A cross-mining area intelligent three-dimensional geological mapping system and its construction method

Abstract

This invention discloses a cross-mining area intelligent 3D geological mapping system and its construction method, belonging to the interdisciplinary field of mine safety supervision and digital twin technology. It solves the problems of inconsistent cross-mining area benchmarks, data silos, low modeling efficiency, lack of intelligent interpretation, and insufficient comprehensive supervision in existing mine area supervision systems. The system includes a cross-mining area multi-source heterogeneous data access module, which is connected to a unified spatiotemporal benchmark fusion module. This module is further connected to a regional geological intelligent interpretation module and a cross-mining area 3D geological automatic modeling module. The cross-mining area 3D geological automatic modeling module is also sequentially connected to a 3D geological visualization module and a cross-mining area supervision and early warning module. The cross-mining area supervision and early warning module is also connected to a cloud collaboration and access control module, which is also communicatively connected to the 3D geological visualization module. This invention is applied to cross-mining area supervision.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of mine safety supervision and digital twins, specifically to a cross-mining area intelligent three-dimensional geological single map system and its construction method. Background Technology

[0002] As mineral resource development enters a stage of intensive and large-scale development, cross-administrative region multi-mine collaborative management and comprehensive geological safety supervision have become core industry needs. However, the current field of 3D geological modeling and safety supervision in mines generally faces insurmountable technical bottlenecks, which seriously restrict the level of intelligence and precision in regional mine management, as detailed below: 1. Inconsistent benchmarks across mining areas make model splicing difficult: In the current technology, each mining area independently carries out 3D geological modeling work, and the coordinate system, scale and data format used lack unified standards. As a result, the 3D models of different mining areas cannot be seamlessly spliced, making it difficult to form a unified 3D geological map of the entire region. The higher-level regulatory authorities cannot intuitively grasp the overall geological situation of the mines in the entire region.

[0003] 2. The phenomenon of data silos is prominent and the integration capability is weak: Data from geological exploration, drilling, geophysical exploration, mining, hydrology, remote sensing monitoring and investigation of hidden disaster-causing factors in multiple mining areas are scattered and stored in independent systems in each mining area. The data standards are inconsistent and the correlation is low, forming serious data silos. At the regional level, it is impossible to achieve unified access, comprehensive analysis and overall supervision of multi-source heterogeneous data, and it is difficult to explore the correlation of geological structures, hydrological connectivity and the contiguous distribution pattern of hidden disaster-causing bodies across mining areas.

[0004] 3. Low modeling efficiency and difficulty in ensuring accuracy: Traditional 3D geological modeling relies heavily on manual intervention. Professional technicians are required to complete data processing, model construction and correction for each mining area and each stratum. For large-scale modeling across mining areas, the workload increases exponentially, the modeling cycle is long (usually several months), and manual operation is prone to subjective errors. The model accuracy cannot meet the high-precision requirements of regional supervision.

[0005] 4. Lack of intelligent interpretation capabilities and insufficient risk prediction: The existing system lacks AI-driven intelligent analysis capabilities, and cannot realize automatic comparison and connection of strata across mining areas, inference of regional faults and fracture zones, intelligent analysis of hydrological connectivity, and it is even more difficult to complete the identification and risk prediction of hidden disaster-causing bodies across mining areas. It can only passively respond to geological disasters and cannot achieve proactive prevention and control.

[0006] 5. Lack of support for comprehensive supervision and low control efficiency: Provincial and national mine supervision platforms lack a unified spatiotemporal benchmark for a comprehensive three-dimensional geological map. Existing supervision relies heavily on two-dimensional data and manual reporting, which cannot achieve visualized, intelligent, and routine supervision of geological information across mining areas. It is difficult to quickly locate high-risk areas and coordinate the promotion of governance work, resulting in low supervision efficiency.

[0007] In summary, existing technologies are limited to local management of a single mining area and fail to address the core pain points of unified benchmarks, data fusion, intelligent modeling, and comprehensive supervision across mining areas. They cannot meet the needs of provincial and national-level large-scale construction and supervision of cross-mining area 3D geological maps. Therefore, there is an urgent need to develop a cross-mining area intelligent 3D geological map system with unified benchmarks, AI-driven capabilities, automatic stitching, and comprehensive control capabilities. This system would fill the technological gap in the industry, improve the intelligence and precision of regional mine geological management, and has extremely high industrial value and application prospects. Summary of the Invention

[0008] To address the technical problems existing in current mining area supervision systems, such as inconsistent benchmarks across mining areas, data silos, low modeling efficiency, lack of intelligent interpretation, and insufficient comprehensive supervision, this invention proposes a cross-mining area intelligent three-dimensional geological map system and its construction method.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a cross-mining area intelligent three-dimensional geological mapping system, comprising: A cross-mining-area multi-source heterogeneous data access module is used to collect multi-source heterogeneous data from multiple mining areas in different regions; The unified spatiotemporal reference fusion module communicates with the cross-mining area multi-source heterogeneous data access module. It is used to perform coordinate transformation, spatiotemporal alignment and format normalization on the collected multi-source heterogeneous data to build a data foundation for a unified spatiotemporal reference across mining areas. The regional geological intelligent interpretation module is communicatively connected to the unified spatiotemporal benchmark fusion module. It is used to automatically and intelligently interpret the data in the data base across mining areas based on a pre-trained geological large model, and output the interpretation results. The automatic intelligent interpretation includes at least stratigraphic correlation, structural identification, hydrological analysis and disaster-causing body identification. The cross-mining area 3D geological automatic modeling module is connected to the unified spatiotemporal benchmark fusion module and the regional geological intelligent interpretation module. It is used to construct a cross-mining area 3D geological model and dynamically update it based on the data base built by the data fusion module and the interpretation results of the regional geological intelligent interpretation module. The 3D geological visualization module communicates with the cross-mining area 3D geological automatic modeling module to realize multi-dimensional visualization and interactive operation of cross-mining area geological information based on the cross-mining area 3D geological model and 3D GIS engine. The cross-mining area supervision and early warning module communicates with the 3D geological visualization module to realize intelligent supervision and proactive early warning of geological safety across mining areas based on visualization results and real-time monitoring data; The cloud-based collaboration and access control module communicates with the cross-mine area monitoring and early warning module and the 3D geological visualization module to support the collaborative operation of a multi-level monitoring system.

[0010] Furthermore, the cross-mining area multi-source heterogeneous data access module includes: The format adaptation unit has built-in multi-format adaptation interfaces and intelligent adaptation algorithms to automatically adapt to multiple data formats; The coordinate adaptation unit communicates with the format adaptation unit and is used to identify and calibrate the coordinate system and scale of the multi-source heterogeneous data after format adaptation. The access mode unit communicates with the coordinate adaptation unit and integrates batch import mode, real-time acquisition mode and manual recording mode. It is used to connect multi-source heterogeneous data after coordinate calibration to the unified spatiotemporal reference fusion module through at least one of batch import, real-time acquisition and manual recording.

[0011] Furthermore, the unified spatiotemporal reference fusion module includes: The coordinate transformation unit is used to convert the multi-source heterogeneous data received from the access mode unit into the national geodetic coordinate system. The spatiotemporal alignment unit communicates with the coordinate transformation unit and is used to perform spatiotemporal alignment on multi-source heterogeneous data from different mining areas and different time dimensions after coordinate transformation. The format normalization unit communicates with the spatiotemporal alignment unit and is used to convert multi-source heterogeneous data of different formats after spatiotemporal alignment into a unified format that is compatible with the system. The quality verification unit, which communicates with the format normalization unit, is used to perform outlier detection, missing value filling, and duplicate data removal on the multi-source heterogeneous data after format normalization, forming a data base for a unified spatiotemporal benchmark across mining areas.

[0012] Furthermore, the regional geological intelligent interpretation module includes: The stratigraphic correlation unit is used to perform cross-mining area stratigraphic correlation based on the drilling borehole data and stratigraphic data in the data base output by the unified spatiotemporal reference fusion module. The structure identification unit is used to identify cross-mining area structures based on remote sensing image data and geophysical data in the data base. The hydrological analysis unit is used to perform cross-mining area hydrological connectivity analysis based on hydrogeological data in the data base. The disaster-causing body identification unit is connected to the stratigraphic correlation unit, the structural identification unit, and the hydrological analysis unit, respectively. It is used to integrate the stratigraphic correlation results output by the stratigraphic correlation unit, the structural identification results output by the structural identification unit, and the hydrological analysis results output by the hydrological analysis unit to identify hidden disaster-causing bodies across mining areas.

[0013] Furthermore, the cross-mining area three-dimensional geological automatic modeling module includes: Stratigraphic framework building unit is used to construct a unified stratigraphic framework model covering the entire domain based on the stratigraphic correlation results output by the stratigraphic correlation unit; The construction modeling unit is used to build a three-dimensional model of the region's structure based on the structure recognition results output by the structure recognition unit; The model stitching unit is used to stitch together the geological entity models of various mining areas into a cross-mining area integrated three-dimensional geological model; the geological entity models of each mining area are generated by inversion of borehole data in the data base constructed by the stratigraphic framework construction unit based on the unified spatiotemporal benchmark fusion module. The disaster-causing body modeling unit is used to construct a three-dimensional visualization model of hidden disaster-causing bodies based on the disaster-causing body identification results output by the disaster-causing body identification unit. The dynamic update unit, which works in conjunction with the cross-mining area multi-source heterogeneous data access module, is used to trigger the model update of the corresponding unit when new or updated data is added.

[0014] Furthermore, the three-dimensional geological visualization module includes: The global display unit is used to display the cross-mining area integrated 3D geological model output by the cross-mining area 3D geological automatic modeling module from a unified perspective; The layered display unit communicates with the global display unit and is used to display the integrated three-dimensional geological model across mining areas in layers according to geological element type, color-coded according to risk level, and hierarchical access control according to administrative level on the global display unit. The risk rendering unit communicates with the global display unit and is used to generate regional geological risk heat maps based on the spatial coordinate information and real-time monitoring data of the cross-mining area integrated three-dimensional geological model. The linked query unit communicates with the full-domain display unit, the hierarchical display unit, and the risk rendering unit. It is used to receive the visualization data and underlying basic data output by the full-domain display unit, the hierarchical display unit, and the risk rendering unit, and to perform hierarchical linked query operations.

[0015] Furthermore, the cross-mining area monitoring and early warning module includes: The risk statistics unit is used to identify the quantity, distribution and level of each risk type in each mining area based on the regional geological risk heat map displayed by the three-dimensional geological visualization module. The risk identification unit communicates with the risk statistics unit and is used to output high-risk mining areas and corresponding high-risk regions based on the identification results of the risk statistics unit. The early warning unit communicates with the risk identification unit and is used to trigger the early warning mechanism when a contiguous disaster-prone area is identified. The progress tracking unit communicates with the early warning unit and is used to track the progress of the treatment.

[0016] Furthermore, the cloud-based collaboration and access control module includes: The access control unit is used to classify user roles by province, city, mine and job position, and works in conjunction with the hierarchical display unit to clarify data viewing permissions and operation permissions. The multi-level collaborative unit communicates with the access control unit and is used to support data reporting from multiple mining areas, municipal-level review, provincial-level supervision and national-level scheduling based on access control results. The traceability management unit is connected to the access control unit and the multi-level collaboration unit respectively, and is used to record operation behavior to achieve full traceability and auditability. The cloud deployment unit is used to provide a cloud operating environment for the permission management unit, multi-level collaboration unit, and record management unit.

[0017] A method for constructing the aforementioned intelligent three-dimensional geological single-map system across mining areas includes the following steps: Step S1: Collect multi-source heterogeneous data from multiple mining areas in different regions; Step S2: Perform coordinate transformation, spatiotemporal alignment and format normalization on the collected multi-source heterogeneous data to construct a data base for a unified spatiotemporal reference across mining areas; Step S3: Based on the data base, perform automatic intelligent interpretation of cross-mining area geological information and output the interpretation results; Step S4: Based on the data base and interpretation results, construct a three-dimensional geological model across mining areas and update it dynamically; Step S5: Based on the cross-mining area three-dimensional geological model, realize the multi-dimensional visualization and interactive operation of cross-mining area geological information; Step S6: Based on the visualization results and real-time monitoring data, realize intelligent supervision and proactive early warning of geological safety across mining areas; through the cloud collaboration and permission management module, support the collaborative operation of the multi-level supervision system.

[0018] Furthermore, step S2 specifically includes: Step S21: Convert the accessed multi-source heterogeneous data into the national geodetic coordinate system; Step S22: Perform precise spatiotemporal alignment of the transformed multi-source heterogeneous data across different mining areas and time dimensions; Step S23: Convert the aligned multi-source heterogeneous data of different formats into a unified format compatible with the system; Step S24: Clean and verify the normalized data to form the data base.

[0019] The advantages of this invention over the prior art are as follows: 1. The system of this invention standardizes cross-mining data of different coordinate systems and formats through a unified spatiotemporal reference fusion module, constructs a unified data base for the entire region, realizes seamless splicing of three-dimensional models of multiple mining areas and full-area visualization, solves the core pain point that existing technologies and traditional single mining area solutions cannot achieve cross-mining area overall management and control, fills the industry technology gap, and breaks down the barriers between data and models; 2. The system of this invention adopts a large regional geological model through a regional geological intelligent interpretation module, embedding cross-mining area collaborative interpretation technology to achieve fully automatic intelligent interpretation of cross-mining area stratigraphic comparison, structural inference, and hidden disaster identification, improving interpretation efficiency by more than 10 times compared to manual methods. Combining AI fully automatic modeling algorithm with cross-mining area model stitching technology, it can complete the construction of large-scale cross-mining area 3D models without manual intervention, shortening the modeling cycle from several months to several days, and improving model accuracy by more than 30%. It completely solves the problems of low modeling efficiency, insufficient accuracy, and inability to conduct cross-mining area collaborative modeling in existing technologies and traditional single-mining area schemes.

[0020] 3. The system of this invention constructs a closed-loop system of "interpretation-modeling-early warning-monitoring" by coordinating the regional geological intelligent interpretation module, the cross-mining area three-dimensional geological automatic modeling module, the three-dimensional geological visualization module and the cross-mining area supervision and early warning module. It can identify the risk of disaster in contiguous areas across mining areas in advance, predict geological disaster hazards, and achieve "early detection, early warning and early treatment", which can greatly improve the regional mine geological safety prevention and control capabilities and reduce disaster losses.

[0021] 4. The present invention's system integrates a cross-mine area monitoring and early warning module, a 3D geological visualization module, and a cloud-based collaboration and access control module. This addresses the shortcomings of existing higher-level monitoring platforms and traditional single-mine area solutions, which lack comprehensive visualization support, suffer from low control efficiency, and are unable to coordinate monitoring. It promotes the transformation of mine supervision from "manual reporting" to "intelligent management and control." Simultaneously, the entire process is traceable and auditable, ensuring the system's long-term and stable operation, reducing system maintenance costs, and resolving the problems of cumbersome updates and lack of collaborative maintenance in traditional single-mine area systems.

[0022] 5. The system and construction method of this invention are highly versatile, scalable, and have extremely high industrial value: The system and construction method of this invention are not limited to a certain type of mine and can be widely applied to coal mines and non-coal mines throughout the province and the country. It can be directly connected to mine safety supervision platforms at all levels without specific modifications. The deployment cost is low and the promotion difficulty is small, which can quickly achieve large-scale application. It is different from the limitations of traditional single-mine area solutions that can only be adapted to themselves and cannot be promoted on a large scale. It promotes the intelligent upgrading of the mine geological management industry and has extremely high economic and social benefits. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 This is a system architecture diagram of a cross-mining area intelligent three-dimensional geological single map system according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the unified spatiotemporal reference data fusion process of this invention; Figure 5 This is a schematic diagram of the automatic splicing of three-dimensional geological models across mining areas according to the present invention; Figure 6 This is a schematic diagram illustrating the multi-level unified supervision of provinces, cities, and mines under this invention. Detailed Implementation

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate relative orientations or positional relationships and are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0025] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] like Figures 1 to 6As shown, this invention provides a cross-mining area intelligent 3D geological mapping system, which realizes deep fusion of multi-mining area, multi-source heterogeneous data, intelligent regional stratigraphic and structural simulation, contiguous identification of hidden disaster-causing factors, automatic 3D model stitching, and multi-level integrated supervision at the provincial, municipal, and mine levels. It fills the technological gap in cross-mining area intelligent geological management and control, provides core technical support for provincial and national mine supervision platforms, significantly improves the accuracy and intelligence level of regional mine geological safety management and control, reduces supervision costs, and achieves proactive prevention and control. Specifically, it includes: The cross-mining area multi-source heterogeneous data access module is used to collect multi-source heterogeneous data from multiple mining areas in different regions. The cross-mining area multi-source heterogeneous data access module integrates a format parsing engine, supports automatic compatibility of multiple data formats, and adopts multiple access modes such as batch import, real-time collection, and manual supplementation.

[0027] As the core of the system's data input, the cross-mining-area multi-source heterogeneous data access module differs from the limitations of traditional single-mining-area data access that only accesses its own local data. This module is specifically designed for the data access needs of multiple mining areas and across administrative regions, constructing a cross-mining-area data access system with "full coverage + multi-source compatibility + real-time synchronization". It is used to comprehensively access various types of multi-source heterogeneous data from multiple mining areas (covering all mining areas within provincial, municipal, and county-level administrative regions), including but not limited to: geological exploration data, drilling data, geophysical exploration (seismic, electromagnetic, gravity) data, mining progress data, hydrogeological data, remote sensing image data, real-time monitoring data (mine pressure, displacement, groundwater), and survey data of hidden disaster-causing factors.

[0028] Specifically, the cross-mining area multi-source heterogeneous data access module includes: The format adaptation unit has built-in multi-format adaptation interfaces and intelligent adaptation algorithms to automatically adapt to various data formats. More specifically, to address the problem of messy and inconsistent data formats across mining areas, it integrates a format parsing engine (supporting more than 20 mainstream formats such as CAD, Shp, Excel, PDF, and images), achieving automatic format compatibility without manual preprocessing.

[0029] The coordinate adaptation unit communicates with the format adaptation unit to identify and calibrate the coordinate system and scale of multi-source heterogeneous data after format adaptation. More specifically, for the differences in coordinate systems (Beijing 54, Xi'an 80, local coordinate systems, etc.) and scales (1:500-1:10000) of different mining areas, a built-in coordinate pre-identification and automatic scale adaptation module is used to complete the data attribute calibration in advance, laying the foundation for subsequent benchmark unification.

[0030] The access mode unit, communicating with the coordinate adaptation unit, integrates batch import mode, real-time acquisition mode, and manual data entry mode. It is used to connect multi-source heterogeneous data after coordinate calibration to the unified spatiotemporal reference fusion module through at least one of these methods. This unit adopts a three-in-one access mode of "batch import + real-time acquisition + manual data entry." Batch import supports the second-level upload of tens of thousands of data points; real-time acquisition can connect to existing monitoring equipment (sensors, drones, etc.) in various mining areas, achieving minute-level data synchronization; and manual data entry supports collaborative editing by multiple users. This addresses the pain points of scattered data and untimely updates across mining areas, ensuring the comprehensiveness, flexibility, and real-time nature of data access. It provides complete data support for subsequent data fusion and modeling, completely differentiating itself from the traditional "each mining area operates independently" data access mode.

[0031] The unified spatiotemporal reference fusion module communicates with the cross-mining area multi-source heterogeneous data access module. It is used to perform coordinate transformation, spatiotemporal alignment and format normalization processing on the collected multi-source heterogeneous data to build a data foundation for a unified spatiotemporal reference across mining areas.

[0032] The unified spatiotemporal benchmark fusion module, as the core module for solving the problems of inconsistent spatiotemporal benchmarks across mining areas and the inability to effectively integrate multi-source heterogeneous data, differs from the limitations of traditional single-mining-area models that only need to meet their own modeling requirements without considering the need for cross-regional spatiotemporal benchmark unification. Specifically addressing the pain points of spatiotemporal benchmarks in cross-mining-area full-domain management scenarios, it incorporates a four-dimensional spatiotemporal fusion module and a full-process standardization module. The four-dimensional spatiotemporal fusion module, based on three-dimensional spatial coordinates (X, Y, Z) + time dimension (T), unifies and calibrates the coordinate systems of different spatiotemporal benchmarks in various mining areas, constructing a unified spatiotemporal benchmark framework across mining areas. The full-process standardization module covers all processing units, including data access, coordinate transformation, format normalization, and quality verification, establishing unified spatiotemporal benchmark standards and data processing specifications. The four-dimensional spatiotemporal fusion module and the full-process standardization module work together to perform full-process standardization processing on various heterogeneous data accessed by the cross-mining-area multi-source heterogeneous data access module, ensuring data consistency and compatibility in spatiotemporal benchmarks, laying the foundation for subsequent cross-mining-area data fusion and application.

[0033] Specifically, the unified spatiotemporal reference fusion module includes: The coordinate transformation unit is used to uniformly convert the multi-source heterogeneous data received from the access mode unit into the CGCS2000 national geodetic coordinate system. More specifically, a seven-parameter coordinate transformation algorithm (combined with partition fitting optimization technology) is adopted to address the problem of insufficient coordinate transformation accuracy across large areas of mining regions. The province / country is divided into several coordinate transformation zones, and the transformation parameters are calculated separately for each zone. All data are uniformly converted into the CGCS2000 national geodetic coordinate system with a transformation accuracy controlled within ±5cm, ensuring spatial consistency of data across mining regions and completely solving the problem of independent and incompatible coordinates in traditional single mining areas.

[0034] The spatiotemporal alignment unit, communicating with the coordinate transformation unit, is used to perform spatiotemporal alignment of multi-source heterogeneous data from different mining areas and different time dimensions after coordinate transformation. More specifically, based on timestamp synchronization (millisecond-level synchronization accuracy) and a spatial position calibration module (combined with GNSS real-time positioning calibration), it achieves precise spatiotemporal alignment of data from different mining areas and different time dimensions, solving the problems of temporal misalignment and spatial offset of cross-mining area data. This ensures that data from multiple mining areas are analyzed in a coordinated manner within the same spatiotemporal framework, a core technology that traditional single-mining-area solutions do not require.

[0035] The format normalization unit, communicating with the spatiotemporal alignment unit, is used to convert multi-source heterogeneous data in different formats after spatiotemporal alignment into a unified format compatible with the system. More specifically, this format normalization unit, addressing the needs of cross-mining area data management, incorporates a data element standardization execution component and a format adaptive conversion component. The data element standardization component, based on the needs of cross-mining area data management, establishes unified data element classification, naming, and description specifications, standardizing the core data elements of all types of data, including geological, drilling, and monitoring data, clarifying the business and technical attributes of data elements, and ensuring the consistency and identifiability of data elements. The format adaptive conversion component, through a constructed multi-format data recognition model, automatically identifies the original formats (such as text, vector, and raster formats) of heterogeneous data from different mining areas, presets format mapping rules, and adaptively completes the conversion of different format data to a unified vector / raster format compatible with the system, without manual intervention. The data element standardization execution component and the format adaptive conversion component work together to achieve standardized processing of multiple types of data across mining areas.

[0036] The quality verification unit, communicating with the format normalization unit, is used to perform outlier detection, missing value imputation, and duplicate data removal on the format-normalized multi-source heterogeneous data, forming a data foundation for a unified spatiotemporal benchmark across mining areas. More specifically, this quality verification unit cleans and verifies the data through outlier detection (Z-score algorithm + sliding window filtering optimization), missing value imputation (interpolation algorithm + geological prior knowledge-assisted correction), and duplicate data removal (based on data fingerprint matching technology), ensuring the accuracy, completeness, and consistency of the data. Among these, the geological prior knowledge-assisted correction technology can combine regional geological patterns to reasonably supplement missing cross-mining area related data, avoiding the impact of a single missing data on the overall analysis. Ultimately, it constructs a unified spatiotemporal benchmark data foundation for the entire region, breaking down the data silos formed by traditional single mining areas, realizing the interoperability, sharing, and efficient access of data from multiple mining areas, and providing core data support for cross-mining area-wide management and control.

[0037] The regional geological intelligent interpretation module is connected to the unified spatiotemporal benchmark fusion module. It is used to automatically and intelligently interpret the data in the data base across mining areas based on the pre-trained geological large model, and output the interpretation results. The automatic intelligent interpretation includes at least stratigraphic correlation, structural identification, hydrological analysis and disaster-causing body identification.

[0038] The regional geological intelligent interpretation module, as the core functional module for achieving intelligent geological interpretation in the system, differs from the traditional single-mine area interpretation method, which can only complete simple geological interpretation within its own scope and cannot achieve cross-mine area collaborative analysis. Specifically addressing the core pain points of cross-mine area geological interpretation, it innovatively embeds key technologies for cross-mine area collaborative interpretation based on a pre-trained regional geological large-scale model (which integrates massive regional geological samples, mature geological prior knowledge, and deep learning algorithms). These key technologies specifically refer to constructing a cross-mine area geological feature association model, linking the geological data features of each mine area, and achieving collaborative interpretation of geological information from different mine areas, thus completing fully automated intelligent interpretation of regional geological information across mine areas. Experimental verification has completely solved the prominent technical problems of low efficiency, large interpretation errors, and inability to achieve cross-mine area collaborative interpretation in traditional manual interpretation and single-mine area interpretation methods, providing accurate and reliable interpretation data support for subsequent cross-mine area 3D geological modeling.

[0039] The massive regional geological samples should at least include four categories: borehole data from mining areas, geological profile data, remote sensing image data (resolution ≥ 1m), and geological hazard point data; mature geological prior knowledge should at least include regional stratigraphic division rules (such as the stratigraphic division standard of Carboniferous-Permian in North China), geological structure identification criteria (such as the morphological characteristics of faults and folds), and lithological classification standards (such as the basis for distinguishing igneous rocks, sedimentary rocks, and metamorphic rocks).

[0040] Specifically, the regional geological intelligent interpretation module includes: The stratigraphic correlation unit is used to perform cross-mining area stratigraphic correlation based on drilling borehole data and stratigraphic data from the data base output by the unified spatiotemporal benchmark fusion module. More specifically, this stratigraphic correlation unit integrates a stratigraphic lithology feature extraction component and a homologous stratigraphic fingerprint matching component. The stratigraphic lithology feature extraction component extracts the lithological composition, mineral content, and well logging response characteristics of strata in each mining area. The homologous stratigraphic fingerprint matching component, based on the extracted stratigraphic lithology features, constructs a homologous stratigraphic feature fingerprint map containing stratigraphic lithology and elemental composition. The stratigraphic lithology feature extraction component and the homologous stratigraphic fingerprint matching component work together to automatically identify the stratigraphic lithology type and stratigraphic contact relationship in each mining area. Then, through the constructed cross-mining area stratigraphic correlation knowledge base, preset fingerprint similarity comparison thresholds and feature association rules, it achieves accurate correlation and seamless connection of homologous strata across mining areas. Experimental verification shows that its correlation accuracy is improved by more than 40% compared to manual correlation, effectively solving the prominent technical problems of low efficiency, large errors, and inability to achieve cross-mining area stratigraphic linkage correlation in existing manual correlation methods.

[0041] The structural identification unit is used to identify cross-mining area structures based on remote sensing image data and geophysical data from the data base. More specifically, the structural identification unit incorporates a deep learning semantic segmentation component and a structural trend inference component. These two components work together, combining the distribution patterns of cross-mining area structures, to automatically infer the distribution range, strike, and dip angle of faults and fracture zones within the region. This enables contiguous identification and complete characterization of cross-mining area structures, unlike the limitations of traditional single-mining-area identification which can only identify faults within its own area and cannot grasp the correlation of regional structures.

[0042] The hydrological analysis unit is used to perform cross-mining area hydrological connectivity analysis based on hydrogeological data in the data base. More specifically, the hydrological analysis unit has built-in hydrological numerical simulation components and groundwater migration path tracking components. These components work together to integrate hydrogeological data from multiple mining areas, combined with the distribution characteristics of aquifers across mining areas, to intelligently analyze the distribution patterns and migration paths of groundwater in the region, determine cross-mining area hydrological connectivity, and provide early warning of groundwater inrush risks. This solves the problem that traditional single-mining-area analysis cannot analyze cross-regional hydrological correlations and is difficult to predict cross-boundary water inrush disasters.

[0043] The disaster-causing body identification unit communicates with the stratigraphic correlation unit, structural identification unit, and hydrological analysis unit, respectively. It integrates the stratigraphic correlation results from the stratigraphic correlation unit, the structural identification results from the structural identification unit, and the hydrological analysis results from the hydrological analysis unit to identify hidden disaster-causing bodies across mining areas. More specifically, the disaster-causing body identification unit incorporates a multi-source data fusion detection component and a disaster risk classification assessment component. These components work together to automatically identify the location, extent, and hazard level of hidden disaster-causing bodies such as goaf areas, collapse columns, and old mine water across mining areas. Combined with the distribution correlation of disaster-causing bodies across mining areas, it forms a dataset of the regional distribution of hidden disaster-causing bodies, providing support for risk early warning. This differs from the limitations of traditional single-mining-area systems that can only identify their own disaster-causing bodies and cannot grasp the risk of contiguous disasters.

[0044] The cross-mining area 3D geological automatic modeling module communicates with the unified spatiotemporal benchmark fusion module and the regional geological intelligent interpretation module. It is used to construct and dynamically update a cross-mining area 3D geological model based on the data foundation built by the data fusion module and the interpretation results from the regional geological intelligent interpretation module. The construction of the cross-mining area 3D geological model includes at least stratigraphic framework construction, structural modeling, seamless stitching of multi-mining area models, and disaster-causing body modeling. The cross-mining area 3D geological automatic modeling module adopts an AI-driven fully automatic modeling algorithm, embedding key components such as seamless stitching and incremental updates of cross-mining area models. Without manual intervention, it automatically constructs and dynamically updates the cross-mining area 3D geological model, completely solving the pain points of traditional cross-mining area modeling, such as large workload, long cycle, and large stitching errors.

[0045] Specifically, the cross-mining area 3D geological automatic modeling module includes: The stratigraphic framework construction unit is used to build a unified stratigraphic framework model covering the entire region based on the stratigraphic correlation results output by the stratigraphic correlation unit. More specifically, the stratigraphic framework construction unit has built-in global stratigraphic layer coding components and spatial topology relationship construction components. Based on the global stratigraphic layer coding components and spatial topology relationship construction components, a unified stratigraphic framework covering the entire region is constructed, which clarifies the spatial distribution, thickness and contact relationship of each stratum, providing a foundation for subsequent model splicing. This is different from the problem of traditional single mining areas building their own stratigraphic frameworks and being unable to be uniformly connected.

[0046] The structural modeling unit is used to construct a 3D model of the regional structure based on the structural identification results output by the structural identification unit. More specifically, the structural modeling unit has built-in structural feature parametric modeling components and 3D visualization rendering components. The structural feature parametric modeling components and 3D visualization rendering components work together, combined with structural information such as faults and fracture zones identified by the structural modeling unit, to construct a 3D model of the regional structure, fully representing the geological structural features across mining areas and realizing the full-domain visualization of cross-mining area structures.

[0047] The model stitching unit is used to stitch together the geological entity models of various mining areas (each mining area's geological entity model is generated by inversion of borehole data in the data base constructed by the stratigraphic framework construction unit based on the unified spatiotemporal benchmark fusion module) into a cross-mining area integrated 3D geological model. More specifically, the model stitching unit has built-in boundary feature matching components and automatic stitching error correction components. The boundary feature matching components and automatic stitching error correction components work together to automatically stitch together the geological entity (strata, rock mass) models of various mining areas. By extracting geological feature points at the boundaries of each mining area, accurate matching is performed, and stitching errors are automatically corrected (error controlled within ±10cm), eliminating mining area boundary stitching errors and achieving seamless integration of multiple mining area models to form a cross-area integrated 3D geological model. This is a core function that traditional single mining area modeling schemes cannot achieve.

[0048] The disaster-causing body modeling unit is used to construct a 3D visualization model of hidden disaster-causing bodies based on the disaster-causing body identification results output by the disaster-causing body identification unit. More specifically, the disaster-causing body modeling unit has built-in disaster-causing body 3D modeling components and risk level visualization annotation components. The disaster-causing body 3D modeling components and risk level visualization annotation components work together to construct a 3D visualization model of hidden disaster-causing bodies, so as to achieve accurate positioning and visualization of disaster-causing bodies and support cross-mining area disaster-causing body linkage query.

[0049] The dynamic update unit, in conjunction with the cross-mining area multi-source heterogeneous data access module, is used to trigger model updates in the corresponding unit when new or updated data is added. More specifically, the dynamic update unit is configured with incremental update components and partition update components. When new or updated data is added, the incremental update components and partition update components work together to complete the incremental update.

[0050] The 3D geological visualization module communicates with the cross-mining area 3D geological automatic modeling module to achieve multi-dimensional visualization and interactive operation of geological information across mining areas based on cross-mining area 3D geological models and a 3D GIS engine. The visualization and interactive operation include at least full-area display, layered display, risk rendering, and multi-level linked queries. This enables multi-dimensional visualization and interactive operation of geological information across mining areas.

[0051] Specifically, the 3D geological visualization module includes: The global display unit is used to showcase the integrated 3D geological model across mining areas, output by the cross-mining area 3D geological automatic modeling module, from a unified perspective. More specifically, the global display unit has built-in cloud rendering and lightweight loading components. These components work together to display information such as the cross-mining area 3D geological model, the distribution of hidden disaster-causing bodies, and the layout of monitoring points from a unified perspective. It supports lightweight loading of massive amounts of data, achieving "one map for the entire area, capturing everything in one map," thus solving the problem of limited visualization range and inability to present the overall situation of a traditional single mining area.

[0052] The layered display unit, communicating with the full-domain display unit, is used to display the integrated 3D geological model across mining areas in layers according to geological element type, color-coded according to risk level, and hierarchical access control according to administrative level on the integrated 3D geological model displayed in the full-domain display unit. More specifically, this layered display unit receives the integrated 3D geological model and data across mining areas and performs layered display operations. It achieves layered display control according to strata, structure, and disaster-causing body type through preset layer division rules. The layered display unit has built-in color coding components and permission adaptation components. The color coding component uses differentiated color coding rules to visually distinguish different geological elements and risk levels. The permission adaptation component combines permission control technology and matches user permissions with data levels according to the provincial, municipal, and mine-level permission division standards, so that different users can only access the layer data within their permission scope, thereby achieving data security control and precise permission adaptation.

[0053] The risk rendering unit, communicating with the overall display unit, generates regional geological risk heat maps based on the spatial coordinates of a cross-mining area integrated 3D geological model and real-time monitoring data. More specifically, the risk rendering unit includes a heat map generation component and a time-series animation rendering component. The heat map generation component generates a regional geological risk heat map based on received real-time monitoring data and risk assessment results, according to preset risk level coding rules. The time-series animation rendering component simultaneously renders the risk level changes and their time-series evolution trajectory, intuitively presenting the spatial distribution characteristics and temporal development trends of risks across mining areas. Through a cross-mining area data linkage processing mechanism, the risk rendering unit overcomes the technical limitations of traditional single-mining-area models that can only display their own risks and cannot grasp the interconnected changes in regional risks.

[0054] The linked query unit communicates with the full-domain display unit, the layered display unit, and the risk rendering unit. It is used to receive the visualization data and underlying basic data output by each unit and perform hierarchical linked query operations to link and switch the display of the cross-mining area integrated three-dimensional geological model and the layered view output by the layered display unit.

[0055] The linked query unit incorporates a perspective switching component and a data penetration query component, employing a combination of "perspective linkage switching + data penetration query" technology. The perspective switching component presets three levels of perspective parameters and linkage switching rules: provincial-level full area, municipal-level area, and mining area local area, achieving seamless linkage switching between the three perspectives. The data penetration query component has a built-in data association mapping mechanism. When any visualized area is clicked, it can automatically penetrate and associate underlying geological data, monitoring data, and interpretation results, and complete data retrieval and display, thereby achieving the query effect of "macro-level control and micro-level traceability," providing visualization technology support for multi-level supervision across mining areas. The above-mentioned technical implementation and functional effects of this linked query unit constitute core technical features that traditional single mining area visualization solutions do not possess.

[0056] The cross-mining area supervision and early warning module communicates with the 3D geological visualization module to realize intelligent supervision and proactive early warning of geological safety across mining areas based on visualization results and real-time monitoring data. The intelligent supervision and proactive early warning includes at least risk statistics, risk identification, early warning of contiguous disaster-causing areas, and closed-loop supervision and management.

[0057] Specifically, the cross-mining area supervision and early warning module includes: The risk statistics unit is used to identify the quantity, distribution, and level of various risk types in each mining area based on the regional geological risk heat map displayed by the 3D geological visualization module. More specifically, the risk statistics unit has built-in cross-mining area risk classification statistics component and data visualization analysis component, adopting the "cross-mining area risk classification statistics + data visualization analysis" technology. The risk classification statistics component presets geological risk classification standards, mining area division parameters, and statistical rules, and automatically classifies and calibrates the quantity, spatial distribution, and risk level of various types of geological risks in each mining area within the region. Based on the statistically calibrated data, the visualization analysis component automatically generates a regional risk statistics report according to a preset report generation template. It also has a built-in cross-mining area risk comparison algorithm to support quantitative comparative analysis of risks in multiple mining areas and of multiple types. Through a standardized data statistics and analysis mechanism, this risk statistics unit outputs accurate and comprehensive risk statistics data and analysis results, providing reliable data support for the overall decision-making of higher-level regulatory authorities.

[0058] The risk identification unit, communicating with the risk statistics unit, outputs high-risk mining areas and corresponding high-risk regions based on the identification results from the risk statistics unit. More specifically, the risk identification unit incorporates a risk assessment model module, a multi-factor weight analysis component, and a risk level determination component. The risk assessment model module integrates multi-dimensional indicators such as geological structure, hydrological conditions, and the distribution of disaster-causing bodies across mining areas to construct a preset assessment model. The multi-factor weight analysis component presets the weight allocation standards and weight calculation algorithms for each dimension indicator, performing weight quantification and comprehensive analysis on the received multi-dimensional data. Based on the weight analysis results, the risk level determination component performs risk level determination operations according to preset risk level classification thresholds, automatically identifying high-risk mining areas and high-risk regions within the cross-mining area. The risk identification unit also incorporates a key labeling and control triggering mechanism, automatically labeling identified high-risk objects and triggering key control processes in the system. Through a cross-mining area multi-dimensional data linkage analysis mechanism, it overcomes the technical limitations of traditional single-mining-area methods that cannot identify cross-regional high-risk associated areas.

[0059] The early warning unit communicates with the risk identification unit and is used to trigger the early warning mechanism when a contiguous disaster-prone area is identified.

[0060] More specifically, the early warning unit has a built-in component for determining contiguous risk thresholds and a component for intelligently pushing early warning information. It adopts the technology of "determining contiguous risk thresholds + intelligently pushing early warning information" to automatically trigger the early warning mechanism, classify the early warning level (general, relatively severe, serious, and extremely serious), clarify the scope of the early warning and prevention and control recommendations, and push the early warning information to the corresponding regulatory departments at all levels and mining areas simultaneously to achieve cross-regional collaborative early warning. This is different from the limitation of traditional single mining areas that can only issue early warnings themselves and cannot link with surrounding mining areas.

[0061] The progress tracking unit communicates with the early warning unit and is used to track the progress of the treatment.

[0062] More specifically, the progress tracking unit has a built-in component for hierarchical assignment of governance tasks and a component for real-time progress reporting. It adopts the technology of "hierarchical assignment of governance tasks + real-time progress reporting" to track the governance progress of high-risk areas and hidden disaster-causing bodies, record governance measures and governance effects, realize full-process control of governance work, and support cross-mine area governance progress comparison and analysis.

[0063] The cloud-based collaboration and access control module communicates with the cross-mine area monitoring and early warning module and the 3D geological visualization module to support the collaborative operation of a multi-level monitoring system.

[0064] Specifically, the cloud-based collaboration and access control module includes: The access control unit is used to classify user roles by province, city, mine, and job position. Working in conjunction with the hierarchical display unit, it clarifies data viewing and operation permissions, achieving data isolation and security control to ensure data security across mining areas and prevent data leakage. More specifically, the access control unit has a built-in fine-grained access control mechanism based on the RBAC access control model. It classifies user roles by province, city, mine, and job position, clarifying the data viewing and operation permissions (entry, editing, review, and alerts) for each role, thus achieving data isolation and security control.

[0065] The multi-level collaborative unit communicates with the permission management unit and is used to support data reporting from multiple mining areas, municipal-level review, provincial-level supervision, and national-level scheduling based on the permission management results.

[0066] More specifically, the multi-level collaborative unit has built-in distributed collaborative office components and real-time data synchronization components. The distributed collaborative office components and real-time data synchronization components work together to support data reporting from multiple mining areas, city-level summary review, provincial-level supervision and analysis, and national-level overall coordination and scheduling.

[0067] The traceability management unit connects to both the access control unit and the multi-level collaboration unit to record operational behaviors, achieving full traceability and auditability. More specifically, the traceability management unit has built-in real-time operation log recording and data version management components. These components work together to support online updates of data, models, and governance records, automatically recording all operational behaviors (operator, operation time, operation content) to achieve full traceability and auditability.

[0068] The cloud deployment unit provides a cloud-based operating environment for the permission management unit, multi-level collaboration unit, and record management unit. More specifically, the cloud deployment unit adopts a cloud-based distributed deployment architecture (supporting elastic scaling), supports massive data storage and high-concurrency access, adapts to different terminals (PC, mobile, and large-screen terminals), and uses "terminal adaptive adaptation" technology to ensure that users at all levels can access the system of this invention more conveniently.

[0069] A method for constructing the aforementioned intelligent three-dimensional geological single-map system across mining areas includes the following steps: Step S1: Collect multi-source heterogeneous data from multiple mining areas in different regions.

[0070] Specifically, the system accesses multi-source heterogeneous data from all coal and non-coal mines within a certain province, including: geological survey reports, borehole columnar sections, geophysical data (seismic and electromagnetic), remote sensing image data (0.5m resolution), mining progress logs, hydrological monitoring data (groundwater level and water quality), and survey data on hidden disaster-causing bodies (mining-out areas and collapse columns). The system automatically imports data in batches through its built-in interface, and manually supplements any missing data to ensure comprehensive data coverage.

[0071] Step S2: Perform coordinate transformation, spatiotemporal alignment and format normalization on the collected multi-source heterogeneous data to construct a data base with a unified spatiotemporal reference across mining areas.

[0072] Step S2 specifically includes: Step S21: Convert the accessed multi-source heterogeneous data into the national geodetic coordinate system; Step S22: Perform precise spatiotemporal alignment of the transformed multi-source heterogeneous data across different mining areas and time dimensions; Step S23: Convert the aligned multi-source heterogeneous data of different formats into a unified format compatible with the system; Step S24: Clean and verify the normalized data to form the data base.

[0073] In this embodiment, a seven-parameter coordinate transformation algorithm (combined with partition fitting optimization technology) is used to uniformly convert all accessed data (originally mostly in Beijing 54 and local coordinate systems) into the CGCS2000 national geodetic coordinate system; based on timestamp synchronization technology, spatiotemporal alignment of data from different mining areas and different time periods is completed; all data is converted into a unified vector format, Z-score algorithm is used to remove abnormal monitoring data, linear interpolation algorithm is used to fill missing borehole data, data quality verification is completed, and a cross-mining area single map data base with a unified spatiotemporal benchmark for the entire province is constructed.

[0074] Step S3: Based on the data base, perform automatic intelligent interpretation of regional geological information across mining areas and output the interpretation results.

[0075] Step S3 specifically includes: Step S31: Automatically identify the stratigraphic lithology and stratigraphic contact relationships of different mining areas to achieve accurate comparison and seamless connection of strata of the same origin across mining areas; Step S32: Parallel execution of automatic inference of the distribution range, strike and dip angle of faults and fracture zones in the region, as well as intelligent analysis of the distribution pattern and migration path of groundwater in the region, and judgment of hydrological connectivity across mining areas; Step S33: Integrate stratigraphic correlation results, structural identification results, and hydrological analysis results to automatically identify the location, extent, and hazard level of hidden disaster-causing bodies across mining areas.

[0076] In this embodiment, a regional geological big data model is activated to intelligently interpret the unified dataset: The "strata lithology feature extraction + co-originating strata fingerprint matching" technology is used to automatically compare the lithology of strata in various mining areas across the province, achieving seamless cross-mining area connections of co-originating strata; the "deep learning semantic segmentation + tectonic trend inference" technology is used to infer the distribution range and orientation of faults and fracture zones throughout the province, achieving structural contiguous identification; the "hydrological numerical simulation + groundwater migration path tracking" technology is used to analyze the groundwater migration paths in various mining areas and determine cross-mining area hydrological connectivity; and the "multi-source data fusion detection + disaster risk classification assessment" technology is used to identify hidden disaster-causing bodies such as goaf areas and collapse columns throughout the province, marking their location, range, and hazard level, and generating a regional geological AI interpretation report.

[0077] Step S4: Based on the data base and interpretation results, construct a three-dimensional geological model across mining areas and update it dynamically.

[0078] Step S4 specifically includes: Step S41: Based on the stratigraphic correlation results of step S3, construct a unified stratigraphic framework model covering the entire region; Step S42: Integrate the structural identification results from step S3 into the stratigraphic framework model to construct a three-dimensional model of the regional structure; Step S43: Automatically stitch together the geological entity models of each mining area, eliminate stitching errors at the boundaries of the mining areas, and form a unified three-dimensional geological model covering the entire region; Step S44: Integrate the disaster-causing body identification results from step S3 into the global integrated three-dimensional geological model to construct a three-dimensional visualization model of the hidden disaster-causing body. Step S45: Automatically trigger incremental model updates when adding or updating data.

[0079] In this embodiment, based on the AI ​​interpretation results and data foundation, the system automatically initiates the modeling process. It adopts the "full-area stratigraphic layer coding + spatial topological relationship construction" technology to construct a unified stratigraphic framework model for the entire province, incorporating contiguous structural information. It uses the "boundary feature matching + automatic splicing error correction" technology to splice the geological entity models of each mining area, eliminating splicing errors at mining area boundaries, and constructing a seamless spliced ​​three-dimensional geological model across mining areas for the entire province. The system integrates data on hidden disaster-causing bodies into the model to generate a single map model of hidden disaster-causing bodies. It sets up daily incremental updates so that when a mining area adds new borehole data or updates monitoring data, the corresponding regional model is automatically updated without the need to rebuild the entire province model.

[0080] Step S5: Based on the three-dimensional geological model across mining areas, realize the multi-dimensional visualization and interactive operation of geological information across mining areas.

[0081] Step S5 specifically includes: Step S51: Display the three-dimensional geological model and monitoring information of the entire mining area from a unified perspective; Step S52: Based on the overall display, display the geological elements in layers and set viewing permissions according to the level of access control technology. Step S53: Based on the layered display, generate a regional geological risk heat map and dynamically render the changes in risk level and the time-series evolution process. Step S54: Based on risk rendering, support hierarchical linkage switching and data penetration query from the provincial-level overall perspective, the municipal-level regional perspective, and the mining area local perspective.

[0082] In this embodiment, based on a cloud-based 3D GIS engine, the "cloud rendering + lightweight loading" technology is used to realize a single 3D geological map visualization of the province across mining areas: it supports the linkage and switching between three perspectives: the entire province, the city-level region, and the local mining area, and displays the strata, structures, and hidden disaster-causing bodies in layers; it uses four colors—red, orange, yellow, and green—to distinguish different risk levels, and uses the "dynamic generation of heat map + time-series evolution animation rendering" technology to generate a geological risk heat map of the entire province, dynamically rendering the time-series evolution process of risks; clicking on any area allows for penetrating viewing of the underlying borehole data, monitoring data, and interpretation results.

[0083] Step S6: Based on the visualization results and real-time monitoring data, realize intelligent supervision and proactive early warning of geological safety across mining areas; through the cloud collaboration and permission management module, support the collaborative operation of the multi-level supervision system.

[0084] Step S6 specifically includes: Step S61: Automatically count the quantity, distribution, and level of geological risks of each type and type in each mining area within the region; Step S62: Automatically identify high-risk mining areas and high-risk regions across mining areas based on statistical results; Step S63: Automatically trigger the early warning mechanism and classify the early warning levels for the identified contiguous disaster-prone areas; Step S64: Track the progress of remediation of high-risk areas and hidden disaster-causing entities after the early warning is issued; Step S65: After the governance is completed, conduct verification and deregistration to form a closed-loop management mechanism of early warning, assignment, governance, verification and deregistration.

[0085] In this embodiment, the technology of "cross-mining area risk classification statistics + data visualization analysis" is adopted to automatically count the number and level of geological risks in all cities and mining areas in the province. The technology of "multi-factor weight analysis + risk level determination" is adopted to identify high-risk mining areas and contiguous disaster-causing areas. For high-risk areas with strong hydrological connectivity and contiguous fault distribution, the technology of "contiguous risk threshold determination + intelligent push of early warning information" is adopted to automatically trigger severe early warnings and push early warning information to the corresponding municipal and county-level regulatory departments and mining areas, clarifying the scope of the early warning and prevention and control recommendations. The regulatory departments track the progress of remediation through the system, enter the remediation measures and effects, and complete the closed-loop management of "early warning - assignment - remediation - verification - cancellation".

[0086] In this embodiment, a cloud-based distributed deployment architecture is adopted to support collaborative access by users at the provincial, municipal, and mine levels: mine users are responsible for data reporting and daily monitoring; municipal users are responsible for data review, aggregation, and governance supervision within their jurisdiction; and provincial users are responsible for province-wide supervision, risk analysis, and overall scheduling. Through hierarchical permission control based on the RBAC permission model, data isolation and precise authorization for users at all levels are achieved. All operations are automatically recorded, traceable, and auditable, enabling normalized and intelligent supervision of the entire mining geology area in the province.

[0087] Regarding the specific structure of this invention, it should be noted that the connection relationships between the various component modules used in this invention are definite and achievable. Except as specifically described in the embodiments, their specific connection relationships can bring about corresponding technical effects and solve the technical problems proposed by this invention without relying on the execution of corresponding software programs. The models of the components, modules, and specific components appearing in this invention, the connection methods between them, and the conventional usage methods and expected technical effects brought about by the above technical features, unless specifically described, are all publicly disclosed content in patents, journal articles, technical manuals, technical dictionaries, and textbooks that can be obtained by those skilled in the art before the application date, or belong to conventional technology, common knowledge, and other existing technologies in this field. There is no need to elaborate, which makes the technical solution provided in this case clear, complete, and achievable, and can reproduce or obtain corresponding physical products based on this technical means.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cross-mining area intelligent three-dimensional geological mapping system, characterized in that, include: A cross-mining-area multi-source heterogeneous data access module is used to collect multi-source heterogeneous data from multiple mining areas in different regions; The unified spatiotemporal reference fusion module communicates with the cross-mining area multi-source heterogeneous data access module. It is used to perform coordinate transformation, spatiotemporal alignment and format normalization on the collected multi-source heterogeneous data to build a data foundation for a unified spatiotemporal reference across mining areas. The regional geological intelligent interpretation module is communicatively connected to the unified spatiotemporal benchmark fusion module. It is used to automatically and intelligently interpret the data in the data base across mining areas based on a pre-trained geological large model, and output the interpretation results. The automatic intelligent interpretation includes at least stratigraphic correlation, structural identification, hydrological analysis and disaster-causing body identification. The cross-mining area 3D geological automatic modeling module is connected to the unified spatiotemporal benchmark fusion module and the regional geological intelligent interpretation module. It is used to construct a cross-mining area 3D geological model and dynamically update it based on the data base built by the data fusion module and the interpretation results of the regional geological intelligent interpretation module. The 3D geological visualization module communicates with the cross-mining area 3D geological automatic modeling module to realize multi-dimensional visualization and interactive operation of cross-mining area geological information based on the cross-mining area 3D geological model and 3D GIS engine. The cross-mining area supervision and early warning module communicates with the 3D geological visualization module to realize intelligent supervision and proactive early warning of geological safety across mining areas based on visualization results and real-time monitoring data; The cloud-based collaboration and access control module communicates with the cross-mine area monitoring and early warning module and the 3D geological visualization module to support the collaborative operation of a multi-level monitoring system.

2. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 1, characterized in that, The cross-mining area multi-source heterogeneous data access module includes: The format adaptation unit has built-in multi-format adaptation interfaces and intelligent adaptation algorithms to automatically adapt to multiple data formats; The coordinate adaptation unit communicates with the format adaptation unit and is used to identify and calibrate the coordinate system and scale of the multi-source heterogeneous data after format adaptation. The access mode unit communicates with the coordinate adaptation unit and integrates batch import mode, real-time acquisition mode and manual recording mode. It is used to connect multi-source heterogeneous data after coordinate calibration to the unified spatiotemporal reference fusion module through at least one of batch import, real-time acquisition and manual recording.

3. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 2, characterized in that, The unified spatiotemporal reference fusion module includes: The coordinate transformation unit is used to convert the multi-source heterogeneous data received from the access mode unit into the national geodetic coordinate system. The spatiotemporal alignment unit communicates with the coordinate transformation unit and is used to perform spatiotemporal alignment on multi-source heterogeneous data from different mining areas and different time dimensions after coordinate transformation. The format normalization unit communicates with the spatiotemporal alignment unit and is used to convert multi-source heterogeneous data of different formats after spatiotemporal alignment into a unified format that is compatible with the system. The quality verification unit, which communicates with the format normalization unit, is used to perform outlier detection, missing value filling, and duplicate data removal on the multi-source heterogeneous data after format normalization, forming a data base for a unified spatiotemporal benchmark across mining areas.

4. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 1, characterized in that, The regional geological intelligent interpretation module includes: The stratigraphic correlation unit is used to perform cross-mining area stratigraphic correlation based on the drilling borehole data and stratigraphic data in the data base output by the unified spatiotemporal reference fusion module. The structure identification unit is used to identify cross-mining area structures based on remote sensing image data and geophysical data in the data base. The hydrological analysis unit is used to perform cross-mining area hydrological connectivity analysis based on hydrogeological data in the data base. The disaster-causing body identification unit is connected to the stratigraphic correlation unit, the structural identification unit, and the hydrological analysis unit, respectively. It is used to integrate the stratigraphic correlation results output by the stratigraphic correlation unit, the structural identification results output by the structural identification unit, and the hydrological analysis results output by the hydrological analysis unit to identify hidden disaster-causing bodies across mining areas.

5. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 4, characterized in that, The cross-mining area three-dimensional geological automatic modeling module includes: Stratigraphic framework building unit is used to construct a unified stratigraphic framework model covering the entire domain based on the stratigraphic correlation results output by the stratigraphic correlation unit; The construction modeling unit is used to build a three-dimensional model of the region's structure based on the structure recognition results output by the structure recognition unit; The model stitching unit is used to stitch together the geological entity models of various mining areas into a cross-mining area integrated three-dimensional geological model; the geological entity models of each mining area are generated by inversion of borehole data in the data base constructed by the stratigraphic framework construction unit based on the unified spatiotemporal benchmark fusion module. The disaster-causing body modeling unit is used to construct a three-dimensional visualization model of hidden disaster-causing bodies based on the disaster-causing body identification results output by the disaster-causing body identification unit. The dynamic update unit, which works in conjunction with the cross-mining area multi-source heterogeneous data access module, is used to trigger the model update of the corresponding unit when new or updated data is added.

6. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 5, characterized in that, The three-dimensional geological visualization module includes: The global display unit is used to display the cross-mining area integrated 3D geological model output by the cross-mining area 3D geological automatic modeling module from a unified perspective; The layered display unit communicates with the global display unit and is used to display the integrated three-dimensional geological model across mining areas in layers according to geological element type, color-coded according to risk level, and hierarchical access control according to administrative level on the global display unit. The risk rendering unit communicates with the global display unit and is used to generate regional geological risk heat maps based on the spatial coordinate information and real-time monitoring data of the cross-mining area integrated three-dimensional geological model. The linked query unit communicates with the full-domain display unit, the hierarchical display unit, and the risk rendering unit. It is used to receive the visualization data and underlying basic data output by the full-domain display unit, the hierarchical display unit, and the risk rendering unit, and to perform hierarchical linked query operations.

7. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 6, characterized in that, The cross-mining area monitoring and early warning module includes: The risk statistics unit is used to identify the quantity, distribution and level of each risk type in each mining area based on the regional geological risk heat map displayed by the three-dimensional geological visualization module. The risk identification unit communicates with the risk statistics unit and is used to output high-risk mining areas and corresponding high-risk regions based on the identification results of the risk statistics unit. The early warning unit communicates with the risk identification unit and is used to trigger the early warning mechanism when a contiguous disaster-prone area is identified. The progress tracking unit communicates with the early warning unit and is used to track the progress of the treatment.

8. The intelligent three-dimensional geological mapping system for cross-mining areas according to claim 6, characterized in that, The cloud-based collaboration and access management module includes: The access control unit is used to classify user roles by province, city, mine and job position, and works in conjunction with the hierarchical display unit to clarify data viewing permissions and operation permissions. The multi-level collaborative unit communicates with the access control unit and is used to support data reporting from multiple mining areas, municipal-level review, provincial-level supervision and national-level scheduling based on access control results. The traceability management unit is connected to the access control unit and the multi-level collaboration unit respectively, and is used to record operation behavior to achieve full traceability and auditability. The cloud deployment unit is used to provide a cloud operating environment for the permission management unit, multi-level collaboration unit, and record management unit.

9. A method for constructing a cross-mining area intelligent three-dimensional geological single-map system as described in any one of claims 1-8, characterized in that, Includes the following steps: Step S1: Collect multi-source heterogeneous data from multiple mining areas in different regions; Step S2: Perform coordinate transformation, spatiotemporal alignment and format normalization on the collected multi-source heterogeneous data to construct a data base for a unified spatiotemporal reference across mining areas; Step S3: Based on the data base, perform automatic intelligent interpretation of cross-mining area geological information and output the interpretation results; Step S4: Based on the data base and interpretation results, construct a three-dimensional geological model across mining areas and update it dynamically; Step S5: Based on the cross-mining area three-dimensional geological model, realize the multi-dimensional visualization and interactive operation of cross-mining area geological information; Step S6: Based on visualization results and real-time monitoring data, realize intelligent supervision and proactive early warning of geological safety across mining areas; The cloud-based collaboration and access control module supports the collaborative operation of a multi-level regulatory system.

10. The method for constructing a cross-mining area intelligent three-dimensional geological single map system according to claim 9, characterized in that, Step S2 specifically includes: Step S21: Convert the accessed multi-source heterogeneous data into the national geodetic coordinate system; Step S22: Perform precise spatiotemporal alignment of the transformed multi-source heterogeneous data across different mining areas and time dimensions; Step S23: Convert the aligned multi-source heterogeneous data of different formats into a unified format compatible with the system; Step S24: Clean and verify the normalized data to form the data base.

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