Digital Mine Modeling Method and System Based on Complex Structural Conditions of Large Mining Areas

Abstract

This invention discloses a digital mine modeling method and system based on the complex structural conditions of large mining areas, relating to the fields of mine safety and digital technology. The modeling method includes the following steps: acquiring geological data to form a structured original geological database; employing block modeling and integrating drilling, geophysical, and tunneling data, using the Monte Carlo method to estimate uncertainties in un-drilled areas to form a complete block geological model; subdividing irregularly distributed strata, importing the block model into 3D modeling software, generating continuous triangular mesh surfaces, determining topological structure and surface features, and outputting mesh data for numerical simulation; and applying FLAC to the mine-scale model. 3D -PFC 3D The continuous-discrete coupling method completes static equilibrium, micro-particle calibration, and dynamic load simulation; based on modeling and simulation analysis, an iteratively updated digital twin mine is formed, realizing virtual-real mapping and dynamic parameter correction.

Description

Technical Field

[0001] This invention relates to the field of mine safety and digital technology, and in particular to a digital mine modeling method and system based on the complex structural conditions of large mining areas. Background Technology

[0002] Deep, large-scale working faces often encounter complex geological structures such as large faults, folds, and weak interlayers, which can easily trigger dynamic disasters such as rockbursts, posing a significant challenge to mine safety. Existing 3D geological modeling technologies often face the following technical bottlenecks when dealing with the strong nonlinearity, heterogeneity, and multi-scale characteristics exhibited by these complex structures, severely restricting the accuracy of mine modeling and the reliability of disaster prediction. For example, in unexplored areas or structurally complex regions, ordinary kriging interpolation based on borehole data struggles to accurately characterize the heterogeneity and discontinuities of geological bodies, leading to model distortion; the difficulty in handling the modeling boundaries of large-scale faults and folds results in mesh distortion, making it difficult to ensure continuous and smooth block boundaries, affecting the convergence and accuracy of subsequent numerical calculations; and the integration of the established geological model with numerical analysis software (such as FLAC) presents challenges. 3D PFC 3D The data formats are incompatible, requiring tedious manual conversion, which is inefficient and prone to errors. At the same time, most existing models are static and cannot be dynamically updated and corrected based on real-time downhole monitoring data (such as microseismic and stress data). They lack unified geometric constraints and physical consistency, resulting in limited model stability and generalization ability, making it difficult to achieve early warning of disaster evolution.

[0003] A search of existing technologies revealed Chinese patent application publication number CN118916438A, entitled "A Method and System for Constructing a Digital Twin of a Mine." This patent application proposes a scheme for constructing a digital twin of a mine based on sensor data and modeling algorithms. It establishes a virtual model by collecting mine environment and equipment operation data and dynamically maps it to the actual operating state. While this method possesses certain real-time and visualization features, its shortcomings include: a lack of refined expression of geological structures, faults, and folds under complex conditions in large mining areas; a lack of modeling mechanisms for geological heterogeneity and spatially segmented structures; and a reliance on static monitoring data in its modeling process, failing to establish a dynamic coupling relationship between geological information, mechanical response, and disaster evolution, making it difficult to accurately reflect the multi-field coupled behavior of the mine. Furthermore, this invention lacks a two-way mapping model between data and physical mechanisms, hindering iterative model correction and dynamic reproduction of the disaster evolution process, thus limiting the model's application effectiveness in analyzing the mechanism of rockburst and constructing digital twin mines.

[0004] Therefore, there is an urgent need for a geological modeling method and system that can accurately characterize the complex geological structure of large mining areas, quantify modeling uncertainties, and seamlessly integrate with numerical simulation and dynamic monitoring to support the intelligent construction and safe and efficient production of mines. Summary of the Invention

[0005] This solution addresses the problems and needs raised above by proposing a digital mine modeling method and system based on the complex structural conditions of large mining areas. The above technical objectives can be achieved by adopting the following technical features, and it also brings about several other technical effects.

[0006] One objective of this invention is to propose a digital mine modeling method based on the complex structural conditions of large mining areas, comprising the following steps: S10: Acquire geological data, determine the modeling scope and geometric constraints based on the geological data of the mining area, and form a structured original geological database; S20: Using block modeling under a unified coordinate system, and integrating drilling, geophysical and tunneling data from the structured original geological database, the Monte Carlo method is used to estimate the uncertainty of the un-drilled area, forming a complete block geological model; S30: The irregularly distributed ground strata are segmented, the block geological model is imported into the 3D modeling software, the Delaunay triangulation is used to generate continuous triangular mesh surfaces, the topology and surface features are determined, and the mesh data for numerical simulation is output. S40: Using FLAC on a mine-scale model 3D -PFC 3D The continuous-discrete coupling method completes the simulation of static equilibrium, micro-granular calibration and dynamic load conditions, and outputs the time-series fields of stress, strain, displacement and energy to reproduce the evolution process of disaster initiation-triggering-causing disaster. S50: Based on modeling and simulation analysis, a digital twin mine model that can be iteratively updated is formed to realize virtual-real mapping and dynamic parameter correction. Ultimately, the digital twin mine model is used as the basis for disaster mechanism research, risk zoning assessment, and prevention and control decisions.

[0007] Furthermore, the digital mine modeling method based on the complex structural conditions of large mining areas according to the present invention may also have the following technical features: In one example of the present invention, in step S10, geological data is acquired, and the modeling scope and geometric constraints are determined based on the geological data of the mining area to form a structured original geological database. This specifically includes the following steps: S11: Obtain mining area GPS data through field surveys for unified spatial reference and surface deformation constraints, including ground reference point coordinates, boundary range, surface subsidence monitoring points and displacement; surface DEM data for describing surface topographic relief and serving as geometric constraints for modeling boundaries, including elevation points, contour lines and topographic slope information. S12: Collect geological maps, profiles, borehole data and mining engineering drawings to reflect the distribution of strata, lithological types, faults, collapse column structures, coal and rock layer thickness, roof and floor burial depth, undulation morphology, and the formation time, range and coal mining volume of goaf. S13: Unify the format, coordinate system and time reference of multi-source geological data formed by mining area GPS data, surface DEM data, geological maps, profile maps, borehole data and mining engineering maps, and remove and standardize outliers. S14: Based on the unified formatted multi-source geological data, establish a hierarchical database structure, including multi-dimensional spatial information and corresponding attribute data for storing and managing mine points, lines, surfaces and volumes. The multi-dimensional spatial information and corresponding attribute data include: data source layer, spatial index layer, object organization layer, attribute relationship layer and time management layer.

[0008] In one example of the present invention, step S20 specifically includes the following steps: S21: Under a unified coordinate system, the study area is divided into several regular blocks or irregular body units based on faults, fold zones, lithological abrupt change surfaces, sedimentary contact interfaces, and mining disturbance boundaries. S22: By comprehensively employing seismic reflection wave method, transient electromagnetic method, georesistivity tomography, gravity and magnetic joint inversion and high-density electrical method, the structure of concealed faults, collapse columns, folds and interlayer slip zones in the strata is identified, and a geometric boundary framework of underground space is established. S23: Based on drilling data, obtain the thickness of coal and rock strata, the burial depth of the top and bottom plates and the undulation characteristics. Use the Monte Carlo method to estimate the continuity of uncertain information in the undrilled area. Simulate the probability field of the distribution of strata thickness and elevation through multiple random samplings, and calculate the mean, variance and confidence interval to reflect the spatial variability of geological information. S24: Combining geological rules and geophysical boundaries, the estimation results are integrated into the three-dimensional spatial inversion process. The internal structure of faults and collapse columns and the interlayer transition zones are completed with attribute completion and stratum smoothing. Thus, within the established underground spatial geometric boundary framework, the spatial extension and volume reconstruction of stratum thickness, lithology and physical parameters are realized, forming a spatially continuous and topologically complete block geological model.

[0009] In one example of the present invention, step S23, specifically the steps of using the Monte Carlo method to perform continuity estimation, include: S231: Parameter settings: Using the coal and rock strata thickness, roof and floor burial depth, and undulation morphology at the borehole location as known samples, calculate their average values. with standard deviation Assume the sample parameter X follows a normal distribution: The fluctuation range and confidence interval are determined based on geological experience and measurement errors; S232: Random Sampling: Using a random number generator, multiple samples are taken within the range of the sample parameter distribution to obtain different parameter combinations. Each sample group represents a type of stratum thickness or top and bottom elevation distribution. S233: Numerical Modeling: Substitute each set of sampling results into the three-dimensional geological model to determine the formation thickness in the un-drilled area. or elevation Perform interpolation and spatial continuity estimation; S234: Results Statistics: Statistical analysis of all simulation results, calculation of mean, variance and 95% confidence interval, thereby obtaining the optimal estimate of the undrilled area and its uncertainty range. By comparing the mean differences and confidence interval distributions of different areas, abnormal areas with large deviations in the prediction results are identified. S235: Model Correction: Compare the prediction results obtained from Monte Carlo simulation with geological maps and geophysical data. If the local differences exceed the set threshold, adjust the parameter distribution or interpolation weights and recalculate until the spatial continuity and geological rationality of the model meet the requirements.

[0010] In one example of the present invention, in step S30, a continuous triangular mesh surface is generated using Delaunay triangulation, specifically including the following steps: S31: Import the block geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and set constraints based on the boundaries of the block geological model and fault lines. S32: The initial triangular mesh is constructed using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through edge flipping and local re-meshing operations. Constrained Delaunay triangulation is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. S33: In the generated triangular mesh, each vertex records its three-dimensional geometric coordinates and geological lithology information. The connecting lines between nodes define the spatial morphology and boundary relationships of the triangular units, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. S34: Adjust the mesh density and refinement level according to the modeling accuracy and numerical calculation requirements to ensure the detailed depiction of key geological structures, and finally export the generated mesh results.

[0011] In one example of the present invention, step S40 specifically includes the following steps: S41: Introducing FLAC into the mine-scale continuum model 3D -PFC 3D The coupling method employs discrete element particle flow simulation for near-field damage regions in engineering projects, and continuous finite difference method for simulation of far-field dynamic regions in large-scale engineering projects. S42: FLAC 3D The velocity obtained through iteration is transferred to the coupling boundary and used as the PFC. 3D The model responds to new boundary conditions, while PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated until numerical equilibrium is reached. S43: In FLAC 3D Static equilibrium is achieved using the Mohr-Coulomb elastoplastic constitutive model, and a particle model is generated in the coupling region to give the parallel cemented constitutive model and achieve micro-equilibrium. S44: Set up a three-dimensional research domain and geometric boundary conditions around a typical working face, define the spatial location and morphological constraints of key structures; perform continuous-discrete coupled solution, and simulate the mechanical response process of the mine after being disturbed by mining through numerical iteration.

[0012] In one example of the present invention, in step S42, PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated continuously. Specifically, it includes the following steps: In continuous-discrete coupling, let discrete particles and FLAC be... 3D The triangular mesh elements come into contact at point C, with the nearest FLAC being [the contact point]. 3D The grid nodes are CP; The contact surface is divided into three sub-triangles, and centroid interpolation is used to distribute the contact force / moment / rotational stiffness to the element nodes according to their weights, ensuring the conservation of resultant force and resultant moment; among them, FLAC 3D The total contact force M on the side and the total bending moment generated by the constitutive model at the contact point Satisfy the following formula: In the formula, F is the contact force at contact point C; Suppose that the weights of the centroids of the three subtriangles are proportional to their areas, and let the weight of the centroid be the first centroid. The area of ​​each sub-triangle is Then nodal force and nodal bending moment The expressions are as follows: In the formula, For CP to The distance.

[0013] In one example of the present invention, step S50 specifically includes the following steps: S51: Unify the formatting, coordinate registration and time synchronization of field monitoring data and numerical simulation results, establish the spatial correspondence between monitoring points and model nodes, and realize virtual-real mapping; S52: Calculate the difference between the monitoring data and the simulation results, and extract the deviation indicators of stress, displacement and energy; when the deviation exceeds the set threshold, correct the model parameters and dynamically adjust the elastic modulus, cohesion and boundary stress. S53: Import the updated model results and monitoring data into the digital twin mine model, store them in multi-time step version, record the version number, parameter correction content and time tag, and realize the traceability update of the digital twin mine model; S54: Utilizes a 3D visualization engine to dynamically display the evolution of stress, displacement, and energy, intuitively showing the migration of stress concentration zones, crack propagation, and energy release patterns.

[0014] Another objective of this invention is to propose a digital mine modeling system based on the complex structural conditions of large mining areas, comprising: The data acquisition module is configured to acquire geological data, determine the modeling scope and geometric constraints based on the geological data of the mining area, and form a structured original geological database; The model building module is configured to use block modeling under a unified coordinate system and integrate drilling, geophysical and tunneling data from the structured original geological database. The Monte Carlo method is used to estimate the uncertainty of the un-drilled area to form a complete block geological model. The mesh generation and data output module is configured to divide irregularly distributed ground strata, import the block geological model into the 3D modeling software, use Delaunay triangulation to generate continuous triangular mesh surfaces, determine the topological structure and surface features, and output mesh data for numerical simulation. The coupling analysis and disaster simulation module is configured to use FLAC on a mine-scale model. 3D -PFC 3DThe continuous-discrete coupling method completes the simulation of static equilibrium, micro-granular calibration and dynamic load conditions, and outputs the time-series fields of stress, strain, displacement and energy to reproduce the evolution process of disaster initiation-triggering-causing disaster. The disaster early warning module is configured to form an iteratively updated digital twin mine model based on modeling and simulation analysis, realize virtual-real mapping and dynamic parameter correction, and ultimately use the digital twin mine model as the basis for disaster mechanism research, risk zoning assessment and prevention and control decisions.

[0015] In one example of the present invention, the mesh generation and data output module includes: The constraint unit is configured to import the segmented geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and setting constraint conditions according to the boundaries of the segmented geological model and fault lines. The mesh generation unit is configured to construct the initial triangular mesh using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through operations such as edge flipping and local re-meshing. Constrained Delaunay subdivision is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. The data coupling unit is configured such that each vertex in the generated triangular mesh records its three-dimensional geometric coordinates and geological lithology information, and the connecting lines between nodes define the spatial morphology and boundary relationships of the triangular unit, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. The grid data export unit is configured to adjust the grid density and refinement level according to the modeling accuracy and numerical calculation requirements, ensuring the detailed depiction of key geological structures, and finally exporting the generated grid results.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The spatial block modeling strategy proposed in this invention effectively solves the model distortion problem caused by the complex geological structure of large mining areas, ensures the smooth continuity of block boundaries, and significantly improves the accuracy and adaptability of the model under complex conditions.

[0017] This invention introduces the Monte Carlo method, which for the first time systematically quantifies the geological uncertainty of unexplored areas in mine geological modeling, providing a scientific basis for probability-based risk assessment and making decisions more reliable.

[0018] This invention generates standardized, structured CSV grid data, which opens up a data channel from geological modeling to professional numerical simulation software, avoiding tedious manual conversion and improving work efficiency and accuracy.

[0019] The digital twin mine constructed by this invention has the ability to iteratively update parameters and can be dynamically corrected based on real-time monitoring data, turning the model from a "static snapshot" into a "dynamic movie," truly realizing virtual-real mapping and providing the possibility for real-time disaster early warning.

[0020] This invention integrates data management, modeling, simulation, visualization, and decision support into a complete "perception-modeling-analysis-decision" closed loop, providing a powerful engineering tool for the prediction and intelligent prevention and control of disasters such as mine rock bursts.

[0021] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0023] Figure 1 A flowchart illustrating a digital mine modeling method based on complex structural conditions in a large mining area, according to an embodiment of the present invention; Figure 2 This is a schematic diagram of model segmentation according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the large-scale model establishment and mesh generation according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a continuous-discrete coupling model according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a digital mine system based on the complex structural conditions of a large mining area, according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0026] According to a first aspect of the present invention, a digital mine modeling method based on the complex structural conditions of a large mining area is provided, such as... Figure 1 As shown, it includes the following steps: S10: Obtain geological data, determine the modeling scope and geometric constraints based on the main GPS data, surface DEM data, geological maps, profiles, boreholes and mining engineering drawings of the mining area, and form a structured original geological database; S20: Under a unified coordinate system, block geological modeling is adopted, and drilling, geophysical and tunneling data from the structured original geological database are integrated. The Monte Carlo method is used to estimate the uncertainty of the un-drilled area to form a complete block geological model. S30: The irregularly distributed ground plane is segmented, the block model is imported into the 3D modeling software, and the Delaunay triangulation is used to generate a continuous triangular mesh surface to determine the topology and surface features, and output mesh data that can be used for numerical simulation. S40: Using FLAC on a mine-scale model 3D -PFC 3D The continuous-discrete coupling method completes the simulation of static equilibrium, micro-granular calibration and dynamic load conditions, and outputs the time-series fields of stress, strain, displacement and energy, thus reproducing the evolution process of disaster initiation-triggering-causing disaster. S50: Based on modeling and simulation analysis, an iteratively updated digital twin mine model is formed to realize virtual-real mapping and dynamic parameter correction. Ultimately, the digital twin mine model will serve as the basis for disaster mechanism research, risk zoning assessment, and prevention and control decisions, providing technical support for mine rockburst monitoring and early warning and safe mining.

[0027] The proposed spatial block modeling strategy effectively solves the model distortion problem caused by the complex geological structure of large mining areas, ensures the smooth continuity of block boundaries, and significantly improves the accuracy and adaptability of the model under complex conditions.

[0028] This modeling method introduces the Monte Carlo approach, which for the first time systematically quantifies the geological uncertainty of unexplored areas in mine geological modeling, providing a scientific basis for probability-based risk assessment and making decisions more reliable.

[0029] This modeling method generates standardized, structured CSV grid data, bridging the data gap between geological modeling and professional numerical simulation software, avoiding tedious manual conversion, and improving work efficiency and accuracy.

[0030] The digital twin mine constructed by this modeling method has the ability to iteratively update parameters and can be dynamically corrected based on real-time monitoring data, turning the model from a "static snapshot" into a "dynamic movie," truly realizing the mapping between the virtual and real worlds and providing the possibility for real-time disaster early warning.

[0031] This modeling method integrates data management, modeling, simulation, visualization, and decision support into a complete "perception-modeling-analysis-decision" closed loop, providing a powerful engineering tool for the prediction and intelligent prevention and control of disasters such as mine rock bursts.

[0032] In one example of the present invention, in step S10, geological data is acquired, and the modeling scope and geometric constraints are determined based on geological data such as main GPS data of the mining area, surface DEM data, geological maps, profiles, boreholes and mining engineering drawings, to form a structured original geological database. Specifically, this includes the following steps: S11: Obtain mining area GPS data through field surveys for unified spatial reference and surface deformation constraints, including ground reference point coordinates, boundary range, surface subsidence monitoring points and displacement; surface DEM data for describing surface topographic relief and serving as geometric constraints for modeling boundaries, including elevation points, contour lines and topographic slope information. S12: Collect geological maps, profiles, borehole data and mining engineering maps through data collection to reflect the stratigraphic distribution, lithological type, faults, collapse column structure, coal and rock layer thickness, roof and floor burial depth, undulation morphology, as well as the formation time, range and coal mining volume of the goaf. S13: Unify the format, coordinate system, and time reference of the multi-source geological data formed by the above-mentioned mining area GPS data, surface DEM data, geological maps, profile maps, borehole data, and mining engineering maps, and perform outlier removal and standardization processing; specifically including: All raw data are stored in CSV (Comma-Separated Value) structured format, recording spatial coordinates (x, y, z), attribute values ​​(such as lithology, thickness, density, elastic modulus, porosity, etc.) and time labels t in the form of fields. Time data is corrected with UTC time base. For each physical quantity parameter A dual-threshold method based on standard deviation and box statistics is used to identify and remove outliers. satisfy If it is , then it is considered an outlier. The sample mean. Standard deviation is usually taken as .

[0033] S14: Based on the standardized multi-source geological data, a hierarchical database structure is established, including storage and management of multi-dimensional spatial information and corresponding attribute data of mine points, lines, surfaces, and volumes. This multi-dimensional spatial information and corresponding attribute data includes: a data source layer, a spatial index layer, an object organization layer, an attribute relationship layer, and a time management layer. The data source layer primarily uses drilling, geophysical, geological mapping, and development and tunneling data as input, employing a structured data format (CSV) for unified storage, achieving standardized management and cross-platform access to multi-source data. The spatial index layer uses the R-tree spatial indexing algorithm for rapid location and retrieval of geological elements, ensuring consistency between the spatial relationships and topological structures of complex geological objects. The object organization layer constructs a mine geometric model using points, lines, surfaces, and volumes as basic units, assigning a unique number to each geological object and establishing a correspondence between coordinates and attribute information, achieving the associated management of spatial and attribute information. The attribute relationship layer records physical and mechanical parameters such as lithology, thickness, density, and elastic modulus, as well as dynamic information such as mining time and monitoring values, enabling synchronous updates and access to spatial and attribute data. Time Management Layer: The database supports timestamp indexing and version control, enabling the tracking of data changes at different stages. It also provides standardized data interfaces (CSV, JSON, or HDF5) to facilitate data interconnection and sharing with modeling software and numerical simulation platforms. This hierarchical structure achieves unified storage, rapid retrieval, and dynamic updates of multi-source heterogeneous geological data, providing efficient data support for subsequent block modeling, gridding, and disaster evolution analysis.

[0034] In one example of the present invention, step S20 specifically includes the following steps: S21: Under a unified coordinate system, the study area is divided into several regular or irregular blocks based on major faults, fold zones, lithological abrupt changes, sedimentary contact interfaces, and mining disturbance boundaries. The block division process considers both geological structural characteristics and numerical computation requirements, using major fault zones and folds as natural boundaries and mining roadways and goafs as artificial constraints to achieve model block division. Geological information within each block remains singular, continuous, and computable, with smooth boundaries between adjacent blocks, free of gaps or overlaps. A combination of automated block division algorithms and manual verification ensures the model can be rapidly generated and maintain geometric accuracy under complex geological conditions.

[0035] S22: By comprehensively employing a variety of geophysical exploration techniques, such as seismic reflection wave method, transient electromagnetic method, georesistivity tomography, gravity and magnetic joint inversion and high-density electrical method, it identifies structures such as concealed faults (e.g., with a drop greater than 5m), collapse columns (e.g., with a diameter greater than 50m), folds and interlayer slip zones in the strata, establishes a precise geometric boundary framework for underground space, and realizes the spatial location of fault planes and the characterization of their extension trends. S23: Based on drilling data, obtain the thickness of coal and rock strata, the burial depth of the top and bottom plates and the undulation characteristics. Use the Monte Carlo method to estimate the continuity of uncertain information in the undrilled area. Simulate the probability field of the distribution of strata thickness and elevation through multiple random samplings, and calculate the mean, variance and confidence interval to reflect the spatial variability of geological information. S24: Combining geological rules and geophysical boundaries, the estimation results are integrated into the three-dimensional spatial inversion process. The internal structure of faults and collapse columns and the interlayer transition zones are completed with attribute completion and stratigraphic smoothing. Thus, within the established underground spatial geometric boundary framework, the spatial extension and volume reconstruction of stratigraphic thickness, lithology and physical parameters are realized, forming a spatially continuous and topologically complete block geological model, providing geometric and physical property inputs for subsequent grid division and numerical calculation.

[0036] In one example of the present invention, step S23, specifically the steps of using the Monte Carlo method to perform continuity estimation, include: S231: Parameter settings: Using the coal and rock strata thickness, roof and floor burial depth, and undulation morphology at the borehole location as known samples, calculate their average values. with standard deviation Assume the parameters follow a normal distribution: The fluctuation range and confidence interval are determined based on geological experience and measurement errors; S232: Random Sampling: Using a random number generator, multiple samples are taken within the above-mentioned parameter distribution range to obtain different parameter combinations. Each sample group represents a possible distribution of stratum thickness or top and bottom elevation. S233: Numerical Modeling: Substitute each set of sampling results into the three-dimensional geological model to determine the formation thickness in the un-drilled area. or elevation Perform interpolation and spatial continuity estimation; spatial prediction can be achieved using linear interpolation or spline functions. in, Weights assigned based on distance or relevance; S234: Results Statistics: Statistical analysis of all simulation results, calculating the mean, variance, and 95% confidence intervals: This yields the optimal estimate and uncertainty range for the undrilled area. By comparing the mean differences and confidence interval distributions of different areas, abnormal areas with large deviations in the prediction results can be identified, providing a quantitative basis for subsequent parameter correction. S235: Model Correction: Compare the prediction results obtained from Monte Carlo simulation with geological maps and geophysical data. If the local differences exceed the set threshold, adjust the parameter distribution or interpolation weights and recalculate until the spatial continuity and geological rationality of the model meet the requirements.

[0037] In one example of the present invention, in step S30, a continuous triangular mesh surface is generated using Delaunay triangulation, specifically including the following steps: S31: Import the segmented geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and set constraints according to the boundaries of the segmented geological model and fault lines to prevent the grid from crossing faults or goaf areas. S32: The initial triangular mesh is constructed using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through edge flipping and local re-meshing operations. Constrained Delaunay triangulation is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. S33: In the generated triangular mesh, each vertex records its three-dimensional geometric coordinates and geological lithology information. The connecting lines between nodes define the spatial morphology and boundary relationships of the triangular units, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. S34: Adjust the grid density and refinement according to the modeling accuracy and numerical calculation requirements to ensure the detailed depiction of key geological structures such as fault zones, fold zones and goaf boundaries. Finally, export the generated grid results. The data file contains node coordinates, unit connection relationships and lithology labels for subsequent numerical simulation, disaster evolution analysis and prediction calculation.

[0038] In one example of the present invention, in step S31, the constrained Delaunay partitioning method satisfies the requirement that in geological structural regions such as faults, folds and contact zones, while maintaining the Delaunay criterion, a forced edge constraint is introduced to preserve the spatial morphology of geological boundaries and fault lines, thereby ensuring the geometric continuity and topological integrity of the partitioning results.

[0039] In one example of the present invention, in step S31, the triangulation results are exported in CSV format to store point coordinates, cell connectivity and related attribute data, which facilitates numerical simulation calls and interoperability with other formats (VTU or HDF5).

[0040] In one example of the present invention, step S40 specifically includes the following steps: S41: Introducing FLAC into the mine-scale continuum model 3D -PFC 3D For the near-field damage region of interest in engineering, discrete element particle flow simulation is used, while for the far-field dynamic region of large-scale engineering, continuous finite difference method is used for simulation. S42: Coupled analysis is achieved through data exchange at the model intersection boundary or region; specifically, this involves using FLAC... 3D The velocity obtained through iteration is transferred to the coupling boundary and used as the PFC. 3D The model responds to new boundary conditions, while PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated until numerical equilibrium is reached. S43: In FLAC 3D Static equilibrium is achieved using the Mohr-Coulomb elastoplastic constitutive model, and a particle model is generated in the coupling region to give the parallel cemented constitutive model and achieve micro-equilibrium. S44: A three-dimensional research domain and geometric boundary conditions are set around a typical working face, defining the spatial location and morphological constraints of key structures such as faults, folds, and mining-induced goafs. Based on this, a continuous-discrete coupled solution is performed, simulating the mechanical response process of the mine after mining disturbance through numerical iteration. The particle flow component in the model can quantitatively characterize the relative displacement, shear slip, and local collapse behavior of the blocks, revealing the correlation mechanism between microscopic fractures and macroscopic deformation. The calculation results are presented in time sequence and spatial dimension, dynamically outputting the multi-field distribution of stress, strain, displacement, and energy, forming a three-dimensional visualized evolution image. The model displays in real time the migration of stress concentration zones, crack initiation and propagation, and the accumulation and release of energy; different colors reflect changes in stress magnitude and direction. Through continuous playback of time-series frames, the entire process from the stable stage to critical instability is reproduced, revealing the spatial evolution law of disaster incubation and sudden occurrence, providing intuitive evidence for disaster mechanism interpretation and risk identification in key areas.

[0041] In one example of the present invention, such as Figure 4 As shown, in step S42, PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated continuously. Specifically, it includes the following steps: In continuous-discrete coupling, let discrete particles and FLAC be... 3D The triangular mesh elements come into contact at point C, with the nearest FLAC being [the contact point]. 3D The grid nodes are CP; Because the contact area undergoes microscopic deformation under tension, shear, and torsion, geometrically, C and CP may spatially offset. To achieve stable field transfer, this method divides the contact surface into three sub-triangles and uses centroid interpolation to distribute the contact force / moment / rotational stiffness to the element nodes according to their weights, ensuring the conservation of resultant force and resultant moment; among them, FLAC 3D The total contact force M on the side and the total bending moment generated by the constitutive model at the contact point Satisfy the following formula: In the formula, F is the contact force at contact point C; Suppose that the weights of the centroids of the three subtriangles are proportional to their areas, and let the weight of the centroid be the first centroid. The area of ​​each sub-triangle is Then nodal force and nodal bending moment The expressions are as follows: In the formula, For CP to The distance; The above interpolation maps the contact contribution to the element nodes in a conserved manner, ensuring mechanical equivalence and numerical stability at the coupling interface.

[0042] In one example of the present invention, step S50 specifically includes the following steps: S51: Unify the formatting, coordinate registration and time synchronization of field monitoring data and numerical simulation results, establish the spatial correspondence between monitoring points and model nodes, and realize virtual-real mapping; S52: Calculate the difference between the monitoring data and the simulation results, and extract the deviation indicators of key field quantities such as stress, displacement and energy; when the deviation exceeds the set threshold (±15%), correct the model parameters and dynamically adjust parameters such as elastic modulus, cohesion and boundary stress. S53: Import the updated model results and monitoring data into the digital twin mine model, store them in HDF5 format for multi-time step versioning, record the version number, parameter correction content and time tag, and realize the traceable update of the digital twin mine model; S54: Utilizes a 3D visualization engine to dynamically display the evolution of stress, displacement, and energy, intuitively showing the migration of stress concentration zones, crack propagation, and energy release patterns; the system conducts disaster risk zoning and early warning analysis based on model evolution results, providing real-time technical support for mine rockburst monitoring and safe mining.

[0043] According to a second aspect of the present invention, a digital mine modeling system based on the complex structural conditions of a large mining area, such as... Figure 5 As shown, it includes: The data acquisition module is configured to acquire geological data. Based on major GPS data, surface DEM data, geological maps, profiles, borehole and mining engineering drawings of the mining area, it determines the modeling scope and geometric constraints, forming a structured original geological database. Specifically, it includes a monitoring network unit, data acquisition equipment, and a communication and edge processing unit. The monitoring network unit includes deployed multi-modal sensors for microseismic, acoustic emission, potential, surface subsidence, stress, and temperature measurements. Microseismic and acoustic emission sensors are used to collect acoustic events of rock mass fracture, potential sensors are used to monitor changes in electrical signals under load disturbances, and surface subsidence and stress sensors are used for macroscopic deformation monitoring. The data acquisition equipment includes a signal conditioning module, a high-speed A / D conversion module, and a data buffer module to achieve high-frequency acquisition and synchronous recording of multi-modal signals. The communication and edge processing unit transmits data via gigabit industrial Ethernet or fiber optic communication systems, deploying edge computing nodes underground to perform preliminary filtering, noise reduction, and data compression, improving transmission efficiency and anti-interference capabilities.

[0044] The model building module is configured to use block modeling in a unified coordinate system and integrate drilling, geophysical and tunneling data from the structured original geological database. The Monte Carlo method is used to estimate the uncertainty of the un-drilled area to form a complete block geological model. Specifically, the system comprises a data standardization unit, a 3D modeling unit, and a database and version management unit. The data standardization unit performs coordinate system unification and format conversion on drilling, geophysical, and mining engineering data from different sources. The 3D modeling unit is configured to perform block modeling based on faults, folds, and goaf boundaries, and uses the Monte Carlo method to estimate the continuity of un-drilled areas, forming a geological model with a complete spatial structure. The database and version management unit uses a hierarchical structure to store multi-dimensional information of points, lines, surfaces, and volumes, and supports HDF5 format data storage and multi-version history tracing.

[0045] The mesh generation and data output module is configured to divide irregularly distributed ground strata, import the block geological model into the 3D modeling software, use Delaunay triangulation to generate continuous triangular mesh surfaces, determine the topological structure and surface features, and output mesh data that can be used for numerical simulation. The coupling analysis and disaster simulation module is configured to use FLAC on a mine-scale model. 3D -PFC 3D A continuous-discrete coupling method is used to complete static equilibrium, micro-granular calibration, and dynamic load simulation, outputting time-series fields of stress, strain, displacement, and energy to reproduce the evolution process of disaster initiation, triggering, and causing. Specifically, it includes a coupled solution unit, a result visualization unit, and a dynamic monitoring and comparison unit. The coupled solution unit is configured to use the FLAC3D and PFC3D coupling method to simulate macroscopic stress distribution and the microscopic process of surrounding rock fracture. The result visualization unit is configured to output three-dimensional evolution images of stress, displacement, and energy fields in the form of rainbow cloud maps, dynamically displaying the crack propagation, energy accumulation, and release processes. The dynamic monitoring and comparison unit is configured to compare field monitoring data with numerical calculation results in real time for model verification and parameter correction.

[0046] The disaster early warning module is configured to generate an iteratively updated digital twin mine model based on modeling and simulation analysis, achieving virtual-real mapping and dynamic parameter correction. Ultimately, the digital twin mine model will serve as the basis for disaster mechanism research, risk zoning assessment, and prevention and control decisions, providing technical support for mine rockburst monitoring and early warning, and safe mining. Specifically, it includes: a data mapping unit, a parameter update unit, and a virtual-real fusion unit. The data mapping unit is configured to achieve spatial mapping and temporal synchronization between monitoring points and model nodes; the parameter update unit is configured to automatically correct model parameters and update boundary conditions and material properties based on the deviation between monitoring and calculation results; the virtual-real fusion unit is configured to construct a two-way interactive mechanism between the virtual mine and the actual mine, displaying stress migration, crack propagation, and disaster evolution processes in real time, achieving virtual-real consistency assessment and early warning strategy calibration.

[0047] Preferably, the disaster early warning module further includes: a risk identification unit, an early warning response unit, a visualization and interactive terminal, and a system interface unit. The risk identification unit is configured to calculate multi-field coupling characteristic indicators, identify potential disaster areas, and assess risk levels. The early warning response unit is configured to trigger audible and visual alarms and dispatch instructions based on risk levels, supporting hierarchical management and emergency response linkage. The visualization and interactive terminal includes a surface control center display screen, an underground explosion-proof tablet, and a management mobile application, supporting 3D scene display and remote access. The system interface unit is configured to provide data exchange interfaces with the mine safety management system, monitoring system, and geological database, enabling integrated operation.

[0048] The spatial block modeling strategy proposed by this modeling system effectively solves the model distortion problem caused by the complex geological structure of large mining areas, ensures the smooth continuity of block boundaries, and significantly improves the accuracy and adaptability of the model under complex conditions.

[0049] This modeling system introduces the Monte Carlo method, which for the first time systematically quantifies the geological uncertainty of unexplored areas in mine geological modeling, providing a scientific basis for probability-based risk assessment and making decisions more reliable.

[0050] This modeling system generates standardized, structured CSV grid data, bridging the data gap between geological modeling and professional numerical simulation software, avoiding tedious manual conversion, and improving work efficiency and accuracy.

[0051] The digital twin mine constructed by this modeling system has the ability to iteratively update parameters and can be dynamically corrected based on real-time monitoring data, turning the model from a "static snapshot" into a "dynamic movie," truly realizing virtual-real mapping and providing the possibility for real-time disaster early warning.

[0052] This modeling system integrates data management, modeling, simulation, visualization, and decision support, forming a complete "perception-modeling-analysis-decision" closed loop, providing a powerful engineering tool for the prediction and intelligent prevention and control of disasters such as mine rock bursts.

[0053] In one example of the present invention, the mesh generation and data output module includes: The constraint unit is configured to import the segmented geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and setting constraint conditions according to the boundaries of the segmented geological model and fault lines to prevent the grid from crossing faults or mining areas. The mesh generation unit is configured to construct the initial triangular mesh using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through operations such as edge flipping and local re-meshing. Constrained Delaunay subdivision is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. The data coupling unit is configured such that each vertex in the generated triangular mesh records its three-dimensional geometric coordinates and geological lithology information, and the connecting lines between nodes define the spatial morphology and boundary relationships of the triangular unit, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. The grid data export unit is configured to adjust the grid density and refinement according to the modeling accuracy and numerical calculation requirements, ensuring the detailed depiction of key geological structures such as fault zones, fold zones, and goaf boundaries. Finally, the generated grid results are exported, and the data file contains node coordinates, unit connection relationships, and lithology labels for subsequent numerical simulation, disaster evolution analysis, and prediction calculations.

[0054] It should be noted that the digital mine modeling system based on the complex structural conditions of large mining areas of the present invention can also perform any of the processing described in the previously described digital mine modeling method based on the complex structural conditions of large mining areas, and the specific details are not repeated here.

[0055] Specific examples: This embodiment uses Qianqiu Coal Mine as the research object. Relevant geological data comes from the "Topographic and Geological Map and Hydrogeological Map of Qianqiu Mine," "Contour Map of Coal Seam Floor of Qianqiu Mine," "Mining Engineering Plan of Qianqiu Mine," and "Geological Report of Qianqiu Mine." After unified coordinate transformation, format standardization, and anomaly removal, a structured original geological database is formed. Based on this, spatial block modeling is performed according to faults, folds, and goaf boundaries to ensure homogeneous lithology and smooth, continuous boundaries within the blocks. Subsequently, the constrained Delaunay triangulation algorithm is used to mesh the irregular strata, generating continuous triangular mesh surfaces. A CSV data file containing node coordinates and lithological properties is exported for subsequent numerical calculations. The block model is then imported into FLAC. 3D Establish a far-field dynamic region continuum model, such as Figure 2 As shown, the overall model size is approximately 2800m × 3000m × 926m, divided into approximately 2.32 million nodes and 13.5 million elements. After the static equilibrium calculation was completed, for the key working face 18220, a range of 300m in front of the working face was selected as the study domain, with a roadway surrounding rock radius of approximately 5m, and the excavated area being a 5m × 4m + 1m semi-circular arch roadway. Figure 3 As shown, to reveal the characteristics of local fracture evolution, PFC was used. 3D A particle flow model was established, with particle diameters controlled between 0.05 and 0.20 μm and a porosity of 0.05, generating approximately 6.92 million particles. The entire process of microcrack initiation, propagation, and penetration was simulated by coupling the model with the FLAC model boundary. The calculation results, output in time sequence, show the distribution of stress, strain, displacement, and energy field. The results indicate that under mining disturbance, the stress chain in the surrounding rock evolves from a uniform to a non-uniform distribution, and cracks accumulate and propagate along bedding planes, forming block collapse channels. The spatial location of these channels corresponds to that of the FLAC model. 3D The calculated high-stress zones are highly consistent. After importing the coupled calculation results into the digital twin mine system, the system performs versioned storage and three-dimensional visualization rendering of multi-time-step HDF5 data. The system dynamically displays the evolution of stress, displacement, and energy through cloud maps. The high-energy zones predicted by the model match the spatial distribution of microseismic events on site well.

[0056] This case demonstrates that the block modeling, constraint partitioning, and continuous-discrete coupling method proposed in this invention can realize the digital reconstruction and visualization of the mechanical response of the mining area under complex structural conditions, providing reliable technical support for the analysis of disaster gestation-evolution mechanisms and risk zoning.

[0057] The foregoing description, with reference to preferred embodiments, details the exemplary implementation of the digital mine modeling method and system based on the complex structural conditions of large mining areas proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention, without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A digital mine modeling method based on the complex structural conditions of large mining areas, characterized in that, Includes the following steps: S10: Acquire geological data, determine the modeling scope and geometric constraints based on the geological data of the mining area, and form a structured original geological database; S20: Using block modeling under a unified coordinate system, and integrating drilling, geophysical and tunneling data from the structured original geological database, the Monte Carlo method is used to estimate the uncertainty of the un-drilled area, forming a complete block geological model; S30: The irregularly distributed geological strata are segmented, and the segmented geological model is imported into 3D modeling software. Delaunay triangulation is used to generate continuous triangular mesh surfaces, determining the topological structure and surface features, and outputting mesh data for numerical simulation. Specifically, generating continuous triangular mesh surfaces using Delaunay triangulation includes the following steps: S31: Import the block geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and set constraints based on the boundaries of the block geological model and fault lines. S32: The initial triangular mesh is constructed using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through edge flipping and local re-meshing operations. Constrained Delaunay triangulation is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. S33: In the generated triangular mesh, each vertex records its three-dimensional geometric coordinates and geological lithology information. The connecting lines between nodes define the spatial morphology and boundary relationships of the triangular units, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. S34: Adjust the mesh density and refinement according to the modeling accuracy and numerical calculation requirements to ensure the detailed depiction of key geological structures, and finally export the generated mesh results; S40: FLAC is used on the mine scale model 3D -PFC 3D Continuous-discrete coupling method, complete static balance, particle mesoscopic calibration and dynamic load working condition simulation, output stress, strain, displacement and energy time sequence field, reproduce the disaster-pregnancy-trigger-disaster evolution process; S50: Based on modeling and simulation analysis, a digital twin mine model that can be iteratively updated is formed to realize virtual-real mapping and dynamic parameter correction. Ultimately, the digital twin mine model is used as the basis for disaster mechanism research, risk zoning assessment, and prevention and control decisions.

2. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 1, characterized in that, In step S10, geological data is acquired, and the modeling scope and geometric constraints are determined based on the geological data of the mining area to form a structured original geological database. This specifically includes the following steps: S11: Obtain mining area GPS data through field surveys for unified spatial reference and surface deformation constraints, including ground reference point coordinates, boundary range, surface subsidence monitoring points and displacement; surface DEM data for describing surface topographic relief and serving as geometric constraints for modeling boundaries, including elevation points, contour lines and topographic slope information. S12: Collect geological maps, profiles, borehole data and mining engineering drawings to reflect the distribution of strata, lithological types, faults, collapse column structures, coal and rock layer thickness, roof and floor burial depth, undulation morphology, and the formation time, range and coal mining volume of goaf. S13: Unify the format, coordinate system and time reference of multi-source geological data formed by mining area GPS data, surface DEM data, geological maps, profile maps, borehole data and mining engineering maps, and remove and standardize outliers. S14: Based on the unified formatted multi-source geological data, establish a hierarchical database structure, including multi-dimensional spatial information and corresponding attribute data for storing and managing mine points, lines, surfaces and volumes. The multi-dimensional spatial information and corresponding attribute data include: data source layer, spatial index layer, object organization layer, attribute relationship layer and time management layer.

3. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 1, characterized in that, Step S20 specifically includes the following steps: S21: Under a unified coordinate system, the study area is divided into several regular blocks or irregular body units based on faults, fold zones, lithological abrupt change surfaces, sedimentary contact interfaces, and mining disturbance boundaries. S22: By comprehensively employing seismic reflection wave method, transient electromagnetic method, georesistivity tomography, gravity and magnetic joint inversion and high-density electrical method, the structure of concealed faults, collapse columns, folds and interlayer slip zones in the strata is identified, and a geometric boundary framework of underground space is established. S23: Based on drilling data, obtain the thickness of coal and rock strata, the burial depth of the top and bottom plates and the undulation characteristics. Use the Monte Carlo method to estimate the continuity of uncertain information in the undrilled area. Simulate the probability field of the distribution of strata thickness and elevation through multiple random samplings, and calculate the mean, variance and confidence interval to reflect the spatial variability of geological information. S24: Combining geological rules and geophysical boundaries, the estimation results are integrated into the three-dimensional spatial inversion process. The internal structure of faults and collapse columns and the interlayer transition zones are completed with attribute completion and stratum smoothing. Thus, within the established underground spatial geometric boundary framework, the spatial extension and volume reconstruction of stratum thickness, lithology and physical parameters are realized, forming a spatially continuous and topologically complete block geological model.

4. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 3, characterized in that, In step S23, the specific steps for continuity estimation using the Monte Carlo method include: S231: Parameter settings: Using the coal and rock strata thickness, roof and floor burial depth, and undulation morphology at the borehole location as known samples, calculate their average values. with standard deviation Assume the sample parameter X follows a normal distribution: The fluctuation range and confidence interval are determined based on geological experience and measurement errors; S232: Random Sampling: Using a random number generator, multiple samples are taken within the range of the sample parameter distribution to obtain different parameter combinations. Each sample group represents a type of stratum thickness or top and bottom elevation distribution. S233: Numerical Modeling: Substitute each set of sampling results into the three-dimensional geological model to determine the formation thickness in the un-drilled area. or elevation Perform interpolation and spatial continuity estimation; S234: Results Statistics: Statistical analysis of all simulation results, calculation of mean, variance and 95% confidence interval, thereby obtaining the optimal estimate of the undrilled area and its uncertainty range. By comparing the mean differences and confidence interval distributions of different areas, abnormal areas with large deviations in the prediction results are identified. S235: Model Correction: Compare the prediction results obtained from Monte Carlo simulation with geological maps and geophysical data. If the local differences exceed the set threshold, adjust the parameter distribution or interpolation weights and recalculate until the spatial continuity and geological rationality of the model meet the requirements.

5. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 1, characterized in that, Step S40 specifically includes the following steps: S41: Introducing FLAC into the mine-scale continuum model 3D -PFC 3D The coupling method employs discrete element particle flow simulation for near-field damage regions in engineering projects, and continuous finite difference method for simulation of far-field dynamic regions in large-scale engineering projects. S42: FLAC 3D The velocity obtained through iteration is transferred to the coupling boundary and used as the PFC. 3D The model responds to new boundary conditions, while PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated until numerical equilibrium is reached. S43: In FLAC 3D Static equilibrium is achieved using the Mohr-Coulomb elastoplastic constitutive model, and a particle model is generated in the coupling region to give the parallel cemented constitutive model and achieve micro-equilibrium. S44: Set up a three-dimensional research domain and geometric boundary conditions around a typical working face, define the spatial location and morphological constraints of key structures; perform continuous-discrete coupled solution, and simulate the mechanical response process of the mine after being disturbed by mining through numerical iteration.

6. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 1, characterized in that, In step S42, PFC 3D The forces generated by the model are then fed back to FLAC. 3D The model updates the response, and this process is iterated continuously. Specifically, it includes the following steps: In continuous-discrete coupling, let discrete particles and FLAC be... 3D The triangular mesh elements come into contact at point C, with the nearest FLAC being [the contact point]. 3D The grid nodes are CP; The contact surface is divided into three sub-triangles, and centroid interpolation is used to distribute the contact force / moment / rotational stiffness to the element nodes according to their weights, ensuring the conservation of resultant force and resultant moment; among them, FLAC 3D The total contact force M on the side and the total bending moment generated by the constitutive model at the contact point Satisfy the following formula: In the formula, F is the contact force at contact point C; Suppose that the weights of the centroids of the three subtriangles are proportional to their areas, and let the weight of the centroid be the first centroid. The area of ​​each sub-triangle is Then nodal force and nodal bending moment The expressions are as follows: In the formula, For CP to The distance.

7. The digital mine modeling method based on the complex structural conditions of large mining areas according to claim 1, characterized in that, Step S50 specifically includes the following steps: S51: Unify the formatting, coordinate registration and time synchronization of field monitoring data and numerical simulation results, establish the spatial correspondence between monitoring points and model nodes, and realize virtual-real mapping; S52: Calculate the difference between the monitoring data and the simulation results, and extract the deviation indicators of stress, displacement and energy; when the deviation exceeds the set threshold, correct the model parameters and dynamically adjust the elastic modulus, cohesion and boundary stress. S53: Import the updated model results and monitoring data into the digital twin mine model, store them in multi-time step version, record the version number, parameter correction content and time tag, and realize the traceability update of the digital twin mine model; S54: Utilizes a 3D visualization engine to dynamically display the evolution of stress, displacement, and energy, intuitively showing the migration of stress concentration zones, crack propagation, and energy release patterns.

8. A digital mine modeling system based on the complex structural conditions of large mining areas, characterized in that, include: The data acquisition module is configured to acquire geological data, determine the modeling scope and geometric constraints based on the geological data of the mining area, and form a structured original geological database; The model building module is configured to use block modeling in a unified coordinate system and integrate drilling, geophysical and tunneling data from the structured original geological database. It uses the Monte Carlo method to estimate the uncertainty of the un-drilled area and form a complete block geological model. The mesh generation and data output module is configured to divide irregularly distributed ground strata, import the block geological model into the 3D modeling software, use Delaunay triangulation to generate continuous triangular mesh surfaces, determine the topological structure and surface features, and output mesh data for numerical simulation. The grid generation and data output module includes: The constraint unit is configured to import the segmented geological model into the 3D modeling software, using borehole control points, geophysical interpretation points and surface elevation points as input nodes, and setting constraint conditions according to the boundaries of the segmented geological model and fault lines. The mesh generation unit is configured to construct the initial triangular mesh using the Delaunay triangulation algorithm, which satisfies the Delaunay criterion that no other nodes are contained within the circumcircle of any triangle. The mesh quality is optimized through operations such as edge flipping and local re-meshing. Constrained Delaunay subdivision is applied in fault and fold regions to maintain the continuity of geological boundaries and structural integrity. The data coupling unit is configured such that each vertex in the generated triangular mesh records its three-dimensional geometric coordinates and geological lithology information, and the connecting lines between nodes define the spatial morphology and boundary relationships of the triangular unit, thereby establishing the topological structure and surface features of the model and realizing the coupled expression of spatial geometry and geological information. The grid data export unit is configured to adjust the grid density and refinement according to the modeling accuracy and numerical calculation requirements, ensuring the detailed depiction of key geological structures, and finally exporting the generated grid results. The coupling analysis and disaster simulation module is configured to use FLAC on a mine-scale model. 3D -PFC 3D The continuous-discrete coupling method completes the simulation of static equilibrium, micro-granular calibration and dynamic load conditions, and outputs the time-series fields of stress, strain, displacement and energy to reproduce the evolution process of disaster initiation-triggering-causing disaster. The disaster early warning module is configured to form an iteratively updated digital twin mine model based on modeling and simulation analysis, realize virtual-real mapping and dynamic parameter correction, and ultimately use the digital twin mine model as the basis for disaster mechanism research, risk zoning assessment and prevention and control decisions.

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