Coal mine water control three-dimensional dynamic modeling method and system based on multi-source data fusion

Abstract

The invention discloses a coal mine water control three-dimensional dynamic modeling method and system based on multi-source data fusion, and belongs to the technical field of coal mine water control engineering. The method comprises the following steps: acquiring coal mine water control data; the coal mine water control data comprises drilling data, contour line data, hydrological monitoring data, geological structure data and engineering data; constructing a stratum model according to the drilling data; constructing a fault model according to the contour line data and the geological structure data; carrying out topological fusion on the fault model and the stratum model to obtain a fusion model; when the working face advances or new drilling data is input, the fusion model is dynamically updated; and performing linkage correction on the fusion model. According to the method, through multi-source data deep fusion, intelligent algorithm accelerated modeling and dynamic interactive decision making, the efficiency and precision of coal mine water prevention and control three-dimensional modeling are remarkably improved, key technical support is provided for water disaster risk prevention and control, and the method has wide engineering application value.

Description

Technical Field

[0001] This invention relates to the field of coal mine water control engineering technology, specifically to a three-dimensional dynamic modeling method and system for coal mine water control based on multi-source data fusion. It is particularly suitable for mines with different geological conditions in coalfields such as North China, South China, and Northwest China, and can be widely used in water hazard risk prediction, prevention and control scheme simulation, and underground safety production decision support. Background Technology

[0002] Traditional coal mine water control relies heavily on manual experience and static data, making it difficult to adapt to dynamically changing underground geological conditions. On the one hand, the average data silo rate of coal enterprises nationwide reaches 78%, with data formats from geological exploration, hydrological monitoring, and mining design systems being incompatible, creating information barriers. On the other hand, traditional 3D geological modeling takes 45-60 days, far behind the daily advance speed of 8-15 meters at the working face, and 85% of mines still rely on manual experience for water hazard early warning, with a manual analysis miss rate as high as 37%, making it difficult to meet the needs of intelligent mine construction for efficient and precise water control. Against this backdrop, the construction of a three-in-one intelligent water control system integrating "data hub - intelligent modeling - decision support" integrates multi-source data governance, intelligent algorithm modeling, and mixed reality interaction technology to achieve compressed modeling cycle, improved model accuracy, and simplified operation process. This system can not only accurately identify water hazard risks and shorten emergency response time, but also promote the transformation of coal mine water control work from "experience-driven" to "data-driven". It has important practical significance and engineering value for ensuring national energy security, reducing economic losses from safety accidents, and helping to achieve the goal of "carbon peaking and carbon neutrality".

[0003] From the perspective of domestic and international research, foreign countries started earlier in the field of coal mine water control modeling and data management, and have formed a relatively mature technical system. In terms of data management, the U.S. Geological Survey (USGS) has led the development of comprehensive hydrogeological data standards, achieving centralized management and open sharing of groundwater and surface water data through the National Water Resources Information System (NWIS). Europe, relying on the Environmental Information Sharing Network (EIONET), has broken down cross-border data barriers, constructing regional hydrogeological models for the Alpine cross-border coal mining area, and improving the joint assessment capability of cross-border water hazard risks. In terms of security, large Australian coal mines use the SHA-256 encryption algorithm to store key geological data such as borehole locations and fault parameters, and have established a three-level access control system of "administrator-engineer-operator" to ensure data storage security and access compliance. In the field of intelligent modeling and decision support, a Canadian research team has developed a complex structural modeling system that integrates Kriging interpolation with geological knowledge rules (such as stratigraphic attitude constraints). This system achieves a 98% accuracy rate in identifying structures such as faults and collapse columns, reducing the modeling cycle to within 72 hours and supporting dynamic correction of model parameters as the working face advances. The US-developed water hazard risk decision platform integrates a multi-parameter dynamic model of water inrush coefficients, achieving over 92% accuracy in automatically delineating risk areas. It also incorporates VR / AR technology to overlay geological models with real-world scenarios, providing decision-makers with an immersive environment for scenario simulation. Furthermore, the UK utilizes artificial intelligence algorithms to perform deep learning on massive aquifer monitoring data, constructing a dynamic water hazard risk prediction model that can provide early warnings of potential risks up to 72 hours in advance. Australia's IoT-based real-time hydrological monitoring platform has increased early warning response speed by 50%, providing a technological paradigm for the intelligent development of water control in coal mines abroad.

[0004] In recent years, China has closely followed international cutting-edge technological trends and made significant progress in the construction of a data hub for coal mine water control, as well as in intelligent modeling and decision support. In terms of data management, the "Geological Cloud" National Geological Big Data Sharing Service Platform has been launched, and a coal mine geological data coding standard covering 12 types of data, including borehole data, geophysical exploration data, and hydrological monitoring data, has been formulated, achieving standardized management of multi-source data. Large-scale bases in major coal-producing areas are using blockchain technology to encrypt and store key data such as fault locations and aquifer thickness, ensuring data immutability. Simultaneously, the data retrieval chain has been optimized, reducing the response time for key data retrieval to ≤1 second. In the field of intelligent modeling technology, research institutions such as China University of Mining and Technology and Chang'an University have developed a 3D geological modeling system based on numerical simulation platforms such as MODFLOW and UDEC, integrating Kriging interpolation and adaptive cross-section selection algorithms. This system achieves an accuracy of ≥96% in identifying complex structures, reducing the modeling cycle from the traditional 45 days to within 72 hours. Meanwhile, institutions such as the China Coal Research Institute and North China University of Water Resources and Electric Power have constructed an intelligent decision-making platform for water hazard risk. This platform features a built-in aquitard thickness calculation engine with a response time of ≤0.8 seconds and an accuracy of over 90% in delineating water inrush risk areas. Furthermore, it has deployed UAV remote sensing monitoring systems and underground telemetry equipment in mining areas in Shandong and Shanxi provinces, achieving full coverage of dynamic hydrological monitoring in mines with different geological conditions in North China, South China, and Northwest China. In the field of mixed reality applications, China University of Mining and Technology has developed a GIS-based 3D geological visualization platform that supports interactive functions such as horizontal / vertical model cross-section and highlighting of specific strata, providing a localized and easily implementable solution for intelligent water control in domestic coal mines.

[0005] Coal mine water hazards are one of the major disasters threatening safe production in mines. Traditional modeling relies on manual strata subdivision and manual interpolation calculations, with a modeling cycle of 45-60 days, which lags far behind the working face advancement speed. This results in the model being unable to reflect geological dynamics in real time and missing water hazard early warning windows. Modeling of key structures such as faults and collapse columns relies on manual experience judgment and lacks intelligent algorithm support. The accuracy rate of structure identification is generally below 75%, and misjudgment of structures can easily lead to missed detection of water inrush risks. Existing modeling software requires professional geological engineers to master complex subdivision rules and parameter settings (such as grid density and interpolation weights), which are difficult for non-professionals to operate. Moreover, it is developed for specific coalfield geological conditions and cannot adapt to the hydrogeological differences of mines in different regions. Modification of plan and profile maps requires repeated manual calibration, and the data correction error often exceeds 8%, which cannot meet the geological specification requirement of "consistency between plan and profile" and leads to distorted basis for water prevention and control measures. The model is displayed in static reports or two-dimensional charts, lacking immersive interactive functions. The response of key parameters such as water inrush coefficient calculation and aquitard thickness analysis is delayed (>5 seconds), which cannot support rapid on-site decision-making.

[0006] Therefore, it is urgent to build an intelligent modeling system with a closed loop of "data-model-decision" to simplify the modeling process, improve accuracy, and facilitate operation, so as to meet the needs of efficient and intelligent water control in coal mines. Summary of the Invention

[0007] The purpose of this invention is to provide a three-dimensional modeling method and system for coal mine water control driven by multi-source data, accelerated by intelligent algorithms, and with dynamic interactive visualization. This system can reduce the modeling cycle to within 30 hours, achieve a complex structure identification accuracy of ≥90%, and a water inrush risk prediction accuracy of ≥90%, thus providing precise support for water control decision-making.

[0008] To address the aforementioned technical problems, this invention provides a three-dimensional dynamic modeling method for coal mine water control based on multi-source data fusion, comprising the following steps: Acquire coal mine water control data; the coal mine water control data includes borehole data, contour data, and geological structure data; Based on borehole data, a formation model is constructed; Based on contour line data and geological structure data, a fault model is constructed; The fault model and the stratigraphic model are topologically fused to obtain a fused model; The fusion model is dynamically updated when new data points are acquired. The fusion model is adjusted accordingly.

[0009] Preferably, a formation model is constructed based on borehole data, specifically including the following steps: The borehole data is preprocessed to obtain borehole layer data; A formation model was constructed based on borehole stratification data.

[0010] Preferably, the borehole data is preprocessed to obtain borehole layer data, specifically including the following steps: Outliers in borehole data are removed by data cleaning algorithms, and stratigraphic profiles are generated based on Kriging interpolation and geological knowledge rules to obtain borehole stratification data.

[0011] Preferably, a formation model is constructed based on borehole stratification data, specifically including the following steps: The Delaunay triangulation algorithm is used to transform the point set of borehole layer data into an irregular triangular network with a minimum interior angle of ≥30°. Spatial relationships between adjacent strata are established using topological relation algorithms.

[0012] Preferably, a fault model is constructed based on contour line data and geological structure data, specifically including the following steps: Based on contour data, a three-dimensional surface model of the geological strata is constructed. Fault models are constructed based on geological structural data and three-dimensional stratigraphic surface models.

[0013] Preferably, a three-dimensional geological surface model is constructed based on contour data, specifically including the following steps: Group contour points according to Z value to identify contour clusters of the same layer; The Delaunay triangulation is constructed using the point-by-point insertion method. First, a super triangle is created that surrounds all points. Then, contour points are inserted one by one, and the diagonals are swapped through a local optimization algorithm. By splicing together triangular meshes of different contour lines in stratigraphic order, a continuous three-dimensional stratigraphic surface model is formed.

[0014] Preferably, a fault model is constructed based on geological structural data and a three-dimensional stratigraphic surface model, specifically including the following steps; Based on the fault dip angle and displacement in the geological structural data, the offset distance of the edge point of the hanging wall is derived, and the coordinates of the footwall boundary are generated. Based on the fault dip angle, strike and elevation difference, a three-dimensional geometric surface of the fault is constructed, and the thickness parameters of the fault are supplemented to form a basic geometric model of the fault. The fault's basic geometric model is interactively computed with a three-dimensional stratigraphic surface model. A topological algorithm is used to cut the stratigraphic triangular network that is truncated by the fault, reconstructing the continuity of the strata on both sides of the fault to obtain the fault model.

[0015] Preferably, when new data points are acquired, the fusion model is dynamically updated, specifically including the following steps: Using the R-tree spatial indexing algorithm, the triangular mesh area within a radius of 50m is located with the new data point as the center. Delete the old triangulation within the affected area, and re-perform Delaunay subdivision based on the new data points and the surrounding original data; The elevation correction of the vertices of the triangulation network with non-original data points is performed using a surface spline function. The function expression is as follows: (1) Undetermined coefficients (2) In the formula: An empirical parameter for adjusting the curvature of the surface; x represents the plane X-axis coordinate of the point whose elevation is to be determined; x i y represents the plane X-axis coordinate of the i-th original data point; y represents the plane Y-axis coordinate of the point whose elevation is to be determined; y i r represents the Y-axis coordinate of the i-th original data point; i F represents the Euclidean distance between the point whose elevation is to be determined and the i-th original data point on the plane; iThe coefficients to be determined for the i-th original data point are used to characterize the influence weight of the original data point on the interpolation surface; W(x,y) represents the elevation value of the point to be determined by the surface spline function. Undetermined coefficients The following system of equations was used to obtain the solution: (3) In the formula, ; It is the elastic coefficient at point j. The matrix form of the given system of equations is: (4) In the formula: A represents the coefficient matrix; B represents the constant term matrix; X represents the undetermined coefficient matrix; D represents the empirical parameter; The known elevation value represents the nth original data point; This represents the undetermined coefficient corresponding to the nth original data point; Represents the coefficients of the linear term; C represents the coefficient related to which point. ｊ = 16πD / k ｊ (Cn when j=n), where k ｊ C is the elastic coefficient at point j. In general geological surface interpolation, C is often set to zero so that the obtained surface spline function matches the original data at the known points, that is, the surface passes through the original data points.

[0016] Preferably, the fusion model is subjected to linkage correction, which specifically includes the following steps: Centered on the model modification point, the dilation algorithm is used to search for adjacent triangles layer by layer to obtain the original data points within the search range; for the vertices of the triangular mesh that are not original data points, spline surface interpolation is used to calculate the new elevation. When the position of fault or coal seam line on the profile is modified, the spatial coordinates of the modified point are extracted, fed back to the corresponding position on the plan view, and the fault trace and coal seam contour line are updated. When key points at the section lines in the plan view are modified, they are mapped to the corresponding section view to correct the stratigraphic interfaces and structural lines on the section.

[0017] This invention also provides a three-dimensional dynamic modeling system for coal mine water control based on multi-source data fusion, comprising: The acquisition module is used to acquire coal mine water control data; the coal mine water control data includes borehole data, contour data, hydrological monitoring data, geological structure data, and engineering data; The formation model building module is used to build formation models based on borehole data; The fault model building module is used to build fault models based on contour data and geological structure data. The fusion module is used to perform topological fusion of the fault model and the stratigraphic model to obtain a fused model. The update module is used to dynamically update the fusion model when new data points are acquired. The linkage correction module is used to perform linkage correction on the fusion model.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention significantly improves the efficiency and accuracy of 3D modeling for water control in coal mines through deep fusion of multi-source data, accelerated modeling using intelligent algorithms, and dynamic interactive decision-making. It provides key technical support for water hazard risk prevention and control and has broad engineering application value. Attached Figure Description

[0019] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0020] Figure 1 This is the overall technical roadmap of the present invention; Figure 2 This is a system architecture diagram of the present invention; Figure 3 This is a flowchart of the Delaunay triangulation optimization process. Detailed Implementation

[0021] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0023] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0024] The present invention will now be described in further detail with reference to the accompanying drawings: To address the shortcomings of traditional 3D modeling, such as reliance on manual operation, long cycles, low accuracy, and weak ability to identify complex structures, this method integrates four core components: multi-source heterogeneous data governance, intelligent 3D modeling, cross-sectional and planar alignment correction, and mixed reality decision-making interaction. It incorporates key technologies such as Kriging interpolation, Delaunay triangulation optimization, and dilation algorithms to achieve a modeling cycle of less than 30 hours, a complex structure identification accuracy of ≥90%, and a water inrush risk prediction accuracy of ≥90%. The system adopts a modular design, supporting automatic borehole data parsing, rapid contour line modeling, and dynamic fault correction. The simplified operation process allows even non-technical personnel to complete modeling after basic training, resolving the pain points of existing technologies such as "difficult modeling, cumbersome operation, and low accuracy." This provides precise and efficient technical support for coal mine water hazard risk prevention and control under complex hydrogeological conditions.

[0025] To better illustrate the technical effects of the present invention, the present invention provides the following specific embodiments to illustrate the above technical process: Example 1: A three-dimensional dynamic modeling method for coal mine water control based on multi-source data fusion, constructing a three-in-one technical architecture of "data hub - intelligent modeling - decision support", wherein: Data central layer: Enables standardized governance, distributed storage, and security management of multi-source heterogeneous data; Intelligent modeling layer: It completes the construction, dynamic updating and accuracy optimization of 3D geological models through adaptive algorithms; Decision support layer: Relying on mixed reality technology to realize model interaction and water prevention and control decision output, the three layers work together to form a closed loop of the whole process of "data-model-decision".

[0026] The specific implementation steps of the modeling method include: Step 1: Governance of Multi-Source Heterogeneous Data Data Collection and Classification: 12 categories of core data for coal mine water control, specifically including: A. Borehole data: borehole number, spatial coordinates (X / Y / Z), stratigraphic depth, lithological description, thickness of aquitard, etc. B. Contour data: A set of X / Y / Z coordinate points stored in Excel format (including contour lines of the coal seam floor and the aquifer top interface); C. Hydrological monitoring data: real-time water pressure, water inflow, soil moisture and historical water damage records; D. Geological structural data: fault attitude (dip, strike, elevation), collapse column boundary coordinates, exploration lines and profile data; E. Engineering data: centerline coordinates of the tunnel, cross-sectional shape parameters (arch / rectangular / trapezoidal), and horizontal drilling trajectory data.

[0027] Data standardization processing: Develop a geological data governance engine to achieve unified data format conversion (such as LAS to JSON, CAD graphics to vector data) through Python scripts, formulate the "Coal Mine Water Control 3D Modeling Data Coding Standard", and standardize the definition of data fields (such as borehole numbers adopting the format "ZK + year + serial number", and stratigraphic names referring to the lithological classification of "China Coal Geology") to ensure data consistency.

[0028] Distributed secure storage: The storage architecture is built on the Hadoop Distributed File System (HDFS), with a storage capacity of ≥10TB, supporting 1000+ concurrent requests per second, and cross-node data synchronization latency of <100ms; blockchain encryption technology (SHA-256 algorithm) is used to store key data such as fault location and aquifer thickness in an immutable manner, and access permissions are controlled by a three-level system of "administrator-engineer-operator", with data call response time ≤1 second.

[0029] Step 2: Intelligent 3D Modeling (Core Innovation) This step utilizes modular algorithm design to achieve an "automated, easy-to-operate, and high-precision" modeling process, specifically comprising three main modeling modules: Module 2.1: Formation Modeling Based on Borehole Data Automatic data parsing and preprocessing: The system automatically reads borehole data (supports importing Excel and CSV formats), removes outliers (such as borehole stratification data that deviates from the stratigraphic trend) through data cleaning algorithms, and generates stratigraphic profiles at any location based on Kriging interpolation and geological knowledge rules (such as stratigraphic attitude constraints: dip angle ≤ 45°, strike continuity). The accuracy rate of identifying complex structures (faults, collapse columns) is ≥ 90%.

[0030] Layered modeling and topology construction: Based on borehole layered data, a stratigraphic model is automatically constructed according to the sequence of "surface - Quaternary - coal-bearing strata - aquifer". First, extract the coordinates of the borehole intersections of each layer to form a layer boundary point set; The Delaunay triangulation algorithm (following the "empty circumcircle" and "minimum angle maximum" criteria) is used to transform the point set into an irregular triangular network (TIN). The minimum interior angle of the triangular network is ≥30° to avoid excessive distortion. The spatial relationships between adjacent strata are automatically established using topological relation algorithms (such as the spacing constraints between coal seams and aquifers in the roof), with model accuracy down to the centimeter level.

[0031] The simplified design allows users to complete modeling in just 3 steps: ① Select the mining area and target strata; ② Upload the borehole data file; ③ Click the "Generate Model" button, and the system will automatically complete data parsing, interpolation, and triangulation without the need for manual parameter setting, reducing the modeling cycle to within 30 hours (compared to 45 days using traditional methods).

[0032] Stratigraphic models reflect the spatial distribution and inter-strata relationships of different strata.

[0033] Module 2.2: Co-modeling of contour lines and faults Contour data modeling: Supports importing contour coordinate point sets from Excel templates (templates include "X / Y / Z layer identifier" fields), and the system will automatically perform the following operations: Point clustering: Group contour points according to Z-value (elevation) to identify contour clusters of the same layer; Triangulation optimization: Delaunay triangulation is constructed using the point-by-point insertion method. First, a super triangle is created that encloses all points. Then, contour points are inserted one by one. The diagonals are swapped through the Local Optimization (LOP) algorithm to ensure that the triangulation meets the "empty circumcircle" criterion. Stratigraphic splicing: The triangular meshes of different contour line clusters are spliced ​​together in stratigraphic order to form a continuous three-dimensional stratigraphic surface model.

[0034] Fault dynamic modeling addresses the disruption of stratigraphic continuity caused by faults by designing a simplified fault modeling process: Users input key fault parameters (dip angle, strike, elevation difference, and edge line coordinates). The algorithm automatically calculates the planar coordinates of the hanging wall and footwall of the fault: Based on the fault dip angle and displacement, it derives the offset distance of the edge points of the hanging wall (X-axis offset = displacement × cosθ, Y-axis offset = displacement × sinθ, where θ is the fault strike), generating the coordinates of the footwall boundary. The system constructs the three-dimensional geometric surface of the fault based on parameters such as dip angle, strike, and displacement (e.g., fitting the fault plane or surface by the footwall boundary points and the edge points of the hanging wall), while supplementing the fault thickness parameters (if it is a fracture zone fault, the fracture zone thickness needs to be input), forming a basic geometric model of the fault. The constructed basic geometric model of the fault is then interactively processed with an existing three-dimensional stratigraphic surface model. A topological algorithm is used to cut the triangular mesh of strata interrupted by the fault, reconstructing the continuity of the strata on both sides of the fault (e.g., the shape of the hanging wall strata being uplifted by the fault and the footwall strata being subsided by the fault), ensuring that the spatial logic of the fault and the surrounding strata is consistent (e.g., after the fault cuts through a coal seam, the elevation difference between the hanging wall and footwall of the coal seam conforms to the displacement parameters), thus obtaining the fault model.

[0035] Fault models reflect the spatial morphology of faults and their cutting effect on strata.

[0036] Fault fusion: The fault model is topologically fused with the stratigraphic model, automatically cutting the triangular mesh that is cut off by the fault, reconstructing the stratigraphic morphology near the fault, and ensuring the spatial consistency between the structure and the strata.

[0037] Module 2.3: Dynamic Model Updates When the working face advances or new borehole data (such as the ZK102 borehole that exposes the water-conducting fracture zone) is input, the system automatically triggers the dynamic update engine: Impact area location: Using the R-tree spatial indexing algorithm, locate the triangular network area within a radius of 50m centered on the new data point; Local triangulation reconstruction: Delete the old triangulation within the affected area, and re-perform Delaunay subdivision based on the new data points and the surrounding original data (boreholes, contour lines); Interpolation correction: The elevation of the vertices of the triangulation network for non-original data points is corrected using a surface spline function. The function expression is as follows: (1) Undetermined coefficients (2) In the formula: x represents the plane X-axis coordinate of the elevation point to be determined, in meters (m); x i y represents the plane X-axis coordinate of the i-th original data point (such as a borehole point or a known contour line point), in meters (m), i = 1, 2, n (n is the number of original data points); y represents the plane Y-axis coordinate of the point whose elevation is to be determined, in meters (m); ir represents the Y-axis coordinate of the i-th original data point, in meters (m), i = 1, 2, n; i F represents the Euclidean distance on the plane between the point whose elevation is to be determined and the i-th original data point, in meters (m), (i = 1,2,n); i The coefficients to be determined for the i-th original data point are used to characterize the influence weight of the original data point on the interpolation surface (i = 1,2,n); W(x,y) represents the elevation value of the point to be determined by the surface spline function, in meters (m).

[0038] This is an empirical parameter used to adjust the curvature of a surface; it is selected appropriately based on the actual situation. When the surface curvature changes significantly, To obtain a smaller value, take a larger value; conversely, to obtain a larger value, take a smaller value. Generally speaking, For surfaces with singularities better.

[0039] Undetermined coefficients It can be obtained through the following system of equations: (3) In equation (3), . It is the elastic coefficient at point j. In the interpolation of general geological surfaces All values ​​are set to zero, ensuring that the calculated surface spline function matches the original data at the known points, meaning the surface passes through the original data points. The matrix form of the given system of equations is: (4) In the formula: A represents the coefficient matrix, which is a (n + 3)-order symmetric matrix. The elements in the matrix are calculated from the planar coordinates and Euclidean distance of the original data points, and are used to construct a system of equations to solve for the undetermined coefficients; B represents the constant term matrix, which is an (n + 3)-dimensional column vector. The first n elements are the known elevation values ​​"w1, w2, ..., wn" of the original data points, and the last 3 elements are 0, which are used in conjunction with the coefficient matrix A to solve for the undetermined coefficients through the system of equations; X represents the undetermined coefficient matrix, which is an (n + 3)-dimensional column vector. It contains n coefficients F1, F2, ..., Fn... related to the original data points and 3 linear term coefficients a0, a1, a2. This matrix can be obtained by solving the system of equations AX = B, and then used to calculate the elevation and other information of the points to be determined; D represents empirical parameters, which are used to adjust the curvature of the surface. When the curvature of the stratum surface varies greatly (such as near a fault), a smaller value should be used; when the stratum is flat (such as a coal seam in a plain), a larger value should be used to make the surface smoother. The known elevation value of the nth original data point is obtained directly from borehole data or contour data, and the unit is meters (m). The undetermined coefficients corresponding to the nth original data point are used to characterize the influence weight of this point on the interpolation surface and are dynamically adjusted according to the distribution density and elevation of the original data points. The coefficients of the linear term are constant terms corresponding to the coordinates of the point to be determined. They are used to correct the overall trend of the surface and ensure that the interpolation results conform to the regional geological occurrence (such as the dip angle and strike of the strata). C represents the coefficient related to which point. ｊ = 16πD / k ｊ (Cn when j=n), where k ｊ C is the elastic coefficient at point j. In general geological surface interpolation, C is often set to zero so that the obtained surface spline function matches the original data at the known points, that is, the surface passes through the original data points.

[0040] This is a system of symmetric equations, and in general, all elements on the main diagonal are zero. To ensure the stability of the solution, the Householder transformation method for symmetric systems of equations can be used.

[0041] The trigger for new data points (working face advancement / new borehole data) is the change in objective geological conditions (working face advancement leads to stratum exposure, and new borehole construction obtains unknown geological data), mainly based on "full-dimensional geological data" (such as new borehole data including layer depth, lithology, and aquitard thickness, etc., and working face advancement data including roadway location and advancement distance, etc.), and regional dynamic updates (such as updating the triangulation network within a 50m radius based on new borehole data, and updating the model boundary of the entire mining area based on working face advancement), triggering "dynamic updates of the fused model" (re-executing Delaunay subdivision and surface interpolation to update the model geometry).

[0042] Step 3: Planar and sectional cross-sectional correction Dilatation algorithm search and interpolation: Centered on the model modification point (such as the fault location adjustment point P in the profile), the dilatation algorithm is used to search for adjacent triangles layer by layer (expanding to a maximum of 5 layers to avoid cross-fault data interference) to obtain the original data points (borehole coordinates, water pressure values, etc.) within the search range; for the vertices of the triangular mesh that are not original data points, spline surface interpolation is used to calculate the new elevation, and the correction error of the plan profile data is ≤3%.

[0043] Model modification points are triggered by manual, proactive correction of model errors (such as discovering that the location of a fault in a profile does not match the actual geological survey). The primary method is "spatial coordinates + modification command" (such as adjusting only the X / Y / Z coordinates of a fault point without adding any new geological parameters). Localized, precise corrections are made (such as correcting only the fault line of a profile or the contour line of a certain stratum, with an impact range typically of 10-30m). This triggers "plan view and profile linkage correction" (automatically synchronizing plan view and profile data after modification to ensure consistency).

[0044] Two-way linkage correction logic Profile plane modification: When the positions of faults and coal seam lines on the profile are modified, the system automatically extracts the spatial coordinates of the modified points and feeds them back to the corresponding positions on the plane map to update the fault traces and coal seam contour lines. Planar profile correction: When key points (such as the elevation of the coal seam floor) at the profile line in the planar view are adjusted, they are automatically mapped to the corresponding profile view, correcting the stratigraphic interfaces and structural lines on the profile, so as to achieve the consistency of the planar profile by "affecting the whole body by changing one part".

[0045] User-friendly design: Users can make corrections through a visual interface: ① Click on the geological line to be modified in the plan / section view; ② Drag the control point to adjust its position; ③ The system automatically completes the linkage update and interpolation calculation, without the need to manually input coordinates or formulas, and the correction process takes less than 2 minutes.

[0046] Step 4: Mixed Reality Decision Interaction 3D Model Visualization and Manipulation: A WebGL 3D rendering engine built on CesiumJS and Three.js supports the following simplified operations: Model cutting: By selecting with a mouse box or clicking, horizontal / vertical / arbitrary polygon cutting can be achieved, and information such as stratigraphic lithology and aquitard thickness of the cutting surface can be displayed in real time; Stratigraphic control: Use the "Hide / Highlight" button to control a specific stratum individually (e.g., hide the Quaternary loose strata and focus on the coal-bearing strata). Information Query: Click on the borehole model to bring up a columnar view (including layer thickness and lithology), double-click on the fault area to display the occurrence parameters and water conductivity level.

[0047] Real-time calculation of water parameters for flood control, integrated with an automated calculation module. After the user selects the target area (polygon with ≥3 vertices), the system automatically executes the calculation. Calculation of water inrush coefficient: Use the formula T=P / M (P is water pressure, M is the thickness of the waterproof layer), and display the calculation results as a heat map (red ≥0.6MPa / m is a high-risk area), with a response time ≤0.8 seconds; Calculation of safe waterproof layer thickness: Input parameters such as the width of the roadway floor and the unit weight of the waterproof layer, and the safe thickness value will be automatically output and marked at the corresponding position in the model.

[0048] AR on-site decision support: Deploy AR terminals (such as AR glasses and mobile tablets) to overlay 3D models with the actual scene.

[0049] Real-time overlay of flood risk warning information (e.g., displaying a red warning box in high-risk areas); Supports visual simulation of prevention and control measures (such as simulating the trend of water pressure change after grouting and water plugging) to assist on-site technicians in quickly developing measures.

[0050] Furthermore, although the hydrological monitoring data and engineering data of this invention do not directly participate in the initial construction of the stratigraphic model and fault model in the three-dimensional dynamic modeling of coal mine water control, they play a key role in subsequent model application, dynamic optimization, and decision support, as detailed below: The role of hydrological monitoring data: Model accuracy verification: Real-time water pressure and inflow data can be compared with parameters such as aquifer distribution and impermeable layer thickness in the model to verify the accuracy of the model's simulation of hydrogeological conditions. For example, if the model predicts a large deviation between the aquifer water pressure in a certain area and the actual monitoring value, the permeability parameters or thickness data of the aquifer in the stratigraphic model can be corrected in reverse.

[0051] Dynamic assessment of water hazard risk: By combining historical water hazard records with real-time monitoring of soil moisture and water inflow changes, a new dimension of "water hazard risk factor" is added to the fusion model. As the work area advances, the risk level distribution in the model can be updated in real time based on monitoring data. For example, areas with a sudden increase in water inflow can be automatically marked as high-risk, assisting in dynamic early warning.

[0052] Groundwater flow simulation support: When constructing a coal mine hydrological model, hydrological monitoring data (such as changes in groundwater level and water pressure) are the core input data for simulating groundwater flow direction, velocity and water quality changes. They can be coupled into the fusion model through numerical simulation algorithms (such as MODFLOW) to predict the path of water hazard diffusion.

[0053] The role of engineering data Boundary constraints for dynamic model updates: The centerline coordinates and cross-sectional morphology parameters of the tunnel can serve as a "spatial reference" for the advancement of the working face. When the working face advances, the system can determine the scope of model updates based on the tunnel engineering data (such as taking the 50m perimeter of the tunnel as the core update area), thus avoiding meaningless global model reconstruction.

[0054] Verification of the adaptability of water control projects: Horizontal drilling trajectory data can be superimposed into the fusion model to simulate the spatial relationship between the borehole and the fault and aquifer, and to determine whether the borehole can effectively hit the water-conducting channel. This provides a visual verification for water control projects (such as the design of grouting and water-blocking boreholes) and reduces the blindness of the project.

[0055] Safety production scenario association: By combining tunnel engineering data with water hazard risk areas in the model, a "tunnel-risk area" association map can be generated, which intuitively shows the distance between the tunnel and high-risk aquifers and faults during the tunnel advancement process, providing a basis for underground safety production path planning.

[0056] System implementation of the present invention: (1) Hardware environment Server: CPU is Intel Xeon Gold 6330 (≥2 cores), memory is ≥128GB, hard drive is ≥2TB SSD; Client: Ordinary office computer (CPU i5 or above, memory ≥8GB), AR terminal supports Android 10.0 or above or iOS 14.0 or above.

[0057] (2) Software module design Data management module: includes sub-modules such as mining area management, borehole management, layer management, and contour line import, and supports batch data upload, real-time preview and export; Intelligent modeling module: integrates borehole modeling, contour line modeling, fault modeling, and model update sub-modules, providing one-click modeling functionality; Planar profile correction module: realizes dilation algorithm search, two-way linkage correction, and correction result verification; Visualized decision-making module: includes model segmentation, parameter calculation, and AR interaction sub-modules, and supports model publishing and sharing.

[0058] (3) Simplified operation process design New user guide: The system has a built-in operation tutorial (including animated demonstrations) to guide users through the entire process of "data import - modeling - correction - decision making"; Templated input: Provides Excel templates for borehole data and contour data (including hints for required fields) to avoid data format errors; Automated output: After modeling is completed, a "3D Geological Model Report" (including model accuracy assessment and structural identification results) is automatically generated without manual writing.

[0059] The modeling cycle has been reduced from 45 days to 30 hours to meet the needs of rapid advancement of the working face; the accuracy rate of complex structure identification is ≥90%, the accuracy of water inrush risk area delineation is ≥90%, and the consistency error of plan and profile data is ≤3%; the AR terminal supports real-time data overlay and scheme simulation, the response time for calculating the thickness of the aquitard is ≤0.8 seconds, and the missed detection rate of manual analysis is reduced to <5%; it is adaptable to different geological conditions of coalfields such as North China type, South China type, and Northwest type, and has been verified in projects such as Jineng Holding Zhaogu No. 2 Mine, and can be promoted to 14 large coal bases across the country.

[0060] Example 1: Taking a North China-type coalfield as an example, the method of this invention is applied for three-dimensional modeling: Data acquisition: 28 sets of borehole data (including ZK101-ZK128), coal seam floor contour data (10,000+ coordinate points), and 3 fault data (F1-F3, dip angle 35°-50°). Modeling process: After uploading the data, click "One-click modeling". The system will complete the model construction within 30 hours and identify the water-conducting fracture zone near the F2 fault. Dynamic update: After the addition of borehole ZK129 (which penetrates the Ordovician limestone aquifer), the system completes a local model update within 30 minutes, correcting the elevation of the top interface of the aquifer. Decision-making application: By viewing the risk of water inrush along the working face advancement route through the AR terminal, the thickness of the safe water-proof layer was calculated to be 58.82m, which guided the formulation of grouting reinforcement measures and effectively avoided the risk of water damage.

[0061] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules, units, or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units, modules, or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0062] The units may or may not be physically separate. The components shown as units can be one or more physical units, meaning they can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0064] In particular, according to embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this invention. It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.

[0065] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0066] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion, characterized by, The method comprises the following steps: Obtaining coal mine water prevention and control data; the coal mine water prevention and control data comprises drilling data, contour line data and geological structure data; According to the drilling data, a stratum model is constructed; According to the contour line data and the geological structure data, a fault model is constructed; The fault model and the stratum model are topologically fused to obtain a fused model; When a new data point is obtained, the fused model is dynamically updated; The fused model is corrected in linkage.

2. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 1, characterized in that, According to the drilling data, a stratum model is constructed, specifically comprising the following steps: The drilling data is preprocessed to obtain drilling stratification data; According to the drilling stratification data, a stratum model is constructed.

3. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 2, characterized in that, The drilling data is preprocessed to obtain drilling stratification data, specifically comprising the following steps: By means of a data cleaning algorithm, abnormal values in the drilling data are removed, and based on Kriging interpolation method and geological knowledge rules, a stratum profile is generated to obtain the drilling stratification data.

4. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 3, characterized in that, According to the drilling stratification data, a stratum model is constructed, specifically comprising the following steps: A point set of the drilling stratification data is converted into an irregular triangle network by means of a Delaunay triangulation algorithm; By means of a topological relationship algorithm, the spatial correlation of adjacent strata is established to obtain a stratum model.

5. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 4, characterized in that, According to the contour line data and the geological structure data, a fault model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed; According to the geological structure data and the three-dimensional stratum surface model, a fault model is constructed.

6. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 5, characterized in that, According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps:

7. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 6, characterized in that, According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps:

8. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 7, characterized in that, According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: （1） Undetermined coefficients （2） In the formula: is an empirical parameter for adjusting the curvature of the curved surface; x represents the plane X-axis coordinate of the elevation point to be solved; x i represents the plane X-axis coordinate of the i-th original data point; y represents the plane Y-axis coordinate of the elevation point to be solved; y i represents the plane Y-axis coordinate of the i-th original data point; r i represents the Euclidean distance of the i-th original data point on the plane; F i represents the undetermined coefficient corresponding to the i-th original data point, used to represent the influence weight of the original data point on the interpolation curved surface; W(x, y) represents the elevation value of the elevation point to be solved calculated by the curved surface spline function; pending coefficients are found by solving the following system of equations: （3） wherein ; is the elastic coefficient with respect to the point j, ; the matrix form of the system of equations given is: （4） where A represents a coefficient matrix; B represents a constant term matrix; X represents a matrix of undetermined coefficients; and D represents an empirical parameter. represents the known elevation value of the nth original data point; represents the undetermined coefficient corresponding to the nth original data point; represents the linear term coefficient; represents the coefficient related to the jth point, C ｊ = 16πD / k ｊ (j = Cn when n), where k ｊ is the elastic coefficient about the jth point, and in general geological surface interpolation C is often taken as zero, so that the surface spline function obtained coincides with the original data at the known points, i.e. the surface passes through the original data points.

9. The coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion according to claim 8, characterized in that, According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface model is constructed, specifically comprising the following steps: According to the contour line data, a three-dimensional stratum surface With the model modification point as the center, the inflation algorithm is used to search the adjacent triangles layer by layer outward to obtain the original data points in the search range; for the triangle net vertexes of non-original data points, the spline surface interpolation is used to calculate the new elevation; When the position of the fault or coal seam line on the profile is modified, the spatial coordinates of the modification point are extracted and fed back to the corresponding position of the plan, and the fault trace and the coal seam contour are updated; When the key point of the profile line in the plan is modified, it is mapped to the corresponding profile, and the stratigraphic interface and structural line on the profile are corrected.

10. A coal mine water prevention and control three-dimensional dynamic modeling system based on multi-source data fusion, used to realize the coal mine water prevention and control three-dimensional dynamic modeling method based on multi-source data fusion as claimed in any one of claims 1-9, characterized in that, Comprise: An acquisition module is configured to acquire coal mine water prevention and control data, wherein the coal mine water prevention and control data comprises drilling data, contour data, hydrological monitoring data, geological structure data and engineering data; A stratigraphic model construction module is configured to construct a stratigraphic model according to the drilling data; A fault model construction module is configured to construct a fault model according to the contour data and the geological structure data; A fusion module is configured to topologically fuse the fault model and the stratigraphic model to obtain a fusion model; An update module is configured to dynamically update the fusion model when a new data point is acquired; A linkage correction module is configured to correct the fusion model in linkage.

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