Mine 3D visualization method and system

Abstract

The present invention relates to the field of mine visualization technology, and in particular to a mine three-dimensional visualization method and system. The method comprises the following steps: establishing a three-dimensional mine terrain model; designing exploration planning spatial distribution parameters based on the mine three-dimensional terrain model, and performing exploration drilling operations through the exploration planning spatial distribution parameters; collecting lithologic data of the mine point strata according to the exploration drilling operations; mapping the lithologic data of the mine point strata to the mine three-dimensional terrain model to obtain optimized lithologic mapping data of the mine point strata; analyzing the boundary structure data of the mine strata and the lithologic blending characteristic data of the strata according to the optimized lithologic mapping data of the mine point strata, and performing lithologic structure analysis of the mine strata to generate lithologic structure data of the mine strata; performing lithologic blending differentiated rendering three-dimensional modeling of the mine strata based on the lithologic structure data of the mine strata, and generating a three-dimensional visualization rendering model of the mine strata. The present invention realizes accurate visualization analysis of the structure inside the mine.

Description

Technical Field

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

[0002] In traditional mining resource exploration and development, the acquisition, analysis, and presentation of mine geological information primarily rely on two-dimensional drawings and text-based materials, which suffer from limited information capacity, difficulty understanding spatial information, and poor dynamic updating capabilities. With the increasing scarcity of mineral resources and the increasing complexity of mining environments, traditional methods are no longer able to meet the demand for detailed, three-dimensional, and real-time representation of orebody structures, severely restricting the efficiency and accuracy of mine exploration, design, and safety management. Existing three-dimensional mine visualization methods, however, incorporate geographic information systems (GIS), remote sensing (RS), and computer-aided design (CAD) tools to digitally model and manage mining areas. However, these technologies still have significant limitations in data fusion, spatial matching accuracy, lithologic structure modeling, and 3D visualization. In particular, the lack of a 3D visualization method that integrates remote sensing monitoring, geological exploration, and spatial modeling in order to express the relationship between lithologic characteristics and spatial distribution of rock formations hinders intuitive understanding and accurate analysis of mine geological structure and lithologic distribution. 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 purpose, a three-dimensional mine visualization method includes the following steps:

[0005] Step S1: using monitoring equipment to collect multi-source monitoring data of the mine area; constructing a three-dimensional model of the mine multi-source monitoring based on the multi-source monitoring data to obtain a three-dimensional terrain model of the mine;

[0006] Step S2: Designing exploration planning spatial distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operations using the exploration planning spatial distribution parameters; obtaining corresponding exploration drilling point spatial data based on the exploration drilling operations; performing lithology analysis of the mine point rock formations based on the exploration drilling point spatial data to generate lithology data of the mine point rock formations;

[0007] Step S3: mapping the mine point rock stratum lithology data to the mine three-dimensional terrain model to obtain the 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;

[0008] Step S4: performing a mine rock stratum lithologic integration guidance analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum lithologic integration guidance data; performing a mine rock stratum boundary structure analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum boundary structure data; performing a rock stratum lithologic integration characteristic analysis on the mine rock stratum boundary structure data based on the mine rock stratum lithologic integration guidance data to generate rock stratum lithologic integration characteristic data; performing a mine rock stratum lithologic structure analysis based on the rock stratum lithologic integration characteristic data and the mine rock stratum boundary structure data to generate mine rock stratum lithologic structure data;

[0009] Step S5: performing lithologic blending and differentiated rendering three-dimensional modeling processing of the mine rock strata based on the lithologic structure data of the mine rock strata to generate a three-dimensional visual rendering model of the mine rock strata.

[0010] Furthermore, the monitoring equipment in step S1 includes satellite equipment, drone aerial survey equipment, laser scanning equipment, and sensor monitoring equipment, and step S1 includes the following steps:

[0011] Step S11: using satellite equipment to collect mine hydrological remote sensing data of the mine area to generate mine hydrological remote sensing data;

[0012] Step S12: using unmanned aerial survey equipment to collect mine surface image data of the mine area to generate mine surface image data;

[0013] Step S13: using laser scanning equipment to collect mine laser data in the mine area to generate mine laser data;

[0014] Step S14: using sensor monitoring equipment to collect mine sensor monitoring data in the mine area to generate mine sensor monitoring data;

[0015] Step S15: marking the mine hydrological remote sensing data, the mine surface image data, the mine laser data, and the mine sensor monitoring data as preliminary mine multi-source monitoring data; performing data preprocessing on the preliminary mine multi-source monitoring data to obtain the mine multi-source monitoring data;

[0016] Step S16: performing mine monitoring space matching processing on the mine multi-source monitoring data to generate mine monitoring space matching data;

[0017] Step S17: performing 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;

[0018] Step S18: establishing a mine surface triangulated mesh based on the mine multi-source monitoring fusion data, and performing elevation interpolation processing on the triangle vertices of the mine surface triangulated mesh to obtain mine surface interpolation data; performing mine terrain surface reconstruction processing based on the mine surface interpolation data to obtain mine terrain surface modeling data;

[0019] Step S19: performing mine terrain surface optimization processing on the mine terrain surface modeling data to establish a three-dimensional mine terrain model.

[0020] Furthermore, step S2 includes the following steps:

[0021] Step S21: Analyzing the complex features of the mine terrain based on the three-dimensional mine terrain model to generate complex feature data of the mine terrain;

[0022] Step S22: Designing exploration planning spatial distribution parameters based on the complex terrain feature data of the mine, and performing exploration drilling operations using the exploration planning spatial distribution parameters;

[0023] Step S23: obtaining corresponding exploration drilling point spatial data according to the exploration drilling operation;

[0024] Step S24: collecting exploration borehole point well logging data and exploration borehole point geophysical data using the exploration borehole point spatial data to obtain exploration borehole point well logging data and exploration borehole point geophysical data;

[0025] Step S25: performing exploration borehole rock stratum spatial distribution analysis based on the exploration borehole point spatial data to generate exploration borehole rock stratum spatial distribution data;

[0026] Step S26: Utilizing the exploration drilling point logging data, the exploration drilling point geophysical data, and the exploration drilling point rock stratum spatial distribution data, the mining point rock stratum lithology analysis is performed to generate the mining point rock stratum lithology data.

[0027] Furthermore, step S3 includes the following steps:

[0028] Step S31: mapping the exploration drilling point spatial data to the three-dimensional terrain model of the mine to perform exploration drilling point mapping conversion to generate exploration drilling point mapping data;

[0029] Step S32: performing exploration borehole plane projection point conversion processing on the exploration borehole point mapping data to generate exploration borehole plane projection point conversion data;

[0030] Step S33: performing exploration borehole elevation constraint adjustment calculation according to the exploration borehole plane projection point conversion data to generate exploration borehole elevation constraint adjustment data;

[0031] Step S34: performing 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;

[0032] Step S35: The lithologic data of the mine point rock formation is transmitted to the three-dimensional terrain model of the mine for lithologic mapping of the mine point rock formation to generate the lithologic mapping data of the mine point rock formation, and the mapping space accuracy of the lithologic mapping data of the mine point rock formation is optimized by calibrating the spatial data of the exploration drilling point to generate the optimized lithologic mapping data of the mine point rock formation.

[0033] Furthermore, step S4 includes the following steps:

[0034] Step S41: performing discretization processing on the lithology of the mine point strata according to the optimized mine point strata lithology mapping data to generate discretized data of the lithology of the mine point strata;

[0035] Step S42: performing a rock stratum heterogeneous lithologic characteristic analysis based on the discretized rock stratum lithologic data of the mining site to generate rock stratum heterogeneous lithologic characteristic data;

[0036] Step S43: performing a mine rock stratum lithologic integration guidance analysis based on the rock stratum heterogeneous lithologic characteristic data to generate mine rock stratum lithologic integration guidance data;

[0037] Step S44: extracting the heterogeneous lithologic differentiation nodes of the strata within the mine site based on the heterogeneous lithologic characteristic data to obtain heterogeneous lithologic differentiation node data; performing heterogeneous stratum morphology fitting processing on the heterogeneous lithologic differentiation node data to generate heterogeneous stratum morphology fitting data; and extracting the boundary structure of the strata at the mine site based on the discretized lithologic data to generate the boundary structure data of the strata.

[0038] Step S45: performing stratum lithologic integration characteristic analysis based on the mine stratum lithologic integration guidance data to generate stratum lithologic integration characteristic data;

[0039] Step S46: performing a lithologic structure analysis of the mine rock strata based on the lithologic blending characteristic data of the rock strata and the boundary structure data of the mine rock strata to generate lithologic structure data of the mine rock strata.

[0040] Furthermore, step S45 includes the following steps:

[0041] Step S451: performing lithologic transition specificity analysis based on the stratum heterogeneous lithologic characteristic data to generate lithologic transition specificity data;

[0042] Step S452: establishing a mapping relationship of lithologic blending guidance based on the lithologic discretization data of the mining site and the lithologic blending transition specificity data, and generating a lithologic blending guidance model;

[0043] Step S453: performing lithologic guidance analysis on the discretized lithologic data of the mining point rock formation to generate discretized lithologic guidance data; performing lithologic guidance gradient feature analysis on the discretized lithologic guidance data to generate lithologic guidance gradient feature data;

[0044] Step S454: transmitting the mine point rock stratum lithologic discretization data and the rock stratum steering gradient characteristic data to the rock stratum lithologic integration steering model to perform mine rock stratum lithologic integration steering analysis to generate mine rock stratum lithologic integration steering data.

[0045] Furthermore, step S452 includes the following steps:

[0046] According to the specific data of lithologic blending transition, the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are analyzed, and the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are obtained respectively;

[0047] Calculate the difference in physical parameters of adjacent lithologies based on the discretized lithologic data of the mining sites;

[0048] Based on the difference in physical parameters of adjacent lithologies, a heterogeneous lithologic transition and blending tree structure is established, and the lithologic characteristic blending transition intensity is analyzed according to the lithologic characteristic gradual blending transition coefficient and the lithologic characteristic sudden blending transition coefficient to generate lithologic characteristic blending transition intensity data. The mapping relationship of the lithologic blending guidance of the stratum is analyzed through the heterogeneous lithologic transition and blending tree structure and the lithologic characteristic blending transition intensity data to establish a stratum lithologic blending guidance model.

[0049] Furthermore, the mine rock stratum lithologic structure data in step S46 includes the mine topsoil layer structure, the mine ore body layer structure, the mine semi-weathered layer structure, the mine weathered non-ore zone structure, the mine bedrock layer structure and the mine groundwater structure.

[0050] Furthermore, step S5 includes the following steps:

[0051] Using the boundary structure data of the mine rock strata to process the lithologic structure data of the mine rock strata, a preliminary layered visualization 3D model of the mine rock strata is generated;

[0052] According to the lithologic blending characteristic data of the rock strata, the lithologic blending differentiated rendering processing of the mine rock strata is performed on the mine rock strata visualization three-dimensional model to generate the mine rock strata visualization three-dimensional rendering model.

[0053] This specification provides a three-dimensional mine visualization system for executing the above-mentioned three-dimensional mine visualization method. The three-dimensional mine visualization system includes:

[0054] The mine 3D terrain construction module is used to collect multi-source monitoring data of the mine area using monitoring equipment; construct a 3D model of the mine multi-source monitoring based on the mine multi-source monitoring data to obtain a 3D terrain model of the mine;

[0055] The mine exploration operation module is used to design exploration planning spatial distribution parameters based on the mine's three-dimensional terrain model and execute exploration drilling operations using the exploration planning spatial distribution parameters; obtain corresponding exploration drilling point spatial data based on the exploration drilling operations; perform lithology analysis of the mine point rock formation based on the exploration drilling point spatial data and generate lithology data of the mine point rock formation;

[0056] The mine point rock layer analysis module is used to map the mine point rock layer lithology data to the mine three-dimensional terrain model to obtain the mine point rock layer lithology mapping data; the mine point rock layer lithology mapping data is optimized for mapping space accuracy to generate optimized mine point rock layer lithology mapping data;

[0057] The mine rock formation lithologic structure analysis module is used to perform mine rock formation lithologic integration guidance analysis based on the optimized mine point rock formation lithologic mapping data to generate mine rock formation lithologic integration guidance data; perform mine rock formation boundary structure analysis by optimizing the mine point rock formation lithologic mapping data to generate mine rock formation boundary structure data; perform rock formation lithologic integration feature analysis on the mine rock formation boundary structure data based on the mine rock formation lithologic integration guidance data to generate rock formation lithologic integration feature data; perform mine rock formation lithologic structure analysis based on the rock formation lithologic integration feature data and the mine rock formation boundary structure data to generate mine rock formation lithologic structure data;

[0058] The mine 3D visualization module is used to perform 3D modeling and processing of the lithologic blending and differentiated rendering of the mine rock strata based on the lithologic structure data of the mine rock strata, and generate a 3D visualization rendering model of the mine rock strata.

[0059] The beneficial effects of this application lie in the fact that, through a multi-source mine monitoring data acquisition and three-dimensional model construction method, the present invention can effectively integrate multiple monitoring methods such as satellite remote sensing, drone aerial surveys, and laser scanning to comprehensively acquire hydrological, surface, and spatial structural data for the mining area. This not only improves the coverage and accuracy of data acquisition, but also achieves efficient integration of multi-source heterogeneous data through spatial matching and fusion processing, accurately constructing a three-dimensional mine terrain model. This provides refined and three-dimensional basic data support for subsequent exploration planning and geological analysis, significantly improving the accuracy and visualization capabilities of mine spatial information representation. Based on the terrain feature analysis of the mine's three-dimensional terrain model, the spatial distribution parameters of the exploration boreholes are scientifically designed, ensuring the targeted and efficient exploration operations. Furthermore, by combining well logging data, geophysical data, and rock stratum spatial distribution analysis results from the borehole locations, accurate identification and multi-dimensional analysis of the mine's rock stratum structure and lithologic characteristics are achieved. This method can effectively reflect the spatial variation patterns of underground geological structures, improve the accuracy and reliability of geological exploration, and provide a solid data foundation and decision-making support for the detailed exploration and rational development of mineral resources. Precisely mapping lithologic data from exploration drill holes to the mine's 3D terrain model achieves spatial integration of underground lithologic information and the surface terrain model, enhancing the integrity and realism of the mine's 3D visualization model. This mapping process not only intuitively presents complex borehole geological information in 3D space, but also provides more intuitive data support for subsequent orebody modeling, resource assessment, and mine planning. High-precision spatial mapping and spatial coordinate adjustment optimization of exploration drill holes improve the spatial registration accuracy and vertical elevation consistency of the mapped data. In particular, the elevation-constrained adjustment and spatial calibration processes effectively eliminate drill hole position errors caused by acquisition errors and terrain undulations. The resulting optimized lithologic mapping data for the mine's sites ensures a high degree of consistency between the underground lithologic data and the terrain model, enhancing the spatial accuracy and analytical reliability of the 3D visualization system. Further analysis of the optimized lithologic mapping data extracts lithologic transition characteristics and boundary structure information between strata, significantly enhancing the structural realism and spatial resolution of the 3D model's lithologic representation. This not only enables extended analysis from point data to overall structure, but also enhances the accuracy and systematicness of mine geological modeling in lithology identification, structural division, and heterogeneity expression through the multi-dimensional extraction and integration of lithologic blending characteristics and lithologic boundaries. Through processes such as lithologic discretization, heterogeneous feature analysis, and blend-oriented modeling, a collaborative process for analyzing rock formations from microscopic differences to macroscopic structure has been established.The quantitative analysis of lithologic guidance gradients and lithologic intermixing intensity not only reveals the transitional relationships between gradual and sudden changes in lithologic properties, but also expresses the logical topological relationships between lithologic structures through a tree-like structure model. This provides a theoretical foundation and algorithmic support for subsequent 3D geological model optimization, intelligent stratification, orebody identification, and automatic boundary tracking. Overall, this significantly enhances the application value and intelligence of 3D mine visualization models in geological interpretation and decision support. The effective conversion of mine strata lithologic structural data into visually expressive 3D visualization models significantly improves the intuitive presentation and spatial resolution of geological information. During the 3D modeling process, initial layered modeling, leveraging strata boundary structure data, effectively delineates the structural hierarchical relationships between strata, providing a clear representation of geological zoning and providing a realistic and accurate structural foundation for geological exploration, orebody identification, and engineering decision-making. Differentiated rendering based on lithologic intermixing data accurately reflects the gradual and sudden changes between different lithologies, enabling a differentiated visual representation of mine strata. This rendering method not only enhances the model's resolution and depth of detail when representing heterogeneous geological structures, but also effectively improves the practicality and intelligence of the mine's 3D visualization system in multiple scenarios such as geological interpretation, intelligent exploration decision-making, and visualization demonstration.

[0060] Therefore, the three-dimensional visualization method of the mine of the present invention can effectively obtain and express the spatial distribution and physical characteristics of the rock layer by constructing a lithologic analysis mechanism based on the spatial data of the drilling point and integrating the logging and geophysical data. In particular, through the lithologic blending-oriented modeling, lithologic differentiation discrete processing and lithologic transition structure analysis, it has a strong advantage in the correlation modeling of lithologic characteristics and spatial structures, and solves the problem that the existing methods are difficult to accurately express the complex structure of the rock layer boundary and transition zone. Through multi-level three-dimensional modeling and rendering processing, a three-dimensional visualization model of the mine rock layer that can feedback the internal information of the mine is constructed. This model can not only intuitively display the detailed information of the lithologic content in the mine space, but also achieve a clearer and more realistic display of the mine geological structure, which greatly improves the three-dimensional visualization effect and the ability to recognize the geological structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the steps of a three-dimensional mine visualization method according to the present invention;

[0062] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0063] Figure 3 for Figure 2 Detailed implementation steps of step S45 are shown in the flowchart;

[0064] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0065] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0066] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0067] To achieve this, please refer to Figures 1 to 3 The present invention provides a three-dimensional visualization method for mines. In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of a process flow of a three-dimensional mine visualization method according to the present invention, wherein the three-dimensional mine visualization method comprises the following steps:

[0068] To achieve the above purpose, a three-dimensional mine visualization method includes the following steps:

[0069] Step S1: using monitoring equipment to collect multi-source monitoring data of the mine area; constructing a three-dimensional model of the mine multi-source monitoring based on the multi-source monitoring data to obtain a three-dimensional terrain model of the mine;

[0070] In this embodiment of the present invention, based on the specific characteristics of various monitoring equipment types, such as satellite equipment, drone aerial survey equipment, laser scanning equipment, and sensor monitoring equipment, systematic management is implemented throughout the entire lifecycle of calibration, maintenance, and usage monitoring. This ensures quality control and safe operation of the equipment during use, and ensures the accuracy and reliability of the measurement data. In a rare earth mining area (approximately 5 square kilometers in area, with primarily mountainous terrain), satellite equipment is used to collect mine hydrological remote sensing data to identify surface water distribution and vegetation cover. UAV aerial survey equipment (flight altitude of 200 meters, flight spacing of 150 meters) is used to obtain mine surface image data, covering the terrain texture and geomorphological features of the entire mining area. Laser scanning equipment (point cloud density of 50 points / m²) is used to collect mine laser data, accurately capturing topographic relief and slope structure. When using sensor monitoring equipment to collect mine sensor monitoring data in the mining area, high-precision displacement sensors and settlement monitoring sensors are strategically deployed at key locations within the mine, such as slopes, mine entrances, and above goafs. These sensors continuously monitor displacement and settlement at set intervals. After the four types of data were uniformly labeled as multi-source mine monitoring data, spatial matching processing was performed using a geographic information system (GIS). The positional deviation between data sources was eliminated through matching of homonymous points and coordinate transformation (such as the seven-parameter method). Finally, the data was fused to generate multi-source mine monitoring fusion data with a resolution of 0.5m. Based on this data, a three-dimensional mine terrain model containing terrain elevation, surface texture, and hydrological elements was constructed, which intuitively presented the mountain morphology, road distribution, and potential waterlogging areas of the mining area.

[0071] Step S2: Designing exploration planning spatial distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operations using the exploration planning spatial distribution parameters; obtaining corresponding exploration drilling point spatial data based on the exploration drilling operations; performing lithology analysis of the mine point rock formations based on the exploration drilling point spatial data to generate lithology data of the mine point rock formations;

[0072] In this embodiment of the present invention, the complex features of the mine's terrain are analyzed based on a three-dimensional mine terrain model. GIS spatial analysis tools are used to calculate slope, aspect, and terrain roughness, identifying steep areas with slopes greater than 30° (accounting for 25% of the mining area) and gentle areas (slopes <15%, accounting for 40%). For steep areas, a dense exploration drilling grid (spacing 50m×50m) is designed, while a conventional grid (spacing 100m×100m) is used for gentle areas. Inspection holes are also added at sudden changes in terrain (such as ridges and valleys). Drilling points are staked out using GPS-RTK equipment (positioning accuracy ±1cm), and drilling operations are performed using a vehicle-mounted drill rig. The drilling depth is preset to 80-150m based on geological data. During drilling, the borehole trajectory is monitored in real time using an inclinometer (accuracy of ±0.1°) to ensure a deviation rate of less than 1%. Core samples are collected and depths recorded. Simultaneous logging (natural gamma ray, resistivity, and acoustic transit time) and ground geophysical exploration (seismic refraction) are performed to obtain physical parameter data for each borehole point. Based on the borehole spatial coordinates, logging curves, and geophysical inversion results, combined with core logging data, the lithology of each point is analyzed. For example, a rare earth mineralized layer is identified at a depth of 100-120m, significantly different from the sandstone layers above and below (natural gamma ray values ​​of 80-120 API). This generates lithology data for each mining point, including lithology categories, physical parameters, and spatial coordinates.

[0073] Step S3: mapping the mine point rock stratum lithology data to the mine three-dimensional terrain model to obtain the 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;

[0074] In an embodiment of the present invention, the lithologic data of the rock formations at the mining site are mapped to a three-dimensional terrain model, the drilling sites are converted from the geographic coordinate system to the local coordinate system of the mining area through coordinate transformation, and the lithologic data are projected onto the terrain surface using bilinear interpolation to generate initial mapping data. To improve accuracy, the mapping data is subjected to planar projection transformation and elevation constraint adjustment: known leveling points are selected within the mining area as control benchmarks, and the elevation residuals of the drilling sites are calculated using the least squares method, and the elevation errors are corrected. The lithologic mapping data is spatially calibrated using the calibrated drilling site data, and distributed to the surrounding voxels using the Kriging interpolation method, ultimately generating optimized lithologic mapping data for the rock formations at the mining site.

[0075] Step S4: performing a mine rock stratum lithologic integration guidance analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum lithologic integration guidance data; performing a mine rock stratum boundary structure analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum boundary structure data; performing a rock stratum lithologic integration characteristic analysis on the mine rock stratum boundary structure data based on the mine rock stratum lithologic integration guidance data to generate rock stratum lithologic integration characteristic data; performing a mine rock stratum lithologic structure analysis based on the rock stratum lithologic integration characteristic data and the mine rock stratum boundary structure data to generate mine rock stratum lithologic structure data;

[0076] In this embodiment of the present invention, the optimized lithologic mapping data is discretized, classifying the continuous lithologic parameters into five categories (topsoil, sandstone, mineralized layer, granite, and weathered layer). Each voxel (0.5 m × 0.5 m × 0.5 m) is assigned a unique lithologic code. By calculating the lithologic complexity index and the transition probability between adjacent voxels, the contact zone between the mineralized layer and the sandstone is identified as a highly intermingled area. A B-spline surface is used to fit the contact zone morphology, combined with the Marching Cubes algorithm (with a triangular mesh accuracy of 0.5 m) to extract the rock layer boundary structure and generate a three-dimensional contour model of the mineralized layer. Lithologic intermingling characteristic data is established by analyzing the intermingling intensity (e.g., the rate of change of lithologic parameters near the contact zone) and the orientation (diffusion trend of the mineralized fluid). For example, at coordinates (3200, 2150, -60), the intermingling intensity is 0.8 and the orientation is 30° northeast. Based on the above data, a lithologic 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.

[0077] Step S5: performing lithologic blending and differentiated rendering three-dimensional modeling processing of the mine rock strata based on the lithologic structure data of the mine rock strata to generate a three-dimensional visual rendering model of the mine rock strata.

[0078] In this embodiment, a preliminary layered lithologic model is constructed using rock stratum boundary structure data: the topsoil layer is rendered as a yellow transparent grid, the mineralized layer as a solid red solid, and the bedrock as a gray translucent volume. This layered transparency setting emphasizes the spatial morphology of the ore body. For areas of lithologic intermixing (such as the contact zone between the mineralized layer and the sandstone), differentiated rendering is performed based on the intermixing characteristic data. In areas with an intermixing intensity greater than 0.7, a gradient texture (red to gray transition) is used to simulate the mixing of mineral components. In areas with consistent directional orientation, a streamlined texture is added to represent the migration path of the mineralized fluid. Furthermore, abrupt interfaces such as faults are rendered with sharp boundaries to contrast with the gradient intermixing areas. The resulting 3D visualization model supports multi-view browsing, attribute querying (such as lithologic coding and physical parameters at any point), and cross-section analysis. Geologists can use this model to intuitively assess the spatial distribution, reserve size, and mining feasibility of rare earth ore bodies, providing a dynamic, visual decision-making basis for mine planning.

[0079] Furthermore, the monitoring equipment in step S1 includes satellite equipment, drone aerial survey equipment, laser scanning equipment, and sensor monitoring equipment, and step S1 includes the following steps:

[0080] Step S11: using satellite equipment to collect mine hydrological remote sensing data of the mine area to generate mine hydrological remote sensing data;

[0081] Step S12: using unmanned aerial survey equipment to collect mine surface image data of the mine area to generate mine surface image data;

[0082] Step S13: using laser scanning equipment to collect mine laser data in the mine area to generate mine laser data;

[0083] Step S14: using sensor monitoring equipment to collect mine sensor monitoring data in the mine area to generate mine sensor monitoring data;

[0084] Step S15: marking the mine hydrological remote sensing data, the mine surface image data, the mine laser data, and the mine sensor monitoring data as preliminary mine multi-source monitoring data; performing data preprocessing on the preliminary mine multi-source monitoring data to obtain the mine multi-source monitoring data;

[0085] Step S16: performing mine monitoring space matching processing on the mine multi-source monitoring data to generate mine monitoring space matching data;

[0086] Step S17: performing 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;

[0087] Step S18: establishing a mine surface triangulated mesh based on the mine multi-source monitoring fusion data, and performing elevation interpolation processing on the triangle vertices of the mine surface triangulated mesh to obtain mine surface interpolation data; performing mine terrain surface reconstruction processing based on the mine surface interpolation data to obtain mine terrain surface modeling data;

[0088] Step S19: performing mine terrain surface optimization processing on the mine terrain surface modeling data to establish a three-dimensional mine terrain model.

[0089] In an embodiment of the present invention, based on the specific characteristics of each type of monitoring equipment, such as satellite equipment, drone aerial survey equipment, laser scanning equipment, and sensor monitoring equipment, systematic management of the entire lifecycle of calibration, maintenance, and usage monitoring is implemented to ensure quality control and safe operation of the equipment during use, and to ensure the accuracy and reliability of the measurement data. The satellite equipment uses optical remote sensing satellites with a resolution better than 0.5 meters and is used to implement large-scale remote sensing monitoring of the hydrological environment in rare earth mining areas. By setting a three-day orbital coverage period and combining multispectral imagery transmitted by the satellite, the target mining area's river flow direction, water body boundaries, runoff paths, and vegetation distribution are collected. The original remote sensing images are then processed through georeferencing and radiometric correction to generate mine hydrological remote sensing data. This data is used to assist in the construction of hydrological factors in the subsequent three-dimensional terrain modeling of the mining area. The drone aerial survey equipment uses a vertical take-off and landing fixed-wing drone system equipped with an RGB sensor and a near-infrared sensor, such as the DJI M300 RTK, to perform surface image acquisition tasks in the mining area. The flight altitude was set at 120 meters, with a flight path overlap of 80% horizontally and 75% vertically, ensuring high spatial continuity and precise geolocation of the captured images. Image rectification was performed using ground control points (GCPs), and the acquired image data was registered according to the unified coordinate datum system (CGCS2000). This output was used for surface morphology and texture mapping in the 3D model. Laser scanning equipment, combining LiDAR (Light Detection and Ranging) with a ground-based tripod laser scanner, completed high-precision 3D point cloud acquisition of the terrain within the open-pit sand and gravel mine. The drone's LiDAR was used for on-the-go scanning to capture large-scale point cloud data of scenes such as mine slopes and haul roads. Tripod-mounted equipment was deployed at key locations along the mine pit and in the stockpile to capture micro-topography information, including ore accumulation and pit wall profiles. All point cloud data was spatially filtered and converted to coordinate projection using a uniform density of 1 cm / point, and then output as standard mining laser data. When using sensor monitoring equipment to collect mine sensor monitoring data in the mining area, high-precision displacement sensors and settlement monitoring sensors can be reasonably deployed at key locations in the mining area, such as slopes, mine entrances, and above goafs. These sensors continuously monitor the displacement and settlement of their locations 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 label structure, the source equipment, collection time, spatial range and data resolution parameters of each type of data are identified to form a unified data structure system, which is constructed as a preliminary mine multi-source monitoring data set, including: GPS measurement data: represented as a point set , containing latitude, longitude and elevation information; LiDAR point cloud data: represented as a point set ,in is the reflection intensity; UAV aerial image data: point set generated by photogrammetry ;Sensor monitoring data: represented as a point set , record displacement or settlement, is the timestamp. These data sources have different spatial resolutions, accuracies and time characteristics, and the fusion processing needs to ensure consistency and complementarity, so as to pre-process the preliminary mine multi-source monitoring data set and mark it as mine multi-source monitoring data. All data sources are spatially aligned based on the reference coordinate system. The same landmark features in each data (such as mining area boundary stakes, mining belts, road intersections, etc.) are extracted through ground feature recognition technology, and spatial registration and correction processing between images, remote sensing and point clouds is implemented. Multi-source data fusion aims to integrate GPS, lidar and drone point cloud data to generate a high-precision, spatially consistent point set. The fusion process includes two stages: spatial registration and weighted averaging. Spatial registration uses the iterative closest point (ICP) algorithm to align data by minimizing the error function between two point clouds: ,in , are the corresponding point pairs in the two point clouds, that is, 、 Represented as a point cloud subset of GPS measurement data, lidar point cloud data, or drone aerial image data. is a 3×3 rotation matrix, is a 3×1 translation vector. ICP algorithm iterative optimization and , until the error converges. For the point clouds in the overlapping area, the weighted average method based on accuracy is used for fusion: ,in For the A data source point, is the standard deviation of measurement error, The weight is inversely proportional to the error variance, ensuring that high-precision data contributes more to the fusion result. With higher spatial resolution and accuracy. Sensor data It does not directly participate in point cloud fusion, but is used for the dynamic adjustment of subsequent model elevation to reflect terrain changes (such as settlement). Content fusion processing is performed on the multi-source data after spatial matching. First, a preliminary terrain model is constructed based on the laser point cloud. For example, the terrain surface reconstruction is based on the fused point cloud. Generate a continuous 3D terrain surface. First, Plane projection point cloud, construct Delaunay triangulation, satisfy the empty circle property: triangle The circumcircle of does not contain any other points This property ensures the optimality and geometric stability of the triangulated network and generates a three-dimensional mesh ,in is the vertex, For the edge, Then the internal points of the triangle Perform elevation interpolation using the barycentric coordinate method: ,in are the elevations of the three vertices of the triangle, is the barycentric coordinate, for Elevation interpolation data. Barycentric coordinate interpolation ensures linear continuity and computational efficiency of elevation. Output is the initial 3D terrain mesh model. Model optimization improves surface smoothness and terrain feature fidelity through mesh smoothing and boundary constraints. Mesh smoothing uses the Laplacian operator to adjust vertex positions: ,in and are the vertex coordinates before and after smoothing, Vertex The neighborhood vertex set of is a smoothing factor (usually 0.1-0.5). This method reduces surface noise by neighborhood averaging. To preserve key terrain features (such as slopes and roads), constraints are imposed on vertices on boundaries and feature lines: ,like Boundary, this constraint ensures that important terrain features are not distorted by smoothing. Both smoothness and feature fidelity. Sensor data At this stage, it is used to dynamically correct the model elevation. , according to the time synchronization Adjust the elevation of the corresponding vertex: This step ensures that the model reflects the latest terrain changes. The watershed boundaries and confluence paths identified by remote sensing hydrological data are then superimposed and combined with surface image textures for mapping to form a complete three-dimensional model of the mine surface and hydrological structure. The model accuracy and reliability are verified through quantitative indicators. Error assessment uses an independent verification point set to calculate the model elevation. and actual elevation The root mean square error is: , Provides a quantitative indicator of the overall accuracy of the model. Terrain feature verification includes slope angle checking, calculated as: ,in is the coordinate difference of adjacent points. Or if the feature error exceeds the threshold, the terrain surface reconstruction and model optimization steps are returned to for iterative optimization.

[0090] Furthermore, step S2 includes the following steps:

[0091] Step S21: Analyzing the complex features of the mine terrain based on the three-dimensional mine terrain model to generate complex feature data of the mine terrain;

[0092] Step S22: Designing exploration planning spatial distribution parameters based on the complex terrain feature data of the mine, and performing exploration drilling operations using the exploration planning spatial distribution parameters;

[0093] Step S23: obtaining corresponding exploration drilling point spatial data according to the exploration drilling operation;

[0094] Step S24: collecting exploration borehole point well logging data and exploration borehole point geophysical data using the exploration borehole point spatial data to obtain exploration borehole point well logging data and exploration borehole point geophysical data;

[0095] Step S25: performing exploration borehole rock stratum spatial distribution analysis based on the exploration borehole point spatial data to generate exploration borehole rock stratum spatial distribution data;

[0096] Step S26: Utilizing the exploration drilling point logging data, the exploration drilling point geophysical data, and the exploration drilling point rock stratum spatial distribution data, the mining point rock stratum lithology analysis is performed to generate the mining point rock stratum lithology data.

[0097] In the embodiment of the present invention, in the rare earth mine scenario, the terrain complex feature analysis is performed based on the constructed three-dimensional terrain model of the mine (resolution of 0.5m×0.5m). First, the terrain slope is calculated using the third-order inverse distance square weighted difference method, and the formula is: Slope = in and They are and The rate of elevation change in the direction of the terrain is calculated. At the same time, the terrain curvature is calculated using the second-order difference method, including plane curvature and profile curvature, to identify the rate of change of terrain undulation. Terrain feature points are further extracted, including mountain top points (local elevation maximum), valley points (local elevation minimum), and saddle points (extreme points in two directions), using the 8-neighborhood elevation comparison method. The terrain complexity is calculated using the fractal dimension and the box counting method. The formula is: ,in The side length required to cover the terrain is The final data on complex mine terrain features, including slope, aspect, curvature, terrain relief, and fractal dimension, was generated and stored as raster data (32-bit floating-point data, CGCS2000 coordinate system). Based on this complex terrain data, the spatial distribution parameters of the exploration drill holes were designed. For areas with slopes greater than 35°, a dense grid layout strategy was adopted, with a drill hole spacing of 50m × 50m. For areas with gentle slopes less than 15°, a conventional grid layout was adopted, with a drill hole spacing of 100m × 100m. Furthermore, inspection holes were added in areas with dramatic changes in terrain curvature (absolute curvature greater than 0.05), with the hole spacing increased to 30m. Three-dimensional geostatistical methods were used to determine the drill hole depth parameter, and a orebody thickness variation function model was developed based on the known distribution patterns of rare earth mineralized zones. For shallow oxidized ore layers, the drill hole depth parameter was set to 80m below the terrain elevation; for deep primary ore layers, the drill hole depth was extended to 150m. A total station (angle measurement accuracy ±2″, distance measurement accuracy ±(2mm+2ppm)) is used to stake out the borehole points, and GPS-RTK technology (plane accuracy ±1cm+1ppm, elevation accuracy ±2cm+1ppm) is used to monitor the verticality of the borehole in real time to ensure that the borehole deviation rate is less than 1%. During the drilling operation, a borehole inclinometer (measuring range 0°~180°, accuracy ±0.1°) is used to measure the borehole inclination and azimuth every 5m of drilling, and the three-point inclinometer calculation method is used to obtain the three-dimensional coordinates of the borehole trajectory. After drilling is completed, a downhole television camera system (resolution 1920×1080, illumination ≥2000lux) is used to obtain the borehole wall image, and an image stitching algorithm is used to generate a continuous borehole wall expansion map. At the same time, the core recovery rate during the drilling process is recorded. For intact rock formations, the core recovery rate is required to be greater than 90%, and for broken rock formations, it is required to be greater than 90%. The core length is measured by a laser rangefinder (measuring range 0~50m, accuracy ±3mm), and the corresponding relationship between the core sample and the borehole depth is established in combination with the borehole depth record. Finally, the exploration borehole point spatial data including the borehole coordinates (X, Y, Z), inclination, azimuth, core sampling rate and core sample depth are generated and stored as a three-dimensional point and line dataset (GeoJSON format). Geophysical logging operations are carried out in the borehole using a multi-parameter logging instrument (measuring parameters include natural gamma, resistivity, sonic time difference, etc.). Natural gamma logging uses a scintillation counter (measuring range 0~300API, accuracy ±5API) to measure the radioactivity intensity of the formation, which is used to identify radioactive anomaly layers containing rare earth minerals. Resistivity logging uses a dual lateral logging device (detection depths of 0.3m and 1.2m respectively), with a measurement range of 0.1~10000 , which is used to distinguish the electrical differences between the ore body and the surrounding rock. The acoustic logging uses a single-transmitter and dual-receiver transducer (transmitting frequency 20kHz) to measure the propagation speed of sound waves in the rock (measuring range 1000~6000m / s, accuracy ±10m / s), which is used to analyze the density and porosity of the rock. At the same time, seismic exploration points are arranged around the borehole, and the shallow seismic refraction method (the source is a hammer source, the sampling interval is 0.5ms, and the recording length is 2s) is used to obtain velocity layering information of the underground geological structure. The electromagnetic induction method (frequency range 10Hz~100kHz) is used to measure the conductivity distribution of the underground medium, which is used to delineate the spatial range of rare earth mineralization. Based on the spatial data of the drilling point and the logging geophysical data, the spatial distribution analysis of the rock formation is carried out. First, the logging data is standardized, and the Z-score standardization formula is used: ,in is the original logging value, is the mean, is the standard deviation. Then cluster analysis method (K-means algorithm, cluster number =5) Perform lithology classification on the standardized logging data to determine the logging response characteristics of different lithologies. Use Kriging interpolation to establish the rock interface model, and select the Gaussian model as the variogram model. The formula is: ,in is the semivariance, is the gold nugget value, is the base value, The top and bottom elevations of different rock layers are determined based on the drilling core logging data and logging interpretation results, and three-dimensional rock layer interfaces are generated through Kriging interpolation. The rock layer thickness distribution is calculated, and a rock layer spatial distribution volume model is constructed using three-dimensional geological modeling software (such as GOCAD), with the volume element size set to 5m×5m×2m. Lithological analysis is performed by comprehensively utilizing drilling point logging data, geophysical data, and rock layer spatial distribution data. First, a lithological interpretation template is established. For rare earth mineralized layers, the natural gamma value is greater than 150API and the resistivity is greater than 500. , the acoustic time difference is less than 250μs / m; for granite layer, the natural gamma value is 50~100API, the resistivity is 1000~5000 , acoustic time difference 200~240μs / m; for sandstone layer, natural gamma value 30~60API, resistivity 200~1000 , acoustic transit time 240~300μs / 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 transit time 0.3). The membership degree of each measuring point to different lithologies is calculated using the following formula: ,in is the degree of membership, is the lithologic standard value, To adjust parameters, the lithology identification results are verified and corrected by combining seismic exploration inversion results (wave impedance values) and electromagnetic induction data (conductivity values). Ultimately, lithology data for each mining site is generated, including lithology categories (such as rare earth mineralization, granite, sandstone, claystone, etc.), lithology probabilities (confidence level ≥ 0.8), and lithology physical parameters (porosity, permeability, density). This data is stored as 3D attribute volume data (SEG-Y format) for subsequent lithology mapping and visualization analysis.

[0098] Furthermore, step S3 includes the following steps:

[0099] Step S31: mapping the exploration drilling point spatial data to the three-dimensional terrain model of the mine to perform exploration drilling point mapping conversion to generate exploration drilling point mapping data;

[0100] Step S32: performing exploration borehole plane projection point conversion processing on the exploration borehole point mapping data to generate exploration borehole plane projection point conversion data;

[0101] Step S33: performing exploration borehole elevation constraint adjustment calculation according to the exploration borehole plane projection point conversion data to generate exploration borehole elevation constraint adjustment data;

[0102] Step S34: performing 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;

[0103] Step S35: The lithologic data of the mine point rock formation is transmitted to the three-dimensional terrain model of the mine for lithologic mapping of the mine point rock formation to generate the lithologic mapping data of the mine point rock formation, and the mapping space accuracy of the lithologic mapping data of the mine point rock formation is optimized by calibrating the spatial data of the exploration drilling point to generate the optimized lithologic mapping data of the mine point rock formation.

[0104] In the embodiment of the present invention, in a rare earth mine scenario, mapping transformation is performed based on the spatial data of the exploration drilling point. First, coordinate system 1 is performed to convert the geodetic coordinates (B, L, H) of the drilling point into the local coordinate system (X, Y, Z) of the mine's three-dimensional terrain model. The drilling point is projected onto the surface of the terrain model. For each drilling point (X, Y), its grid cell is determined in the terrain model grid data (resolution 0.5m×0.5m), and the terrain elevation of the point is calculated using bilinear interpolation. : ,in, 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 to the vertex). Finally, the exploration drilling point mapping data containing the drilling plane position (X, Y) and the terrain matching elevation is generated. Based on the exploration drilling point mapping data, the plane projection point conversion is performed, and the Gaussian projection forward calculation is performed to convert the geodetic coordinates (B, L) into Gaussian plane rectangular coordinates (x, y). Then the projection deformation is corrected. According to the deformation of the edge of the projection zone where the mining area is located (length deformation rate ≤ 0.00025), the zoning projection or compensation elevation surface method is adopted. For areas where the length deformation exceeds the threshold, the central meridian is reselected or the compensation elevation surface is set. : ,in, is the average elevation of the survey area, is the length deformation rate, Expressed as increments or differences in spatial coordinates, Represents the original value or target value of the spatial coordinate, = The mean radius of curvature of the Earth (6371 km). Finally, the Gaussian plane coordinates (x, y) and the corresponding geodetic height (h) of the exploration borehole plane projection point conversion data are generated. Based on the plane projection point conversion data, the height constraint adjustment calculation is performed. First, the error equation is constructed, and the indirect adjustment model is used: ,in, is the observation correction vector, is the coefficient matrix, is the parameter vector to be determined (elevation anomaly value), is the observation value vector (the difference between GPS geoid height and level height). Determined based on observation accuracy: ,in, is the unit weight variance, is the covariance matrix of the observations. The least squares principle is used to solve the parameters: ;Unit weighted mean error after adjustment: ,in, is the number of observations, is the necessary number of observations. For rare earth mines, known leveling points (≥3) are set as elevation constraints, and the constraint weight ratio is 10:1. Finally, the exploration borehole elevation constraint adjustment data containing the adjustment elevation value and the accuracy assessment index (mean error ≤±0.05m) are generated and stored as a weighted elevation dataset (XYZQ format, Q is the precision factor). Based on the elevation constraint adjustment data, the borehole point spatial data is calibrated. First, the plane position calibration is performed, and then the adjustment elevation value is replaced by the original point elevation. At the same time, the point calibration error ellipse parameters are calculated, and finally the calibrated exploration borehole point spatial data is generated. Based on the mine point rock stratum lithology data and the calibration borehole point spatial data, lithology mapping and accuracy optimization are performed. First, the lithology data is interpolated using the ordinary Kriging interpolation method: ,in, is the lithologic value of the point to be estimated, It is represented as the coordinate vector of the target spatial point where the lithologic value is to be estimated. For the The known lithologic values ​​of each exploration drilling point, Indicates the The three-dimensional spatial coordinate components of the exploration drilling point, is the weight coefficient, To calibrate the number of drilling points, The weight coefficients are obtained by solving the variogram equations: ,in is the variation function, is the Lagrange multiplier, Indicates the The spatial calibration data of the drilling points is used as control points to optimize the mapping accuracy of the interpolation results. Finally, the optimized lithology mapping data of the mining points is generated, including lithology category, confidence level and spatial accuracy index (point error ≤ ±0.3m).

[0105] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S4 in the embodiment, step S4 includes the following steps:

[0106] Step S41: performing discretization processing on the lithology of the mine point strata according to the optimized mine point strata lithology mapping data to generate discretized data of the lithology of the mine point strata;

[0107] In this embodiment of the present invention, discretization processing is performed based on optimized lithologic mapping data for strata at mining locations (three-dimensional raster data with a resolution of 0.5m×0.5m×0.5m). Lithologic category coding is first performed, mapping continuous lithologic parameters (such as density, resistivity, and natural gamma ray) into discrete lithologic categories. A threshold segmentation method is used to establish a correspondence between lithologic parameters and codes. Then, spatial discretization is performed, using voxelization to divide the three-dimensional space into a regular grid. Each voxel (0.5m side length) is assigned a unique lithologic code and confidence value, ultimately generating discretized lithologic data for the mining locations, including the lithologic code, confidence value, and spatial coordinates.

[0108] Step S42: performing a rock stratum heterogeneous lithologic characteristic analysis based on the discretized rock stratum lithologic data of the mining site to generate rock stratum heterogeneous lithologic characteristic data;

[0109] In the embodiment of the present invention, the rock heterogeneous lithologic characteristics analysis is performed based on the discretized lithologic data of the mine point rock strata. First, the lithologic spatial distribution characteristics are extracted and the lithologic complexity index is calculated. ,in, Lithologic category The ratio within the local region (3×3×3 voxel window), is the total number of lithologic categories.

[0110] Then the lithologic contact relationship is analyzed and the co-occurrence matrix method is used to calculate the lithologic conversion probability between adjacent voxels. For the discretized lithologic data of the mine point rock formation, the lithologic conversion probability of its neighboring voxels is Defined as: ,in, Lithology and lithology The number of adjacent times. Further calculate the lithologic spatial autocorrelation function ,in, For location The lithology code at is the regional average lithology value, is the spatial lag vector, is the number of sample pairs. Finally, a feature dataset containing complexity index, transition probability matrix and autocorrelation function parameters is generated and stored as a multidimensional attribute table.

[0111] Step S43: performing a mine rock stratum lithologic integration guidance analysis based on the rock stratum heterogeneous lithologic characteristic data to generate mine rock stratum lithologic integration guidance data;

[0112] In the embodiment of the present invention, the lithologic blending guidance analysis is performed based on the heterogeneous lithologic characteristic data. First, the lithologic blending field is constructed and the blending intensity is defined. ,in, is the proportional coefficient (taken as 1.0), is the probability of lithologic transition, is the lithologic interface distance, is the characteristic length (take 3m). Then calculate the blending direction vector ,in, is the lithologic complexity gradient vector, is the normal vector of the lithologic interface. A blending guidance model is established, and the finite element method is used to solve the blending field equation: ,in, is the fusion potential function, is the fusion conduction coefficient matrix, is the source term (related to the probability of lithologic transition). The equation is discretized using the variational principle and solved iteratively using the conjugate gradient method. Ultimately, a guidance data volume containing the blending intensity, direction vector, and blending potential function is generated and stored as a vector field file.

[0113] Step S44: extracting the heterogeneous lithologic differentiation nodes of the strata within the mine site based on the heterogeneous lithologic characteristic data to obtain heterogeneous lithologic differentiation node data; performing heterogeneous stratum morphology fitting processing on the heterogeneous lithologic differentiation node data to generate heterogeneous stratum morphology fitting data; and extracting the boundary structure of the strata at the mine site based on the discretized lithologic data to generate the boundary structure data of the strata.

[0114] In this embodiment of the present invention, differential node extraction is performed based on the heterogeneous lithologic data of rock formations. Taking the contact area between a rare earth mineralized zone and granite as an example, spatial abrupt change regions are first identified using the lithologic complexity index. If the lithologic complexity index of a voxel is significantly higher than that of the surrounding area (e.g., more than twice the average), it is marked as a differential node. For example, at the edge of the mineralized zone, the lithologic complexity index increases sharply due to the rapid transition from a single granite to a complex mineralized mixture. Voxels at these locations are preferentially extracted as differential nodes. Subsequently, the heterogeneous rock formation morphology is fitted. The extracted differential nodes are used as control points for interpolation using a B-spline surface. At the interface between the mineralized zone and the surrounding rock, the weights and positions of the control points are adjusted to ensure that the B-spline surface closely matches the actual lithologic variation trends. For example, in three-dimensional space, the density of the control points is dynamically adjusted based on the severity of the lithologic variation: control points are sparsely distributed within the mineralized zone and densely distributed at the interface with the surrounding rock, ensuring that the surface accurately represents 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 (for example, from a granite code of 1 to a mineralized layer code of 3), the location of the boundary point is determined through linear interpolation. For example, in an 8×8×8 voxel cube, if the endpoints of an edge are granite and a mineralized layer, respectively, the point on the edge with a lithology code equal to the threshold (for example, 2) is found as the boundary point. All boundary points are connected to form a triangular mesh, ultimately constructing the complete rock layer boundary structure, such as the 3D outline of the mineralized zone and the interface between different lithology layers.

[0115] Step S45: performing stratum lithologic integration characteristic analysis based on the mine stratum lithologic integration guidance data to generate stratum lithologic integration characteristic data;

[0116] In this embodiment of the present invention, an analysis of lithologic intermixing characteristics is conducted based on lithologic intermixing guidance data. An intermixing transition specificity index is calculated in the contact area between the rare earth mineralized zone and the sandstone shale. The intensity of lithologic intermixing is assessed by measuring the spatial variation rate of intermixing intensity (i.e., the intermixing intensity gradient) and combining it with the variation rate of the intermixing potential function in the normal direction of the interface. For example, in an area where mineralizing fluids intrude into sandstone shale, a large intermixing intensity gradient and significant variation in the intermixing potential function indicate active lithologic intermixing in this area, potentially leading to the formation of a high-grade mineralized zone. When establishing the intermixing guidance mapping relationship, the relationship between parameters such as lithologic complexity, intermixing intensity, and contact angle is analyzed. For example, at the interface between the mineralized zone and the surrounding rock, a near-vertical contact angle and high intermixing intensity often correspond to a primary migration pathway for the mineralizing fluid; a flat contact interface may indicate lateral diffusion of mineralization. By statistically analyzing the parameter relationships of a large number of sample points, intermixing characteristic patterns for different lithologic combinations are summarized. When performing lithologic guidance gradient analysis, the gradient amplitude and orientation angle of the intermixing potential function in three-dimensional space are calculated. Areas with large gradients and consistent orientations along the direction of a vein indicate the dominant direction of mineralization. For example, if the gradient primarily runs from northeast to southwest, the vein is likely to extend in that direction, providing spatial guidance for subsequent exploration.

[0117] Step S46: performing a lithologic structure analysis of the mine rock strata based on the lithologic blending characteristic data of the rock strata and the boundary structure data of the mine rock strata to generate lithologic structure data of the mine rock strata.

[0118] In an embodiment of the present invention, lithologic structure analysis is carried out based on the blending feature data and boundary structure data. In a large rare earth mining area, rock strata are first stratified. Areas with similar blending characteristics are divided into the same lithologic layer. For example, voxels with similar mineralization intensity, blending direction and transition specificity index are clustered into mineralized layers. In this way, the entire mining area is divided into different layers such as topsoil layer, mine ore body layer, semi-weathered layer, weathered mineral-free zone and bedrock layer. When constructing the lithologic structure model, a geostatistical simulation method is used. Based on the known drilling data, a continuous lithologic attribute field is generated by sequential Gaussian simulation. For example, for the grade of rare earth elements, the grade distribution is simulated in three-dimensional space with the measured drilling data as a constraint condition, and a grade isosurface is generated to intuitively display the spatial distribution of high-grade mineralized bodies. When calculating the structural surface attitude parameters, the extracted rock stratum boundary triangular mesh is analyzed. Using the spherical projection method, the normal vector of each triangle is projected onto the unit sphere to calculate the inclination and dip. For example, by analyzing the occurrence parameters of a large number of triangular surfaces at the interface between the mineralized zone and the surrounding rock, the team determined that the average dip angle of the contact surface was 65° and the dip was 30° northeast. These parameters are of great significance for understanding the spatial distribution of the mineralized body and subsequent mining design. Ultimately, the layered structure, attribute simulation results, and structural surface parameters were integrated into a complete lithologic and structural dataset, forming a three-dimensional geological model that intuitively displays the lithologic distribution and structural characteristics within the rare earth mine, such as the topsoil layer, ore body layer, semi-weathered layer, and bedrock layer in an open-type mine, or the topsoil layer, ore body layer, and groundwater structure in a fully covered mine.

[0119] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S45 in the embodiment, step S45 includes the following steps:

[0120] Step S451: performing lithologic transition specificity analysis based on the stratum heterogeneous lithologic characteristic data to generate lithologic transition specificity data;

[0121] In the embodiment of the present invention, the lithologic transition specificity analysis is performed based on the heterogeneous lithologic characteristic data of the rock formation, for example, the lithologic transition specificity analysis is performed based on the location of the collected mineral points, and the lithologic difference index of adjacent voxels is calculated. ,in, 、 The lithologic code, 、 for 、 The lithology quantification value of the lithology code. Set the threshold , identifying the transition zone between the mineralized layer and the surrounding rock. Furthermore, by analyzing the lithologic transition gradient and lithologic transition width, and analyzing the lithologic conversion frequency between adjacent voxels through the lithologic difference index, lithologic transition gradient, and lithologic transition width of adjacent voxels, the contact area between the rare earth mineralized layer and the sandstone is identified as an active lithologic blending zone. The proportion of voxels with different lithologic combinations in the contact area is counted. 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 lithologic complexity index of these voxels is significantly higher than that of other areas, the area can be determined to be a key area of ​​lithologic blending transition. At the same time, the spatial change rate of lithologic parameters is analyzed in the area where the rare earth mineralized layer transitions to the sandstone to obtain lithologic blending transition-specific data, including information such as the change rate, change amplitude, and spatial coordinates of the key transition area.

[0122] Step S452: establishing a mapping relationship of lithologic blending guidance based on the lithologic discretization data of the mining site and the lithologic blending transition specificity data, and generating a lithologic blending guidance model;

[0123] In this embodiment of the present invention, a lithologic integration guidance model is constructed by combining discretized lithologic data and lithologic integration transition-specific data at mining sites. Taking the contact area between a rare earth mineralized layer and sandstone as an example, the lithologic code and spatial coordinates of each voxel in the discretized data are associated with the variation characteristics in the lithologic integration transition-specific data. For example, for a voxel with a lithologic code of rare earth mineralized layer and located within a key region marked by the lithologic integration transition-specific data, a correspondence is established between the spatial coordinates of that voxel and information such as the rate and direction of change in lithologic integration. By analyzing a large amount of sample data, the integration patterns of different lithologic combinations at different spatial locations are summarized, forming a set of mapping rules. For example, within a specific depth range (-50 meters to -80 meters), when a rare earth mineralized layer is adjacent to sandstone, the mineralized material tends to diffuse along a 30° northeast direction. Based on these rules, a lithologic integration guidance model is constructed, which can predict the trend and direction of lithologic integration based on the input lithologic and spatial information.

[0124] Step S453: performing lithologic guidance analysis on the discretized lithologic data of the mining point rock formation to generate discretized lithologic guidance data; performing lithologic guidance gradient feature analysis on the discretized lithologic guidance data to generate lithologic guidance gradient feature data;

[0125] In an embodiment of the present invention, a lithologic guidance analysis is performed on the discretized lithologic data of the rock formations at the mine site. In the three-dimensional model of the rare earth mine, for each discretized voxel, the lithologic 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, and the adjacent voxel on the west side is still a mineralized layer, and the lithologic complexity of the sandstone area on the east side is relatively low, combined with the lithologic blending transition specificity data, it can be known that this direction is the potential direction of diffusion of mineralized materials, and this direction information is recorded as discretized lithologic guidance data. Furthermore, a lithologic guidance gradient feature analysis is performed based on the discretized lithologic guidance data. The degree of change of the lithologic guidance direction at different positions is calculated by derivation. On the contact zone between the mineralized layer and the sandstone, a group of voxels is selected every 5 meters to compare the differences in the lithologic guidance directions of adjacent groups of voxels. If it is found that the lithologic orientation direction gradually changes from 30° northeast to 45° northeast from the center to the edge of the mineralized layer, the changing gradient of the lithologic orientation in the area is calculated, and the information such as the gradient size and direction change trend is generated into lithologic orientation gradient characteristic data.

[0126] Step S454: transmitting the mine point rock stratum lithologic discretization data and the rock stratum steering gradient characteristic data to the rock stratum lithologic integration steering model to perform mine rock stratum lithologic integration steering analysis to generate mine rock stratum lithologic integration steering data.

[0127] In the embodiment of the present invention, the lithologic discretization data of the mine point rock formation and the lithologic guidance gradient characteristic data are input into the established rock formation lithologic blending guidance model. Taking an unexplored area of ​​a rare earth mine as an example, the lithologic codes and spatial coordinates of some voxels in the area are known. These discretized data are input into the model. At the same time, combined with the lithologic guidance gradient characteristic data of the area, the model analyzes and predicts the specific situation of lithologic blending in the area based on the established mapping relationship and blending law. For example, by calculating the blending intensity between different rock layers ,in, is the proportional adjustment coefficient, is the gradient operator of the steering vector perpendicular to the lithologic interface, Expressed as the lithologic orientation vector at point The spatial rate of change at for point The vertical distance to the reference lithologic interface, The effective range of lithologic intermixing is determined by the gradient amplitude's directional rate of change, and the intermixing correction coefficient for each stratum is calculated. Ultimately, the model generates guidance data for lithologic intermixing in the mine. The model outputs guidance data for lithologic intermixing, including information on intermixing trends, possible intermixing boundaries, and potential mineralized areas, providing a basis for decision-making in further exploration and mining.

[0128] Furthermore, step S452 includes the following steps:

[0129] According to the specific data of lithologic blending transition, the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are analyzed, and the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are obtained respectively;

[0130] Calculate the difference in physical parameters of adjacent lithologies based on the discretized lithologic data of the mining sites;

[0131] Based on the difference in physical parameters of adjacent lithologies, a heterogeneous lithologic transition and blending tree structure is established, and the lithologic characteristic blending transition intensity is analyzed according to the lithologic characteristic gradual blending transition coefficient and the lithologic characteristic sudden blending transition coefficient to generate lithologic characteristic blending transition intensity data. The mapping relationship of the lithologic blending guidance of the stratum is analyzed through the heterogeneous lithologic transition and blending tree structure and the lithologic characteristic blending transition intensity data to establish a stratum lithologic blending guidance model.

[0132] In the embodiment of the present invention, the contact area between the mineralized layer and the sandstone (such as the coordinate point (2250, 1750, -100)) is used to collect lithologic physical parameters (natural gamma value) every 0.5m along the vertical interface direction (such as the positive direction of the X axis). GR ,density ). Calculate the parameter change rate of adjacent sampling points (Unit: API / m), for example, from the mineralized layer ( GR =180API) to sandstone ( GR =80API) transition, the change rate in a certain interval is -200API / m. Normalize all the change rates to the [0,1] interval to obtain the gradual blending transition coefficient , here =250API / m, then the interval =0.8. In the contact zone between the mineralized layer and the granite near the fault, the absolute difference of the parameters of adjacent voxels is calculated: , normalize all differences to the interval [0,1] to obtain the sudden transition coefficient , here ,but =0.67. The parameter change rate of adjacent sampling points corresponding to the discretized data of rock strata at mining sites and the absolute difference of the parameters of adjacent voxels , for each voxel, calculate the physical parameter difference between it and its neighboring voxels. Build a tree structure: each node represents a lithology (e.g., sandstone code 2, granite code 1). The edge weight is the weighted sum of the physical parameter differences of adjacent lithologies. For example, the edge weight connecting the mineralized layer and the sandstone is: , where 0.6 and 0.4 are the parameter change rates of adjacent sampling points GR The absolute difference between the parameters of adjacent voxels The weights are 100 and 0.3 as normalized reference values. Starting from the root node, all adjacent lithology nodes are connected, and for each child node, the adjacent lithology nodes are recursively connected, eventually forming a multi-level tree structure. Each layer of nodes represents the transition relationship between different lithologies. Combining the tree structure with the transition coefficient, the blending transition strength is calculated. (1-node level / maximum level) + (node ​​level / maximum level), the blending strength of all node pairs is stored as a matrix to obtain Lithology and The lithologic blending guidance vector is calculated for each voxel based on its position in the tree structure and the blending transition strength of the adjacent connecting edges, and the weight is determined according to the proportion of the blending transition strength of the connecting edges. The lithologic blending guidance vector is obtained by vector synthesis. The above calculations are performed on all voxels to establish a mapping relationship of the lithologic blending guidance of the strata in the entire mining area, thereby generating a lithologic blending guidance model. This model can be used to predict the direction and trend of lithologic blending in rare earth mines, and provide clear feedback on the specific content of each layer in the mine's three-dimensional visualization.

[0133] Furthermore, the mine rock stratum lithologic structure data in step S46 includes the mine topsoil layer structure, the mine ore body layer structure, the mine semi-weathered layer structure, the mine weathered non-ore zone structure, the mine bedrock layer structure and the mine groundwater structure.

[0134] Furthermore, step S5 includes the following steps:

[0135] Using the boundary structure data of the mine rock strata to process the lithologic structure data of the mine rock strata, a preliminary layered visualization 3D model of the mine rock strata is generated;

[0136] According to the lithologic blending characteristic data of the rock strata, the lithologic blending differentiated rendering processing of the mine rock strata is performed on the mine rock strata visualization three-dimensional model to generate the mine rock strata visualization three-dimensional rendering model.

[0137] In an embodiment of the present invention, preliminary layered visualization and 3D modeling is performed based on generated mine rock stratum boundary structure data and mine rock stratum lithologic structure data. Based on the mine rock stratum boundary structure data, this data records the spatial interfaces of different rock strata in the form of triangular meshes, such as the interface between the rare earth mineralized layer and sandstone and granite. This boundary structure data is imported into 3D modeling software, and the mine rock stratum lithologic structure data (including layered information such as topsoil, ore body, semi-weathered layer, weathered unmineralized zone, and bedrock layer) is spatially partitioned using the boundaries as constraints. For example, within the coordinates X: 1000-1500m, Y: 800-1200m, and Z: -80-0m, the software accurately defines the 3D spatial extent of the rare earth mineralized layer based on the boundary structure data, while placing the topsoil layer at the top and the bedrock layer at the bottom, forming a preliminary 3D layered structure model. Each rock layer is distinguished by a different color, with the topsoil layer represented in yellow and the rare earth mineralized layer in red. This allows the 3D visualization model of the mine rock layer to intuitively present the spatial distribution relationships of the various rock layers. Based on the lithologic intermixing data, the preliminary 3D visualization model of the mine rock layer is rendered with differentiated lithologic intermixing. In the interface between the rare earth mineralized layer and the sandstone, the rendering effect is adjusted based on the intermixing intensity and direction recorded in the lithologic intermixing data. In areas with high intermixing intensity, texture complexity and color transitions are increased. For example, at the interface between the mineralized layer and the sandstone, red (mineralized layer) and gray (sandstone) are blended in a gradient to simulate actual lithologic transitions. In areas with low intermixing intensity, clearer boundaries and a single color are used for rendering. Furthermore, based on the lithologic guidance gradient data, the rendering emphasizes the direction of lithologic variation, such as along the diffusion direction of the mineralized material, creating a visual effect of extended color and texture. After this series of processing, the final generated three-dimensional visualization rendering model of the mining rock layer can not only clearly display the spatial position of each rock layer, but also vividly present the detailed characteristics of the rock blending through differentiated rendering, providing intuitive and accurate visualization data support for mine geological analysis, mining planning, etc.

[0138] This specification provides a three-dimensional mine visualization system for executing the above-mentioned three-dimensional mine visualization method. The three-dimensional mine visualization system includes:

[0139] The mine 3D terrain construction module is used to collect multi-source monitoring data of the mine area using monitoring equipment; construct a 3D model of the mine multi-source monitoring based on the mine multi-source monitoring data to obtain a 3D terrain model of the mine;

[0140] The mine exploration operation module is used to design exploration planning spatial distribution parameters based on the mine's three-dimensional terrain model and execute exploration drilling operations using the exploration planning spatial distribution parameters; obtain corresponding exploration drilling point spatial data based on the exploration drilling operations; perform lithology analysis of the mine point rock formation based on the exploration drilling point spatial data and generate lithology data of the mine point rock formation;

[0141] The mine point rock layer analysis module is used to map the mine point rock layer lithology data to the mine three-dimensional terrain model to obtain the mine point rock layer lithology mapping data; the mine point rock layer lithology mapping data is optimized for mapping space accuracy to generate optimized mine point rock layer lithology mapping data;

[0142] The mine rock formation lithologic structure analysis module is used to perform mine rock formation lithologic integration guidance analysis based on the optimized mine point rock formation lithologic mapping data to generate mine rock formation lithologic integration guidance data; perform mine rock formation boundary structure analysis by optimizing the mine point rock formation lithologic mapping data to generate mine rock formation boundary structure data; perform rock formation lithologic integration feature analysis on the mine rock formation boundary structure data based on the mine rock formation lithologic integration guidance data to generate rock formation lithologic integration feature data; perform mine rock formation lithologic structure analysis based on the rock formation lithologic integration feature data and the mine rock formation boundary structure data to generate mine rock formation lithologic structure data;

[0143] The mine 3D visualization module is used to perform 3D modeling and processing of the lithologic blending and differentiated rendering of the mine rock strata based on the lithologic structure data of the mine rock strata, and generate a 3D visualization rendering model of the mine rock strata.

[0144] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0145] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional mine visualization method, characterized in that: The following steps are involved: Step S1: using monitoring equipment to collect multi-source monitoring data of the mine area; Construct a three-dimensional model of mine multi-source monitoring based on mine multi-source monitoring data to obtain a three-dimensional terrain model of the mine; Step S2: Designing exploration planning spatial distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operations using the exploration planning spatial distribution parameters; obtaining corresponding exploration drilling point spatial data based on the exploration drilling operations; performing lithology analysis of the mine point rock formations based on the exploration drilling point spatial data to generate lithology data of the mine point rock formations; Step S3: Mapping the lithologic data of the mine point rock formation to the three-dimensional terrain model of the mine to obtain the lithologic mapping data of the mine point rock formation; Optimize the mapping space accuracy of the lithology mapping data of the mine points to generate optimized lithology mapping data of the mine points; Step S3 includes: Step S31: mapping the exploration drilling point spatial data to the three-dimensional terrain model of the mine to perform exploration drilling point mapping conversion to generate exploration drilling point mapping data; Step S32: performing 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: performing exploration borehole elevation constraint adjustment calculation according to the exploration borehole plane projection point conversion data to generate exploration borehole elevation constraint adjustment data; Step S34: performing 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: transmitting the lithologic data of the mine point strata to the three-dimensional terrain model of the mine for lithologic mapping of the mine point strata to generate the lithologic mapping data of the mine point strata; and optimizing the mapping space accuracy of the lithologic mapping data of the mine point strata by calibrating the spatial data of the exploration drilling points to generate the optimized lithologic mapping data of the mine point strata; Step S4: performing a mine rock stratum lithologic integration guidance analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum lithologic integration guidance data; performing a mine rock stratum boundary structure analysis based on the optimized mine point rock stratum lithologic mapping data to generate mine rock stratum boundary structure data; performing a rock stratum lithologic integration characteristic analysis on the mine rock stratum boundary structure data based on the mine rock stratum lithologic integration guidance data to generate rock stratum lithologic integration characteristic data; performing a mine rock stratum lithologic structure analysis based on the rock stratum lithologic integration characteristic data and the mine rock stratum boundary structure data to generate mine rock stratum lithologic structure data; Step S5: performing lithologic blending and differentiated rendering three-dimensional modeling processing of the mine rock strata based on the lithologic structure data of the mine rock strata to generate a three-dimensional visual rendering model of the mine rock strata.

2. The three-dimensional mine visualization method according to claim 1, characterized in that: The monitoring equipment in step S1 includes satellite equipment, drone aerial survey equipment, laser scanning equipment, and sensor monitoring equipment. Step S1 includes the following steps: Step S11: using satellite equipment to collect mine hydrological remote sensing data of the mine area to generate mine hydrological remote sensing data; Step S12: using unmanned aerial survey equipment to collect mine surface image data of the mine area to generate mine surface image data; Step S13: using laser scanning equipment to collect mine laser data in the mine area to generate mine laser data; Step S14: using sensor monitoring equipment to collect mine sensor monitoring data in the mine area to generate mine sensor monitoring data; Step S15: marking the mine hydrological remote sensing data, the mine surface image data, the mine laser data, and the mine sensor monitoring data as preliminary mine multi-source monitoring data; performing data preprocessing on the preliminary mine multi-source monitoring data to obtain the mine multi-source monitoring data; Step S16: performing mine monitoring space matching processing on the mine multi-source monitoring data to generate mine monitoring space matching data; Step S17: performing 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: establishing a mine surface triangulated mesh based on the mine multi-source monitoring fusion data, and performing elevation interpolation processing on the triangle vertices of the mine surface triangulated mesh to obtain mine surface interpolation data; performing mine terrain surface reconstruction processing based on the mine surface interpolation data to obtain mine terrain surface modeling data; Step S19: performing mine terrain surface optimization processing on the mine terrain surface modeling data to establish a three-dimensional mine terrain model.

3. The three-dimensional mine visualization method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Analyzing the complex features of the mine terrain based on the three-dimensional mine terrain model to generate complex feature data of the mine terrain; Step S22: Designing exploration planning spatial distribution parameters based on the complex terrain feature data of the mine, and performing exploration drilling operations using the exploration planning spatial distribution parameters; Step S23: obtaining corresponding exploration drilling point spatial data according to the exploration drilling operation; Step S24: collecting exploration borehole point well logging data and exploration borehole point geophysical data using the exploration borehole point spatial data to obtain exploration borehole point well logging data and exploration borehole point geophysical data; Step S25: performing exploration borehole rock stratum spatial distribution analysis based on the exploration borehole point spatial data to generate exploration borehole rock stratum spatial distribution data; Step S26: Utilizing the exploration drilling point logging data, the exploration drilling point geophysical data, and the exploration drilling point rock stratum spatial distribution data, the mining point rock stratum lithology analysis is performed to generate the mining point rock stratum lithology data.

4. The three-dimensional mine visualization method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing discretization processing on the lithology of the mine point strata according to the optimized mine point strata lithology mapping data to generate discretized data of the lithology of the mine point strata; Step S42: performing a rock stratum heterogeneous lithologic characteristic analysis based on the discretized rock stratum lithologic data of the mining site to generate rock stratum heterogeneous lithologic characteristic data; Step S43: performing a mine rock stratum lithologic integration guidance analysis based on the rock stratum heterogeneous lithologic characteristic data to generate mine rock stratum lithologic integration guidance data; Step S44: extracting the heterogeneous lithologic differentiation nodes of the strata within the mine site based on the heterogeneous lithologic characteristic data to obtain heterogeneous lithologic differentiation node data; performing heterogeneous stratum morphology fitting processing on the heterogeneous lithologic differentiation node data to generate heterogeneous stratum morphology fitting data; and extracting the boundary structure of the strata at the mine site based on the discretized lithologic data to generate the boundary structure data of the strata. Step S45: performing stratum lithologic integration characteristic analysis based on the mine stratum lithologic integration guidance data to generate stratum lithologic integration characteristic data; Step S46: performing a lithologic structure analysis of the mine rock strata based on the lithologic blending characteristic data of the rock strata and the boundary structure data of the mine rock strata to generate lithologic structure data of the mine rock strata.

5. The three-dimensional mine visualization method according to claim 4, characterized in that: Step S45 includes the following steps: Step S451: performing lithologic transition specificity analysis based on the stratum heterogeneous lithologic characteristic data to generate lithologic transition specificity data; Step S452: establishing a mapping relationship of lithologic blending guidance based on the lithologic discretization data of the mining site and the lithologic blending transition specificity data, and generating a lithologic blending guidance model; Step S453: performing lithologic guidance analysis on the discretized lithologic data of the mining point rock formation to generate discretized lithologic guidance data; performing lithologic guidance gradient feature analysis on the discretized lithologic guidance data to generate lithologic guidance gradient feature data; Step S454: transmitting the mine point rock stratum lithologic discretization data and the rock stratum steering gradient characteristic data to the rock stratum lithologic integration steering model to perform mine rock stratum lithologic integration steering analysis to generate mine rock stratum lithologic integration steering data.

6. The three-dimensional mine visualization method according to claim 5, characterized in that: Step S452 includes the following steps: According to the specific data of lithologic blending transition, the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are analyzed, and the gradual blending transition coefficient and the abrupt blending transition coefficient of lithologic characteristics are obtained respectively; Calculate the difference in physical parameters of adjacent lithologies based on the discretized lithologic data of the mining sites; Based on the difference in physical parameters of adjacent lithologies, a heterogeneous lithologic transition and blending tree structure is established, and the lithologic characteristic blending transition intensity is analyzed according to the lithologic characteristic gradual blending transition coefficient and the lithologic characteristic sudden blending transition coefficient to generate lithologic characteristic blending transition intensity data. The mapping relationship of the lithologic blending guidance of the stratum is analyzed through the heterogeneous lithologic transition and blending tree structure and the lithologic characteristic blending transition intensity data to establish a stratum lithologic blending guidance model.

7. The three-dimensional mine visualization method according to claim 4, characterized in that: The mine rock stratum lithologic structure data in step S46 includes the mine topsoil layer structure, the mine ore body layer structure, the mine semi-weathered layer structure, the mine weathered non-ore zone structure, the mine bedrock layer structure and the mine groundwater structure.

8. The three-dimensional mine visualization method according to claim 4, characterized in that: Step S5 includes the following steps: Using the boundary structure data of the mine rock strata to process the lithologic structure data of the mine rock strata, a preliminary layered visualization 3D model of the mine rock strata is generated; According to the lithologic blending characteristic data of the rock strata, the lithologic blending differentiated rendering processing of the mine rock strata is performed on the mine rock strata visualization three-dimensional model to generate the mine rock strata visualization three-dimensional rendering model.

9. A three-dimensional mine visualization system, characterized in that: For executing the mine 3D visualization method according to claim 1, the mine 3D visualization system comprises: The mine 3D terrain construction module is used to collect multi-source monitoring data of the mine area using monitoring equipment; construct a 3D model of the mine multi-source monitoring based on the mine multi-source monitoring data to obtain a 3D terrain model of the mine; The mine exploration operation module is used to design exploration planning spatial distribution parameters based on the mine's three-dimensional terrain model and execute exploration drilling operations using the exploration planning spatial distribution parameters; obtain corresponding exploration drilling point spatial data based on the exploration drilling operations; perform lithology analysis of the mine point rock formation based on the exploration drilling point spatial data and generate lithology data of the mine point rock formation; The mine point rock layer analysis module is used to map the mine point rock layer lithology data to the mine three-dimensional terrain model to obtain the mine point rock layer lithology mapping data; the mine point rock layer lithology mapping data is optimized for mapping space accuracy to generate optimized mine point rock layer lithology mapping data; The mine rock formation lithologic structure analysis module is used to perform mine rock formation lithologic integration guidance analysis based on the optimized mine point rock formation lithologic mapping data to generate mine rock formation lithologic integration guidance data; perform mine rock formation boundary structure analysis by optimizing the mine point rock formation lithologic mapping data to generate mine rock formation boundary structure data; perform rock formation lithologic integration feature analysis on the mine rock formation boundary structure data based on the mine rock formation lithologic integration guidance data to generate rock formation lithologic integration feature data; perform mine rock formation lithologic structure analysis based on the rock formation lithologic integration feature data and the mine rock formation boundary structure data to generate mine rock formation lithologic structure data; The mine 3D visualization module is used to perform 3D modeling and processing of the lithologic blending and differentiated rendering of the mine rock strata based on the lithologic structure data of the mine rock strata, and generate a 3D visualization rendering model of the mine rock strata.

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