding the spatial distribution of mineralized bodies and subsequent mining design. Finally, the layered structure, the result of attribute simulation and the structural plane parameters are integrated into a complete lithologic structure data set to form a three-dimensional geological model, which intuitively displays the lithologic distribution and structural characteristics inside the rare earth mine, such as the overburden layer, the ore body layer, the semi-weathered layer and the bedrock layer included in the barefoot mine, or the overburden layer, the ore body layer and the groundwater structure included in the fully covered mine, etc.

[0039] Further, as an embodiment of the present invention, refer to Figure 3 shown, which is Figure 2 the detailed step flow schematic diagram of step S45 in Step S451: Perform lithologic blending transition specificity analysis according to the heterogeneous lithologic feature data of the rock strata to generate lithologic blending transition specificity data; In the embodiments of the present invention, lithologic blending transition specificity analysis is performed according to the heterogeneous lithologic feature data of the rock strata. For example, lithologic blending transition specificity analysis is performed based on the collected ore point positions, and the lithologic difference index of adjacent voxels is calculated , where and are the lithologic codes of and , and and are the lithologic quantization values of the lithologic codes of , the transition zone between the mineralized layer and the surrounding rock is identified. By analyzing the lithological transition gradient and the lithological transition width, and analyzing the lithological conversion frequency between adjacent voxels through the lithological difference index, lithological transition gradient and lithological transition width of adjacent voxels, it is identified that the contact area between the rare earth mineralized layer and the sandstone is an area where lithological blending is active. The proportion of the number of voxels of different lithological combinations in this contact area is statistically analyzed. If the number of voxels adjacent to the rare earth mineralized layer and the sandstone accounts for 60% of the total contact voxels, and the lithological complexity index of these voxels is significantly higher than that of other areas, it can be determined that this area is the key area of lithological blending transition. At the same time, analyze the spatial change rate of lithological parameters in the area where the rare earth mineralized layer transitions to the sandstone to obtain specific data on lithological blending transition, including information such as the change rate, change amplitude, and spatial coordinates of the key transition area.

[0040] Step S452: Establish a mapping relationship oriented to lithological blending of rock strata through the discrete data of rock strata lithology at the mine site and the specific data of lithological blending transition, and generate a lithological blending orientation model of rock strata; In the embodiment of the present invention, a lithological blending orientation model of rock strata is constructed by combining the discrete data of rock strata lithology at the mine site and the specific data of lithological blending transition. Taking the contact area between the rare earth mineralized layer and the sandstone as an example, the lithological code, spatial coordinates of each voxel in the discrete data are associated with the change characteristics in the specific data of lithological blending transition. For example, for a certain voxel, its lithological code is the rare earth mineralized layer and it is located in the key area marked by the specific data of lithological blending transition. Establish a corresponding relationship between the spatial coordinates of this voxel and information such as the change rate and direction of lithological blending. Through the analysis of a large number of sample data, summarize the blending rules of different lithological combinations at different spatial positions to form a set of mapping rules. For example, in a specific depth interval (-50 meters to -80 meters), when the rare earth mineralized layer is adjacent to the sandstone, the mineralized material tends to diffuse along the direction of 30° northeast. Based on these rules, a lithological blending orientation model of rock strata is constructed. This model can predict the trend and direction of lithological blending according to the input lithological and spatial information.

[0041] Step S453: Conduct lithological orientation analysis on the discrete data of rock strata lithology at the mine site to generate discrete lithological orientation data; conduct lithological orientation gradient feature analysis according to the discrete lithological orientation data to generate lithological orientation gradient feature data; In the embodiment of the present invention, lithology-guided analysis is performed on the discretized data of the lithology of the rock strata at the mine points. In the three-dimensional model of the rare earth mine, for each discretized voxel, the lithology distribution of the surrounding voxels is analyzed. For example, for a voxel located at the edge of the mineralized layer, if the adjacent voxel on its east side is sandstone, the adjacent voxel on its west side is still the mineralized layer, and the lithology complexity of the sandstone area on the east side is relatively low, it can be known from the specific data of lithology blending and transition that this direction is the potential direction of mineralized material diffusion, and this direction information is recorded as the discretized lithology-guided data. Further, lithology-guided gradient feature analysis is performed according to the discretized lithology-guided data. By taking the derivative to calculate the change degree of the lithology-guided direction at different positions, a group of voxels is selected every 5 meters on the contact zone between the mineralized layer and the sandstone, and the difference in the lithology-guided direction of adjacent groups of voxels is compared. If it is found that from the center of the mineralized layer to the edge, the lithology-guided direction gradually changes from 30° northeast to 45° northeast, then the change gradient of the lithology guidance in this area is calculated, and information such as the gradient magnitude and the direction change trend is generated as lithology-guided gradient feature data.

[0042] Step S454: Transmit the discretized data of the lithology of the rock strata at the mine points and the lithology-guided gradient feature data to the lithology blending guidance model of the rock strata for lithology blending guidance analysis of the mine rock strata, and generate lithology blending guidance data of the mine rock strata.

[0043] In the embodiment of the present invention, the discretized data of the lithology of the rock strata at the mine points and the lithology-guided gradient feature data are input into the established lithology blending guidance model of the rock strata. Taking an unexplored area in a rare earth mine as an example, the lithology codes and spatial coordinates of some voxels in this area are known. These discretized data are input into the model, and at the same time, combined with the lithology-guided gradient feature data of this area, the model analyzes and predicts the specific situation of lithology blending in this area according to the established mapping relationship and blending rules. For example, by calculating the blending strength between different rock strata , where is the proportional adjustment coefficient, is the gradient operator of the guiding vector perpendicular to the lithology interface, represents the spatial change rate of the lithology guiding vector at point , is the point to the perpendicular distance of the reference lithology interface, is the effective range affected by lithology blending, and the blending direction correction coefficient of each rock stratum lithology is calculated through the change rate of the gradient amplitude in the direction. Finally, the lithology blending guidance data of the mine rock strata is obtained. The model outputs the lithology blending guidance data of the mine rock strata, including information such as the lithology blending trend, possible blending boundaries, and potential mineralized areas, providing a decision-making basis for the further exploration and mining of the mine.

[0044] Further, step S452 includes the following steps: Analyze the gradual blending transition coefficient and mutation blending transition coefficient of lithological characteristics based on the specific data of lithological blending transition, and obtain the gradual blending transition coefficient and mutation blending transition coefficient of lithological characteristics respectively; Calculate the difference in adjacent lithological physical parameters based on the discretized data of the rock strata lithology at the mine points; Establish a heterogeneous lithological transition blending tree structure based on the difference in adjacent lithological physical parameters, and analyze the blending transition intensity of lithological characteristics according to the gradual blending transition coefficient and mutation blending transition coefficient of lithological characteristics to generate lithological characteristic blending transition intensity data; conduct mapping relationship analysis of the rock strata lithology blending orientation through the heterogeneous lithological transition blending tree structure and the lithological characteristic blending transition intensity data to establish a rock strata lithology blending orientation model.

[0045] In the embodiment of the present invention, for the contact area between the mineralized layer and the sandstone (such as near the coordinate point (2250, 1750, -100)), collect lithological physical parameters (natural gamma value GR , density per 0.5 m along the direction perpendicular to the interface (such as the positive X-axis direction). Calculate the parameter change rate (unit: API / m) of adjacent sampling points. For example, when transitioning from the mineralized layer ( GR = 180 API) to the sandstone ( GR = 80 API), the change rate in a certain interval is -200 API / m. Normalize all change rates to the interval [0, 1] to obtain the gradual blending transition coefficient . Here, = 250 API / m, then for this interval = 0.8. At the contact zone between the mineralized layer and the granite near the fault, calculate the absolute difference in parameters of adjacent voxels: . Normalize all differences to the interval [0, 1] to obtain the mutation blending transition coefficient . Here, , then = 0.67. Based on the parameter change rate of adjacent sampling points corresponding to the discretized data of the rock strata lithology at the mine points and the absolute difference in parameters of adjacent voxels , calculate the physical parameter difference between each voxel and its neighboring voxels. Construct a tree structure: each node represents a lithology (such as sandstone coded 2, granite coded 1). The edge weight is the weighted sum of the physical parameter differences of adjacent lithologies. For example, the connection edge weight between the mineralized layer and the sandstone: , where 0.6 and 0.4 are the parameter change rate GR of adjacent sampling points and the absolute difference in parameters of adjacent voxels The weights, 100 and 0.3 are the normalization reference values. Starting from the root node, connect all adjacent lithology nodes, and for each child node, recursively connect its adjacent other lithology nodes, finally forming a multi-level tree structure. Each layer of nodes represents the transition relationship of different lithologies. Combining the tree structure with the transition coefficient, calculate the blending transition intensity (1 - node level / maximum level) + (node level / maximum level), store the blending intensities of all node pairs as a matrix to obtain Lithology and of the blending intensity. For each voxel, calculate its lithology blending orientation vector according to its position in the tree structure and the blending transition intensity of the adjacent connection edges. The weight is determined according to the proportion of the blending transition intensity of the connection edges. Through the method of vector synthesis, obtain the lithology blending orientation vector. Perform the above calculations for all voxels to establish the mapping relationship of the lithology blending orientation in the entire mine area, thereby generating the lithology blending orientation model of the rock stratum. This model can be used to predict the direction and trend of lithology blending in the rare earth mine, providing a clear feedback on the specific content of each level in the three-dimensional visualization of the mine

[0046] Furthermore, the rock stratum lithology structure data described in step S46 includes the surface soil layer structure of the mine, the ore body layer structure of the mine, the semi-weathered layer structure of the mine, the weathered ore-free zone structure of the mine, the bedrock layer structure of the mine, and the groundwater structure of the mine

[0047] Furthermore, step S5 includes the following steps Use the rock stratum boundary structure data of the mine to perform preliminary hierarchical visualization three-dimensional modeling processing on the rock stratum lithology structure data of the mine to generate a three-dimensional visualization model of the rock stratum of the mine According to the lithology blending characteristic data of the rock stratum, perform lithology blending differential rendering processing on the three-dimensional visualization model of the rock stratum of the mine to generate a three-dimensional visualization rendering model of the rock stratum of the mine

[0048] In the embodiments of the present invention, based on the generated mine rock stratum boundary structure data and mine rock stratum lithology structure data, preliminary hierarchical visualization three-dimensional modeling processing is carried out. According to the mine rock stratum boundary structure data, these data record the spatial interfaces of different rock strata in the form of triangular meshes, such as the contact surfaces between the rare earth mineralized layer and sandstone and granite. Import these boundary structure data into three-dimensional modeling software, and use the boundaries as constraint conditions to spatially divide the mine rock stratum lithology structure data (including stratification information such as topsoil, ore body, semi-weathered layer, weathered ore-free zone, bedrock layer, etc.). For example, in the area with coordinates X: 1000 - 1500m, Y: 800 - 1200m, Z: -80 - 0m, the software accurately defines the three-dimensional spatial range of the rare earth mineralized layer according to the boundary structure data, and at the same time places the topsoil layer at the top and the bedrock layer at the bottom to form a preliminary three-dimensional layered structure model. Each rock stratum is distinguished by different colors, with the topsoil layer represented by yellow and the rare earth mineralized layer represented by red, so that the three-dimensional visualization model of the mine rock stratum can intuitively present the spatial distribution relationship of each rock stratum. Based on the rock lithology blending characteristic data, differential rendering processing of the preliminary three-dimensional visualization model of the mine rock stratum is carried out. In the contact area between the rare earth mineralized layer and sandstone, adjust the rendering effect according to the blending intensity and direction information recorded in the rock lithology blending characteristic data. For areas with high blending intensity, increase the complexity of the texture and the color transition effect. For example, at the junction of the mineralized layer and sandstone, blend red (mineralized layer) and gray (sandstone) in a gradient manner to simulate the actual rock lithology transition characteristics; while for areas with low blending intensity, use a clearer boundary and a single color for rendering. At the same time, according to the rock lithology guiding gradient characteristic data, highlight the direction of rock lithology change during rendering. For example, in the direction of mineralized material diffusion, make the color and texture present an extended visual effect. After this series of processing, the finally generated three-dimensional visualization rendering model of the mine rock stratum can not only clearly display the spatial positions of each rock stratum, but also vividly present the detailed characteristics of rock lithology blending through differential rendering, providing intuitive and accurate visualization data support for mine geological analysis, mining planning, etc.

[0049] This specification provides a mine three-dimensional visualization system for performing the mine three-dimensional visualization method as described above. The mine three-dimensional visualization system includes: A mine three-dimensional terrain construction module for collecting multi-source mine monitoring data of a mine area using monitoring equipment; constructing a three-dimensional model for multi-source mine monitoring based on the multi-source mine monitoring data to obtain a mine three-dimensional terrain model; A mine exploration operation module for designing exploration plan spatial distribution parameters based on the mine three-dimensional terrain model and performing exploration drilling operations through the exploration plan spatial distribution parameters; obtaining corresponding exploration drilling point spatial data according to the exploration drilling operations; performing mine point rock stratum lithology analysis based on the exploration drilling point spatial data to generate mine point rock stratum lithology data; The mine point rock stratum analysis module is used to map the lithology data of the mine point rock stratum to the mine three-dimensional terrain model to obtain the mapped data of the mine point rock stratum lithology; perform mapping space accuracy optimization processing on the mapped data of the mine point rock stratum lithology to generate optimized mapped data of the mine point rock stratum lithology. The mine rock stratum lithology structure analysis module is used to perform mine rock stratum lithology blending orientation analysis based on the optimized mapped data of the mine point rock stratum lithology to generate mine rock stratum lithology blending orientation data; perform mine rock stratum boundary structure analysis through the optimized mapped data of the mine point rock stratum lithology to generate mine rock stratum boundary structure data; perform lithology blending feature analysis on the mine rock stratum boundary structure data according to the mine rock stratum lithology blending orientation data to generate lithology blending feature data; perform mine rock stratum lithology structure analysis based on the lithology blending feature data and the mine rock stratum boundary structure data to generate mine rock stratum lithology structure data. The mine three-dimensional visualization module is used to perform three-dimensional modeling processing of lithology blending differentiation rendering of the mine rock stratum based on the mine rock stratum lithology structure data to generate a three-dimensional visualization rendering model of the mine rock stratum.

[0050] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0051] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional visualization method for mines, characterized in that, It includes the following steps: Step S1: Use monitoring equipment to collect multi-source mine monitoring data in the mine area; Based on the multi-source mine monitoring data, construct a three-dimensional model for multi-source mine monitoring to obtain a three-dimensional mine terrain model; Step S2: Design the spatial distribution parameters of the exploration plan based on the three-dimensional mine terrain model, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; Obtain the corresponding spatial data of the exploration drilling points according to the exploration drilling operations; Based on the spatial data of the exploration drilling points, analyze the rock lithology of the mine points to generate the rock lithology data of the mine points; Step S3: Map the rock lithology data of the mine points to the three-dimensional mine terrain model to obtain the mapped data of the rock lithology of the mine points; Perform optimization processing on the spatial accuracy of the mapped data of the rock lithology of the mine points to generate the optimized mapped data of the rock lithology of the mine points; Step S4: Conduct an analysis on the guidance of the rock lithology fusion in the mine based on the optimized mapped data of the rock lithology of the mine points to generate the guidance data of the rock lithology fusion in the mine; Analyze the boundary structure of the mine rock strata through the optimized mapped data of the rock lithology of the mine points to generate the boundary structure data of the mine rock strata; Analyze the characteristics of the rock lithology fusion of the mine rock strata based on the guidance data of the rock lithology fusion in the mine to generate the characteristic data of the rock lithology fusion; Conduct an analysis on the rock lithology structure of the mine rock strata based on the characteristic data of the rock lithology fusion and the boundary structure data of the mine rock strata to generate the rock lithology structure data of the mine rock strata; Step S5: Perform three-dimensional modeling processing of differential rendering of the rock lithology fusion in the mine rock strata based on the rock lithology structure data of the mine rock strata to generate a three-dimensional visual rendering model of the mine rock strata.

2. The three-dimensional visualization method for mines according to claim 1, wherein Among them, the monitoring equipment described in Step S1 includes satellite equipment, UAV aerial survey equipment, laser scanning equipment, and sensor monitoring equipment. Step S1 includes the following steps: Step S11: Use satellite equipment to collect mine hydrographic remote sensing data in the mine area to generate mine hydrographic remote sensing data; Step S12: Use UAV aerial survey equipment to collect mine surface image data in the mine area to generate mine surface image data; Step S13: Use laser scanning equipment to collect mine laser data in the mine area to generate mine laser data; Step S14: Use sensor monitoring equipment to collect mine sensor monitoring data in the mine area to generate mine sensor monitoring data; Step S15: Mark the mine hydrographic remote sensing data, mine surface image data, mine laser data, and mine sensor monitoring data as preliminary multi-source mine monitoring data; Perform data preprocessing on the preliminary multi-source mine monitoring data to obtain multi-source mine monitoring data; Step S16: Perform spatial matching processing on the multi-source mine monitoring data to generate spatial matching data for mine monitoring; Step S17: Perform matching and integration processing on the multi-source mine monitoring data through the spatial matching data for mine monitoring to generate multi-source mine monitoring fusion data; Step S18: Establish a triangular mesh of the mine surface based on the multi-source monitoring fusion data of the mine, and perform elevation interpolation on the triangular vertices of the triangular mesh of the mine surface to obtain the interpolated data of the mine surface; perform mine terrain surface reconstruction processing based on the interpolated data of the mine surface to obtain the modeling data of the mine terrain surface. Step S19: Perform mine terrain surface optimization processing on the modeling data of the mine terrain surface to establish a 3D mine terrain model.

3. The three-dimensional visualization method for a mine according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Analyze the complex characteristics of the mine terrain based on the 3D mine terrain model to generate complex characteristic data of the mine terrain. Step S22: Design the spatial distribution parameters of the exploration plan according to the complex characteristic data of the mine terrain, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan. Step S23: Obtain the corresponding spatial data of the exploration drilling points according to the exploration drilling operations. Step S24: Collect the logging data of the exploration drilling points and the geophysical exploration data of the exploration drilling points respectively through the spatial data of the exploration drilling points to obtain the logging data of the exploration drilling points and the geophysical exploration data of the exploration drilling points. Step S25: Analyze the spatial distribution of the exploration drilling rock formations according to the spatial data of the exploration drilling points to generate spatial distribution data of the exploration drilling rock formations. Step S26: Use the logging data of the exploration drilling points, the geophysical exploration data of the exploration drilling points, and the spatial distribution data of the exploration drilling rock formations to analyze the lithology of the rock formations at the mine points to generate lithology data of the rock formations at the mine points.

4. The three-dimensional visualization method for a mine according to claim 1, wherein Step S3 includes the following steps: Step S31: Map the spatial data of the exploration drilling points to the 3D mine terrain model for exploration drilling point mapping conversion to generate exploration drilling point mapping data. Step S32: Perform exploration drilling plane projection point conversion processing on the exploration drilling point mapping data to generate exploration drilling plane projection point conversion data. Step S33: Perform elevation constraint adjustment calculation on the exploration drilling plane projection point conversion data to generate elevation constraint adjustment data of the exploration drilling. Step S34: Perform exploration drilling point spatial calibration processing on the spatial data of the exploration drilling points according to the elevation constraint adjustment data of the exploration drilling to generate calibrated spatial data of the exploration drilling points. Step S35: Transmit the lithology data of the rock formations at the mine points to the 3D mine terrain model for lithology mapping processing of the rock formations at the mine points to generate lithology mapping data of the rock formations at the mine points, and perform mapping space accuracy optimization processing on the lithology mapping data of the rock formations at the mine points through the calibrated spatial data of the exploration drilling points to generate optimized lithology mapping data of the rock formations at the mine points.

5. The three-dimensional visualization method for mines according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform discretization processing of the lithology of the rock formations at the mine points according to the optimized lithology mapping data of the rock formations at the mine points to generate discretized data of the lithology of the rock formations at the mine points. Step S42: Analyze the heterogeneous lithology characteristics of the rock formations according to the discretized data of the lithology of the rock formations at the mine points to generate heterogeneous lithology characteristic data of the rock formations. Step S43: Perform guiding analysis on the blending of the lithology of the mine rock formations based on the heterogeneous lithology characteristic data of the rock formations to generate guiding data for the blending of the lithology of the mine rock formations. Step S44: Extract the differentiated nodes of the heterogeneous rock lithology within the mine point based on the heterogeneous rock lithology characteristic data of the rock stratum to obtain the differentiated node data of the heterogeneous rock lithology of the rock stratum. Perform fitting processing on the heterogeneous rock stratum morphology according to the differentiated node data of the heterogeneous rock lithology of the rock stratum to generate the fitting data of the heterogeneous rock stratum morphology; perform extraction processing on the boundary structure of the mine rock stratum for the discretized data of the mine rock stratum lithology to generate the boundary structure data of the mine rock stratum. Step S45: Analyze the characteristics of the rock lithology blending based on the rock lithology blending guiding data of the mine rock stratum to generate the characteristic data of the rock lithology blending. Step S46: Analyze the rock lithology structure of the mine rock stratum based on the characteristic data of the rock lithology blending and the boundary structure data of the mine rock stratum to generate the rock lithology structure data of the mine rock stratum.

6. The three-dimensional visualization method for mines according to claim 5, characterized in that, Step S45 includes the following steps: Step S451: Analyze the specificity of the lithology blending transition based on the heterogeneous rock lithology characteristic data of the rock stratum to generate the specificity data of the lithology blending transition. Step S452: Establish a mapping relationship of the rock lithology blending guiding through the discretized data of the mine rock stratum lithology and the specificity data of the lithology blending transition to generate the rock lithology blending guiding model. Step S453: Perform lithology guiding analysis on each discretized data of the discretized data of the mine rock stratum lithology to generate the discretized lithology guiding data; perform analysis on the gradient characteristics of the lithology guiding based on the discretized lithology guiding data to generate the gradient characteristic data of the lithology guiding. Step S454: Transmit the discretized data of the mine rock stratum lithology and the gradient characteristic data of the lithology guiding to the rock lithology blending guiding model for analysis of the rock lithology blending guiding of the mine rock stratum to generate the rock lithology blending guiding data of the mine rock stratum.

7. The three-dimensional visualization method for mines according to claim 6, wherein, Step S452 includes the following steps: Analyze the gradual blending transition coefficient and the sudden blending transition coefficient of the lithology characteristics according to the specificity data of the lithology blending transition to respectively obtain the gradual blending transition coefficient of the lithology characteristics and the sudden blending transition coefficient of the lithology characteristics. Calculate the difference in adjacent lithology physical parameters based on the discretized data of the mine rock stratum lithology. Establish a tree-like structure of heterogeneous lithology transition blending based on the difference in adjacent lithology physical parameters, and perform analysis on the intensity of the lithology characteristic blending transition according to the gradual blending transition coefficient of the lithology characteristics and the sudden blending transition coefficient of the lithology characteristics to generate the intensity data of the lithology characteristic blending transition; perform analysis on the mapping relationship of the rock lithology blending guiding through the tree-like structure of heterogeneous lithology transition blending and the intensity data of the lithology characteristic blending transition to establish the rock lithology blending guiding model.

8. The three-dimensional visualization method for mines according to claim 5, characterized in that The rock lithology structure data of the mine rock stratum described in Step S46 includes the structure of the mine surface soil layer, the structure of the mine ore body layer, the structure of the mine semi-weathered layer, the structure of the mine weathered ore-free zone, the structure of the mine bedrock layer, and the structure of the mine groundwater.

9. The three-dimensional visualization method for mines according to claim 5, characterized in that, Step S5 includes the following steps: Perform preliminary hierarchical visualization three-dimensional modeling processing on the rock lithology structure data of the mine rock stratum using the boundary structure data of the mine rock stratum to generate a three-dimensional visualization model of the mine rock stratum. Perform lithology blending differentiation rendering processing on the three-dimensional visualization model of the mine rock stratum according to the characteristic data of the rock lithology blending to generate a three-dimensional visualization rendering model of the mine rock stratum.

10. A three-dimensional visualization system for mines, characterized in that, For executing the three-dimensional visualization method of a mine as described in claim 1, the three-dimensional visualization system of the mine includes: A three-dimensional mine terrain construction module, configured to collect multi-source monitoring data of the mine area by using monitoring devices; construct a three-dimensional model for multi-source monitoring of the mine based on the multi-source monitoring data of the mine to obtain a three-dimensional mine terrain model; A mine exploration operation module, configured to design spatial distribution parameters of an exploration plan based on the three-dimensional mine terrain model, and perform exploration drilling operations through the spatial distribution parameters of the exploration plan; obtain corresponding spatial data of exploration drilling points according to the exploration drilling operations; perform lithology analysis of rock formations at mine points based on the spatial data of exploration drilling points to generate lithology data of rock formations at mine points; A mine point rock formation analysis module, configured to map the lithology data of rock formations at mine points to the three-dimensional mine terrain model to obtain mapped lithology data of rock formations at mine points; perform optimization processing on the spatial accuracy of the mapped lithology data of rock formations at mine points to generate optimized mapped lithology data of rock formations at mine points; A mine rock formation lithology structure analysis module, configured to perform lithology blending orientation analysis of mine rock formations based on the optimized mapped lithology data of rock formations at mine points to generate lithology blending orientation data of mine rock formations; perform mine rock formation boundary structure analysis through the optimized mapped lithology data of rock formations at mine points to generate mine rock formation boundary structure data; perform lithology blending feature analysis on the mine rock formation boundary structure data according to the lithology blending orientation data of mine rock formations to generate lithology blending feature data; perform mine rock formation lithology structure analysis based on the lithology blending feature data and the mine rock formation boundary structure data to generate mine rock formation lithology structure data; A three-dimensional mine visualization module, configured to perform three-dimensional modeling processing of differential rendering of lithology blending of mine rock formations based on the mine rock formation lithology structure data to generate a three-dimensional visualization rendering model of mine rock formations.

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