A GIS-based risk management system and method for pollutant migration in mining watersheds

Abstract

This invention discloses a GIS-based risk management system and method for pollutant migration in mining area watersheds, relating to the technical field of simulating pollutant migration patterns. It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. Based on watershed hydrology theory and pollutant migration and diffusion mechanisms, it fully considers the migration of pollutants in the storage area after rainfall via surface microrunoff. By studying the formation patterns of microrunoff, it simulates the watershed migration process of pollutants in the mining area under different rainfall intensities, thereby more accurately calculating pollutant migration and diffusion fluxes and managing the risk of pollutant migration in the watershed. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, achieving accurate pollutant identification and source tracing. It can simulate pollution control measures in mining area watersheds, predict development trends, and provide early warnings for pollution events.

Description

Technical Field

[0001] This invention relates to a GIS risk management system and method for pollutant migration in mining areas and watersheds, particularly applicable to coal mining and processing industrial areas, organically contaminated sites, and solid waste storage sites, and relates to the technical field of simulating pollutant migration patterns. Background Technology

[0002] Large-scale coal mining primarily employs underground and open-pit mining methods. Underground mining leads to extensive land subsidence, while open-pit mining requires the removal of sparse vegetation and topsoil, severely impacting the fragile mining area's ecosystem. The waste slag, including gravel, gangue, and sand, generated during mining accumulates into dumps and waste rock piles. These not only completely destroy the original vegetation environment and occupy vast amounts of land, but also contain harmful elements and organic pollutants such as As, Mo, Hg, Pb, Cr, Ni, Ba, Sb, CO2, SO2, and NOx, posing a potential source of pollution. Rainfall runoff is a major driver of the migration and diffusion of pollutants from these piles. Rainwater soaking and scouring the piles, along with leachate, carries these pollutants into the surrounding land, depositing them in the soil or entering downstream water bodies, polluting surrounding soils and water sources, leading to the phenomenon of "small pollution from light rain, heavy pollution from heavy rain." Since surface microrunoffs only occur briefly with rainfall and their flow paths change after each occurrence, there are currently no technical solutions that fully consider the spread of pollution carried by surface microrunoffs, nor are there any methods to simulate the spread of microrunoffs generated after rainfall in storage yards. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a GIS-based risk management system and method for watershed pollutant migration in mining areas. This system is based on watershed hydrology theory and pollutant migration and diffusion mechanisms, fully considers the migration of pollutants in the storage area with surface microrunoff after rainfall, and simulates the watershed migration process of pollutants in the mining area under different rainfall intensities by studying the formation law of microrunoff. This allows for more accurate calculation of pollutant migration and diffusion fluxes and the management of watershed pollutant migration risks.

[0004] To achieve the aforementioned technical objectives, a GIS risk management system for pollutant migration in mining watersheds is provided, comprising a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. The 3D GIS functional module includes a measurement unit, a micro-runoff analysis unit, and a 3D pollution model unit. The hydrodynamic-water quality model coupling module includes a real-scene 3D model unit, a field investigation and sampling unit, a data preprocessing unit, a water quality model construction unit, a hydrodynamic model construction unit, and a hydrodynamic-water quality model coupling unit. The mining watershed pollution risk management module includes a pollutant migration risk assessment and early warning unit, a pollutant migration dynamic visualization unit, and a risk control strategy unit.

[0005] Measurement Unit: Includes spatial distance measurement, spatial area measurement, and triangulation. Spatial distance measurement: Used by UAV to calculate the straight-line distance between the coordinates of two selected points; Spatial area measurement: Measure the area of ​​the drawn plane; Triangulation: Calculate the straight-line distance, perpendicular distance, and planar distance between two selected points.

[0006] Microrunoff Analysis Unit: Using UAV aerial surveying technology, three-dimensional oblique and orthophoto data of the study area with a high resolution of 1.5cm were acquired and generated. Then, a three-dimensional oblique model and an orthophoto model were generated based on the three-dimensional oblique and orthophoto data. Based on the ray projection algorithm, a corresponding number of rays were projected from the Z-axis coordinate system at a set density of 0.1 rays / m² to 100 rays / m², perpendicular to the XY-axis plane. Then, the intersection point of each ray with the three-dimensional oblique model was taken to obtain the corresponding elevation data. Finally, the GSFLOW software was used to extract the microrunoff of the mine in the watershed with high precision.

[0007] 3D pollution model unit: used to perform 3D rendering of pollution migration data and visualize it, making hidden pollution easier to observe;

[0008] Real-world 3D model unit: Real-world information is collected simultaneously from vertical and four oblique angles by five camera lenses of the drone. POS data and pixel data are obtained by combining oblique photogrammetry automatic modeling technology with GIS platform application technology. The data is then imported into processing software to obtain a high-precision real-world 3D model, digital orthophoto, and surface model.

[0009] On-site survey and sampling unit: used to set the coordinates and navigation of UAV flight sampling points and to arrange sampling tasks;

[0010] The data preprocessing unit is used to process the collected UAV sampling data according to a standard format, including editing the sampling number, latitude and longitude coordinates, detection concentration and sampling depth;

[0011] Water quality model building unit: The migration of pollutants in water bodies, the pollutants formed by the leaching of pollutant solutions from the storage yard under the action of rainfall, are added to the model in the form of rainfall based on the given water quality concentration of rainfall, so as to match the actual pollution situation as closely as possible;

[0012] Hydrodynamic model building unit: The terrain is meshed, and parameters such as roughness, boundary, rainfall, evaporation, and wind speed of the object are input. Measured flow field data such as water level, flow rate, and flow direction are substituted into the model.

[0013] Hydrodynamic and water quality model coupling unit: used to establish a hydrodynamic model and couple a solute migration model. The established model is then used to perform interpolation calculations and predictive simulations of pollutant migration in surface water, and to analyze the range and concentration distribution of pollution plumes.

[0014] Pollutant Migration Risk Assessment and Early Warning Unit: Used to evaluate and classify the water quality of the basin, and to determine the surface water pollution early warning level as 5 levels, namely no warning - minor warning - moderate warning - severe warning - major warning; no warning means that the surface water faces a very small risk of pollution and no warning needs to be issued; minor warning, moderate warning, severe warning and major warning mean that the surface water faces different degrees of pollution and an early warning needs to be issued.

[0015] Risk control strategy unit: It assesses the impact of pollutants on the watershed by calculating the pollutant migration and diffusion flux, exports the calculation results, and uses the 3D GIS function module to dynamically visualize the pollutant migration process. Based on the surface water environmental quality standards, it guides decision-makers to provide corresponding risk control countermeasures and solutions.

[0016] A method for GIS-based risk management and control of pollutant migration in a mining area watershed, comprising the following steps:

[0017] The drones acquire real-world information about the mining watershed, and then use this information to build a high-precision 3D model of the watershed. The drones also collect image data of pollutant migration in the mining area from vertical and four tilt angles, thereby converting the image data into a high-precision 3D model, including digital orthophotos and surface models.

[0018] After generating a high-precision real-scene 3D data model, topographic mapping software is used to draw contour lines and extract elevation points for the entire mining area of ​​the mining basin, thereby collecting a 2D contour map of the entire mining area, so as to analyze the on-site conditions of the area in a more intuitive way by combining 2D and 3D.

[0019] High-resolution 3D oblique and orthophoto images of the study area were acquired and generated using UAV aerial surveying technology. This allowed for the differentiation between vegetation-covered and vegetation-free areas of the mining area. After rainfall, ray casting algorithms were used to obtain surface microrunoff generated in the vegetation-free areas. Since vegetation has good water retention and its transpiration consumes some groundwater, only the movement of pollutants after infiltration into the ground with groundwater and with surface microrunoff needs to be considered. Pollutant movement with groundwater was detected using monitoring equipment. However, surface microrunoff is not only small but also gradually decreases with rainfall and disappears after the rainfall ends. Therefore, a combination of CIS surface analysis, hydrological analysis, and mathematical statistics methods is needed. The development characteristics of surface microrunoff in the micro-topography are analyzed through visualization of slope, aspect, and elevation profiles of the mining area. This reveals the short-term development pattern of surface microrunoff accompanying rainfall. Combining this with groundwater and the short-term surface microrunoff that occurs with rainfall, pollution migration paths along water flow are generated in a realistic 3D model of the mining area's watershed.

[0020] Based on a realistic 3D model of pollutant migration in a mining area watershed, and combined with surface micro-runoff development characteristic data, a simulation of pollutant migration in the mining area watershed is conducted. A mathematical model of pollutant migration in the mining area watershed is established, including a hydrodynamic model and a water quality model. First, the terrain is gridded, and the roughness, boundary, rainfall, evaporation, and wind speed parameters of the object are input. The flow field data of the measured surface micro-runoff are used to construct a hydrodynamic model. Then, based on the hydrodynamic model, a water quality model of the surface micro-runoff is constructed. The pollutant concentration is used as the initial condition for water quality, and the pollution input is the pollutant solution leached from the stockpile under the action of rainfall. The amount of pollution input is given in the form of rainfall.

[0021] The overall rainfall and rainfall infiltration in the mining area were calculated by measuring the spatial area. The instantaneous flow rate of surface microrunoff and the migration and diffusion flux of pollutants in the storage yard with microrunoff were simulated and calculated by combining hydrodynamic model and water quality model.

[0022] The calculated flux of pollutants from the stockpile migrating and diffusing with microrunoff is used to conduct pollution risk early warning and control assessment of the mining watershed. The single-factor evaluation method recommended in the "Surface Water Environmental Quality Standard" (GB3838-2002) is adopted to evaluate and classify the watershed water quality. The comprehensive water quality category of the entire evaluation area is determined based on the category of the worst single water quality indicator in the evaluation classification, thus directly reflecting the water quality status to meet water quality protection requirements. Then, the comprehensive index evaluation method is used to compare runoff sections with the same water quality category to identify the main pollutants and comprehensively reflect the pollution status of surface microrunoff water. The relative pollution indices of each pollution indicator are statistically analyzed to calculate the pollution index of the pollutants. Based on the pollution index, the degree of pollution and the main pollutants of the water body are determined. Finally, a warning is issued based on a pre-set warning level.

[0023] The impact of pollutants on the watershed is assessed by measuring the pollutant migration and diffusion flux. At the same time, the pollutant migration and diffusion flux values ​​are combined with the three-dimensional GIS function module to dynamically visualize the pollutant migration process. Based on the surface micro-runway environmental quality standards, the system guides decision-makers to give corresponding risk management countermeasures and solutions.

[0024] Furthermore, the process of obtaining surface microrunoff using the ray projection algorithm is as follows: Based on the three-dimensional oblique and orthophoto image data, a three-dimensional oblique model and an orthophoto model are generated. According to the set density of 0.1 rays / m² to 100 rays / m², a corresponding number of rays are projected from the Z-axis coordinate system at high altitude, perpendicular to the XY-axis plane. Then, the intersection point of each ray with the three-dimensional oblique model is taken to obtain the corresponding elevation data. The development characteristics of the watershed mine microrunoff are extracted and restored from the elevation data using GSFLOW software to improve the water flow data of surface microrunoff.

[0025] Furthermore, by using image recognition algorithms, areas with lush vegetation in the mining area image are identified as impermeable areas, and areas without vegetation are identified as permeable areas. By selecting normal and extreme rainfall meteorological conditions, the surface runoff and water infiltration information of permeable areas can be calculated using the water temperature model GSFLOW and the infiltration runoff mechanism. The water storage, evaporation and infiltration runoff data of impermeable areas can be calculated. Then, the actual rainwater infiltration amount of the storage yard can be calculated based on the water balance.

[0026] Furthermore, the specific method for constructing the hydrodynamic model is as follows:

[0027] a1. Mesh generation for the hydrodynamic model: High-precision topographic data of the study area was obtained through UAV oblique photogrammetry technology and converted into a format suitable for MIKE software. The computational grid of the study area was generated using the MIKE software mesh generator. Taking into account the requirements of model simulation accuracy, software computation time and result accuracy, the model adopted a triangular mesh. Considering the topographic relief, the mesh was further densified locally to ensure higher computational efficiency.

[0028] a2. Set the initial conditions for the hydrodynamic model: The initial conditions include the initial water level and the initial flow velocity. In order to avoid instability in the calculation, the initial conditions should be set as consistent as possible with the actual data at the initial moment in the model simulation cycle.

[0029] a3. Set boundary conditions for the hydrodynamic model: Set the upstream boundary of the runoff path for pollutant migration as the inlet and the downstream boundary as the outlet. Set the flow rate value at the upstream boundary and the inlet of each tributary in the hydrodynamic model, and set the water level value at the downstream boundary.

[0030] a4. Setting hydrodynamic parameters: In the hydrodynamic calculation process, the riverbed roughness of micro-runoff and the start time of the hydrodynamic model calculation are considered. The riverbed roughness is used as a comprehensive factor to reflect the resistance during the water flow process. Based on UAV aerial survey technology, 1.5cm high-precision resolution three-dimensional oblique and orthophoto image data of the study area are obtained and generated. The riverbed roughness is defined according to the topography, geomorphology and surface features. The other dry and wet boundary model parameters, eddy viscosity model parameters and bed friction force model parameters adopt the default values.

[0031] a5. Verification of the hydrodynamic model: Calibrate the model parameters based on measured data, use the boundary positions as verification points, output the simulated water level and flow rate results, then select the measured water level and flow rate data within the study period, plot the comparison chart of the simulated water level and flow rate results and the actual results, and compare them. If the simulated values ​​are basically within the range of the measured values, with no significant differences, a high degree of trend agreement, and relatively small errors, it proves that the hydrodynamic model has good accuracy and can be used for hydrodynamic simulation research.

[0032] Further, the applicability assessment of the water quality model:

[0033] The similarity between simulated and measured water quality data is quantitatively described using the skill score (SI) and root mean square error (RMSE). The SI value ranges from 0 to 1, representing "poor simulation results" to "perfectly matching simulation results," respectively. The formula is as follows:

[0034]

[0035]

[0036] In the formula, n represents the number of water quality monitoring points deployed near the upstream inlet and downstream outlet of the watershed; Q i and S i These are the measured water quality values ​​and the simulated water quality values, respectively.

[0037] Furthermore, the watershed water quality is evaluated and classified using a single-factor evaluation method. The comprehensive water quality category of the water area is determined by selecting the category of the worst-performing single indicator from all participating water quality indicators. The calculation formula is as follows:

[0038] G = maxG i

[0039]

[0040] In the formula: G i For the water quality category of the i-th pollutant, C i Let C be the concentration of the i-th pollutant. s The evaluation criteria for the i-th pollutant.

[0041] Furthermore, the comprehensive index evaluation method adopted for micro-runoff in the watershed first statistically analyzes the relative pollution indices of each pollution indicator, calculates the pollution index of each pollutant, and then determines the degree of pollution and the main pollutants based on the pollution index. The calculation formula for the comprehensive pollution index evaluation method is as follows:

[0042]

[0043]

[0044] In the formula: P i Let n be the pollution index of the i-th pollutant; n is the number of water quality indicators being evaluated.

[0045] The evaluation and grading method is as follows:

[0046]

[0047] If the early warning indicator only needs to provide early warning for a single factor water quality indicator, the concept of water quality change rate is introduced, and its calculation formula is as follows:

[0048]

[0049] In the formula: S i This represents the simulated water quality value; Q i t represents the measured water quality value; t represents time.

[0050] By analyzing and comparing the existing water quality and the predicted rate of change Q, values ​​are assigned to the surface water quality and its dynamics. The assignments are referenced in the table below:

[0051] Surface water quality and dynamic values ​​in mining watersheds (single factor)

[0052]

[0053] Furthermore, the method for calculating the pollutant migration and diffusion flux is as follows:

[0054]

[0055] In the formula, Q(t) is the instantaneous flow rate, m 3 / s; C(t) is the instantaneous concentration, mg / L.

[0056] Beneficial effects:

[0057] This method uses close-up photography technology to model with an accuracy of 1.5cm, providing a model foundation for subsequent accurate measurement and prediction, thereby realizing refined modeling of the watershed in the mining area, establishing high-precision terrain, realistically depicting spatial details, and making surface microrunoff analysis more accurate based on the real-scene 3D model with an accuracy of 1.5cm.

[0058] Accurate analysis of rainfall infiltration and surface runoff, based on watershed hydrology theory and pollutant migration and diffusion mechanisms, fully considers the migration of pollutants in the storage area with surface micro-runoff after rainfall, and simulates the watershed migration process of pollutants in the mining area under different rainfall intensities by studying the formation law of micro-runoff, thereby more accurately calculating the pollutant migration and diffusion flux.

[0059] It enables pollution simulation, prediction, and early warning. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, enabling accurate identification and source tracing of pollutants. It can simulate pollution control measures in mining areas and watersheds, predict development trends, and provide early warnings for pollution events. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of a GIS risk management system for the migration of pollutants in a mining area watershed according to the present invention. Detailed Implementation

[0061] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0062] Example 1

[0063] Taking mining area watersheds as the research object, this paper designs and implements relevant functions that meet the needs of mining area watersheds. Based on WebGL technology, a 3D client development platform is developed. Based on Cesium optimization and B / S architecture design, a lightweight and high-performance GIS development platform for mining area watersheds is developed. It can run efficiently in the browser without installation or plugins, and can quickly access and use various GIS data and 3D models to present 3D spatial visualization. The paper also develops a GIS risk management system for mining area watershed pollutant migration. Based on watershed hydrology theory and pollutant migration and diffusion mechanism, it constructs a watershed hydrodynamic-water quality coupling model to simulate the migration process of pollutants in the mining area watershed under different rainfall intensities, and calculates pollutant migration and diffusion fluxes, as well as visualization, analysis and data management functions, to control the risk of pollutant migration in the watershed.

[0064] WebGL (Web Graphics Library) is a 3D graphics protocol. This graphics technology standard allows JavaScript and OpenGL ES 2.0 to be combined. By adding a JavaScript binding to OpenGL ES 2.0, WebGL can provide hardware-accelerated 3D rendering for HTML5 Canvas. This allows web developers to use the system's graphics card to display 3D scenes and models more smoothly in the browser, and also to create complex navigation and data visualizations.

[0065] Cesium provides a high-efficiency data visualization platform for 3D GIS. Cesium is a cross-platform, cross-browser JavaScript library for displaying 3D globes and maps. It uses WebGL for hardware-accelerated graphics and does not require any plugins.

[0066] It supports streaming of various 3D data in 3D Tiles format, including oblique photogrammetry models, 3D buildings, CAD and BIM exterior and interior data, and point cloud data. It also supports style configuration and user interaction.

[0067] High-precision topographic data visualization is achieved based on orthophotos of the mining watershed, supporting topographic exaggeration effects and programmable contour and slope analysis effects.

[0068] It supports image layers from various resources, including WMS, TMS, WMTS, and time-series images. Images can be dynamically adjusted for transparency overlay, brightness, contrast, gamma, hue, and saturation. It also supports image roll-out contrast to display the migration of pollution in mining areas and watersheds at different stages.

[0069] It supports standard vector formats KML, GeoJSON, TopoJSON, and vector grounding effects.

[0070] CZML supports the display of dynamic time-series data. CZML is a JSON-formatted string used to describe time-related animated scenes. CZML contains points, lines, landmarks, models, and other graphical elements, and specifies how these elements change over time.

[0071] Terrain, model, and 3D tile model face clipping.

[0072] The GIS risk management system for pollutant migration in mining watersheds includes functions such as spatial distance measurement, spatial area measurement, slope triangulation, coordinate measurement, inundation analysis, micro-runoff analysis, attribute data volume drawing and rendering, and earthwork rendering.

[0073] UAV oblique photogrammetry modeling technology acquires a realistic 3D model using a UAV equipped with a Sel 202S five-lens camera. The five cameras simultaneously capture real-world information from vertical and four oblique angles. This product combines oblique photogrammetry automatic modeling technology with GIS platform application technology to obtain POS data and pixel data. The data is then imported into processing software to obtain a high-precision realistic 3D model, digital orthophoto, and surface model. During project implementation, the overall 3cm realistic model was insufficient for further decision-making analysis of local terrain. Therefore, a new photogrammetric technique, close-up photogrammetry, was added to the existing oblique photogrammetry. To conduct close-up photogrammetry, the UAV needs high-precision positioning and gimbal attitude control capabilities, and the photogrammetry software needs to support processing irregular flight path data. The close-up photogrammetry workflow is a process from scratch, from coarse to fine. Based on the original 3cm model, the target's positional and structural information is acquired. Then, based on the results of the 3cm model, the UAV's close-up photogrammetry flight path is planned, ultimately generating a 1.5cm refined 3D model. By using photographic equipment to closely observe the object's surface and acquire high-resolution (sub-centimeter) images, photogrammetric processing is performed to restore the object's precise coordinates and detailed shape structure, thus reconstructing a detailed 3D model. This surpasses the accuracy requirements of other photogrammetric methods, meeting the needs of this project for further analysis and decision-making regarding local areas. After generating the high-precision 3D real-scene data model, topographic mapping software is used to draw contour lines and extract elevation points across the entire mining area, resulting in a 2D contour map of the region. This allows for a more intuitive combination of 2D and 3D analysis of the area's site conditions. Since this project requires data to be published on a data platform for 3D data analysis and management, the generated high-precision DEM data and tilt model are sliced ​​using CesiumLab platform software. The high-precision topographic data is then published to the GIS data platform for further analysis and processing, replacing the original low-precision, older version of the topographic data.

[0074] Based on high-fidelity, full-view imagery, the ground resolution reaches a high precision of 1.5 cm. The GIS risk management system for the migration of pollutants in mining area watersheds can protect and archive spatial structure data of mining area watersheds, making it traceable data.

[0075] Based on a GIS system framework, this system integrates multi-source heterogeneous data such as UAV oblique photography, spatial triangulation, 3D real-scene modeling and vectorization, and BIM. It also integrates core technologies such as site virtual reality (VR) and pollutant 3D spatial distribution interpolation to create a 3D real-scene model of the mine. Through a 3D scene functional platform, it achieves high-resolution depiction of the mine, enabling 3D visualization and refined restoration construction. The real-scene 3D GIS functional module includes not only basic information such as dimensions, area, and geographic coordinates, but also high-fidelity 3D image models and BIM information models. This provides users with intuitive decision-making support, improves management efficiency, and offers advantages such as high color fidelity, good compatibility with high-fidelity maps, diverse data loading, multi-departmental sharing, and big data-based visualization analysis. This realizes a comprehensive, four-dimensional, all-encompassing digital platform for mines, integrating GIS, BIM, IoT, and AI.

[0076] Dual-screen comparison: In the 3D platform, different scenes are compared and analyzed for historical scene comparison and comparison between different solutions. In the scene editing window, click the dual-screen comparison settings button in the left toolbar, and then click "Add Secondary Screen Scene" in the dual-screen comparison tool window on the right. In the pop-up scene selection window, select the scene to be compared on both screens. After setting up, click the save scene button, and the dual-screen comparison tool will be visible in the scene preview window. Clicking the dual-screen comparison tool allows you to select the scene you just chose and perform linked or non-linked dual-screen comparisons.

[0077] Flight Path Settings: In the scene editing window, click "Flight Path Settings" in the left toolbar. In the flight path editing window that appears on the right, click "Add a Flight Path." A flight path consists of many window states; different window states plus their durations form a continuous flight path. To create a new flight path, click "Add Current Location as Viewpoint." This will record the current window state. If you need to modify the current viewpoint of the flight path, first click "Edit" to enter edit mode, then click the camera button to the right of "Viewpoint" to change the window state to the current window state. You can also modify the duration of the current window state; the default is 2 seconds.

[0078] Line-plane interpolation calculation: After drawing the ground-hugging polygon on the terrain, perform in-plane interpolation calculation according to the set interpolation number to form the effect of constructing TIN polygons and triangular meshes, and then return the value of each triangle, which can be used to calculate the surface area of ​​irregular piles;

[0079] Microrunoff analysis: UAV aerial survey technology was used to acquire and generate 3D oblique and orthophoto data of the study area with a high resolution of 1.5cm.

[0080] Model Volumetric Analysis: This function analyzes and calculates the total cut and fill volume within a selected area of ​​the model. Click the analysis module on the right side of the scene browsing window, select the Model Volumetric Analysis tool, and click "Create Analysis Range." Draw the area to be analyzed on the model. After drawing, you can set the reference height of the analysis surface and the analysis precision in the pop-up window. Click "Start Analysis" to obtain the cut or fill data.

[0081] Spatial distance measurement: Spatial distance measurement can calculate the straight-line distance between two selected points;

[0082] Spatial area measurement: It can measure the area of ​​a drawn plane.

[0083] Triangulation: This function can calculate the straight-line distance, perpendicular distance, and planar distance between two selected points; (slope measurement)

[0084] Rainstorm inundation analysis (topography): Utilizing real-world 3D model elevation data and the platform's powerful spatial analysis capabilities, the inundation area affected by rainfall is calculated, and the inundation area and the water depth distribution of the blister membrane are simulated.

[0085] like Figure 1 As shown, the present invention provides a GIS risk management system for the migration of pollutants in a mining area watershed, which includes a three-dimensional GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module.

[0086] The 3D GIS functional module acquires a real-world 3D model of the mining area using a drone. Equipped with a Sel 202S five-lens camera, the drone simultaneously collects real-world information from vertical and four oblique angles. This product combines oblique photogrammetry automatic modeling technology with GIS platform application technology to obtain POS data and pixel data. The data is then imported into processing software to obtain a high-precision real-world 3D model, digital orthophoto, and surface model. During project implementation, the overall 3cm real-world model was insufficient for further decision-making analysis of local terrain. Therefore, a new photogrammetric technique, close-up photogrammetry, was added to the existing oblique photogrammetry. To conduct close-up photogrammetry, the drone needs high-precision positioning and gimbal attitude control, and the photogrammetry software needs to support processing irregular flight path data. The close-up photogrammetry workflow is a process from scratch, from coarse to fine. Based on the original 3cm model, the target's positional and structural information is acquired. Then, based on the results of the 3cm model, the drone's close-up photogrammetry flight path is planned, ultimately generating a 1.5cm refined 3D model. By using photographic equipment to closely observe the object's surface and acquire high-resolution (sub-centimeter) images, photogrammetric processing is performed to restore the object's precise coordinates and detailed shape structure, thus reconstructing a detailed 3D model. This surpasses the accuracy requirements of other photogrammetric methods, meeting the needs of this project for further analysis and decision-making regarding local areas. After generating the high-precision 3D real-scene data model, topographic mapping software is used to draw contour lines and extract elevation points across the entire mining area, resulting in a 2D contour map of the region. This allows for a more intuitive combination of 2D and 3D analysis of the area's site conditions. Since this project requires data to be published on a data platform for 3D data analysis and management, the generated high-precision DEM data and tilt model are sliced ​​using CesiumLab platform software. The high-precision topographic data is then published to the GIS data platform for further analysis and processing, replacing the original low-precision, older version of the topographic data.

[0087] Using UAV aerial surveying technology, three-dimensional oblique and orthophoto images of the study area with a high resolution of 1.5cm were acquired and generated. CIS surface analysis, hydrological analysis and mathematical statistics methods were used to visualize and analyze the development characteristics of surface micro-runoff in micro-topography through data visualization analysis of slope, aspect, elevation profile, river network distribution, etc., and to find pollution migration paths.

[0088] Based on image algorithms, permeable and impermeable areas in the mining watershed are automatically identified (lush vegetation indicates impermeable areas, and no vegetation indicates permeable areas). In the permeable areas of the hydrological response unit, the excess infiltration runoff mechanism is used to calculate surface runoff and water infiltration. In the impermeable areas, water storage, evaporation, and excess infiltration runoff are calculated. Normal and extreme rainfall meteorological conditions are selected, and the GSFLOW model is used to calculate surface runoff, evaporation, and infiltration. Then, based on water balance, the actual rainwater infiltration into the regional reservoir is calculated.

[0089] The overall rainfall and rainfall infiltration in the mining area are calculated by measuring the spatial area, and the pollution flux is calculated by combining the hydrodynamic-water quality model coupling module.

[0090] Hydrodynamic-Water Quality Model Coupling Module: Used to simulate the migration process of pollutants in mining areas with rainwater runoff. Based on the hydrodynamic-water quality model coupling module, the model is established by inputting initial conditions, boundary conditions, and operating conditions to simulate the migration process of pollutants in mining areas. It can obtain the amount of pollutant migration and diffusion with rainwater runoff under different rainfall intensities and visualize it.

[0091] Hydrodynamic-Water Quality Model Coupling Module: The first step is to establish a hydrodynamic model. The foundation of hydrodynamic model construction is to mesh the terrain, input parameters such as roughness, boundary conditions, rainfall, evaporation, and wind speed, and substitute measured flow field data such as water level, flow rate, and flow direction into the model.

[0092] (1) Mesh generation of the hydrodynamic model

[0093] High-precision topographic data of the study area was acquired using UAV oblique photogrammetry and converted into a format compatible with MIKE software. The MIKE software mesh generator was used to generate a computational mesh for the study area. Considering factors such as model simulation accuracy, software computation time, and result accuracy requirements, a triangular mesh was adopted for the model. Further local meshing was implemented to account for terrain undulations and ensure higher computational efficiency.

[0094] (2) Initial conditions of the hydrodynamic model

[0095] The initial conditions involved in the hydrodynamic model include the initial water level and initial flow velocity. To avoid instability in the calculation, the initial conditions should be set as closely as possible to the actual data at the initial moment of the model simulation cycle.

[0096] (3) Boundary conditions of the hydrodynamic model

[0097] The upstream boundary is the inlet, and the downstream boundary is the outlet. Flow rates are set at the upstream boundary and the inlets of each tributary in the model, and water level values ​​are set at the downstream boundary.

[0098] (4) Setting hydrodynamic parameters

[0099] In the hydrodynamic calculations, the riverbed roughness and the model's start time were primarily considered. Roughness *n* is a comprehensive factor reflecting the resistance during water flow and is an important indicator in hydraulic calculations. High-resolution 1.5cm three-dimensional oblique and orthophoto imagery of the study area was acquired and generated using UAV aerial surveying technology. Roughness was defined according to topography, geomorphological features, and surface characteristics. Other model parameters, such as Coriolis force and wind force, used default values.

[0100] (5) Validation of hydrodynamic model

[0101] The model parameters were calibrated using measured data as a benchmark. The boundary locations were used as verification points to output the simulated water level and flow rate results. Then, measured water level and flow rate data from the study period were selected, and a comparison graph of the simulated and actual results was plotted. If the simulated values ​​are largely within the range of the measured values, with no significant differences, a high degree of trend agreement, and relatively small errors, it demonstrates that the hydrodynamic model has superior accuracy and can be used for relatively accurate hydrodynamic simulation studies.

[0102] The next step is to establish a water quality model. This model is based on a hydrodynamic model. Initial water quality conditions are set by pollutant concentrations, provided by measured data. Pollution input primarily consists of pollutant solutions leached from the storage yard by rainfall. By providing a given rainfall concentration, the model is added in the form of rainfall to best reflect actual pollution conditions. The water quality model's calculations are based on the hydrodynamic model. After calibrating the hydrodynamic model, the water quality model also needs calibration to ensure it accurately reflects water quality changes. The calibration and validation process mainly includes sensitivity analysis of water quality parameters, parameter calibration, and water quality validation. To assess the applicability of the water environment model, this paper selects the Skill Score (SI) and Root Mean Square Error (RMSE) between simulated and measured water quality data to quantitatively describe the similarity between the two. SI values ​​range from 0 to 1, representing "poor simulation results" to "perfectly matching simulation results," respectively. They are defined as follows:

[0103]

[0104]

[0105] In the formula, n is the number of water quality monitoring points; Q i and S i These are the measured water quality values ​​and the simulated water quality values, respectively.

[0106] Mine Watershed Pollution Risk Management Module: This module designs a method for early warning and management assessment of pollution risks in mine watersheds, with surface water quality and dynamic values ​​as the main reference indicators for pollution early warning.

[0107] The single-factor evaluation method, recommended in the "Surface Water Environmental Quality Standard" (GB3838-2002), was used to evaluate and classify the water quality of the basin. This method involves selecting the category of the worst-performing single indicator from all participating water quality indicators to determine the overall water quality category of the water area. The calculation formula is as follows:

[0108] G = maxG i

[0109]

[0110] In the formula: G i For the water quality category of the i-th pollutant, C i Let C be the concentration of the i-th pollutant. s The evaluation criteria for the i-th pollutant.

[0111] The comprehensive evaluation of surface water in a watershed adopts the comprehensive index evaluation method. This method first statistically analyzes the relative pollution indices of each pollution indicator, then calculates the pollution index of each pollutant. Based on the pollution index, the degree of pollution and the main pollutants can be determined. The calculation formula for the comprehensive pollution index evaluation method is as follows:

[0112]

[0113]

[0114] In the formula: C i and C s The meaning is the same as in formula (2); P i Let be the pollution index of the i-th pollutant; n is the number of water quality indicators being evaluated.

[0115] The evaluation and grading method is as follows:

[0116]

[0117]

[0118] If the early warning indicator only needs to provide early warning for a single factor water quality indicator, the concept of water quality change rate is introduced, and its calculation formula is as follows:

[0119]

[0120] In the formula: S i —Simulated water quality values;

[0121] Q i — Actual water quality measurement value;

[0122] t — time.

[0123] By analyzing and comparing the existing water quality and the predicted rate of change Q, values ​​are assigned to the surface water quality and its dynamics. The assignments are referenced in the table below:

[0124] Surface water quality and dynamic values ​​in mining watersheds (single factor)

[0125]

[0126] The surface water pollution warning level is set at five levels: No Warning, Minor Warning, Moderate Warning, Severe Warning, and Major Warning. No Warning indicates that the risk of surface water pollution is very low, and no warning is required. Minor Warning, Moderate Warning, Severe Warning, and Major Warning indicate that surface water faces varying degrees of pollution, and a warning is required.

[0127] The impact of pollutants on the watershed is assessed by calculating the pollutant migration and diffusion flux. The calculation results are exported and dynamically visualized by combining the three-dimensional GIS function module. Based on the surface water environmental quality standards, the system guides decision-makers to provide corresponding risk management strategies and solutions.

[0128] The pollutant migration and diffusion flux was calculated based on the simulation results of the hydrodynamic-water quality model. The calculation method is as follows:

[0129]

[0130] In the formula, Q(t) is the instantaneous flow rate, m 3 / s; C(t) is the instantaneous concentration, mg / L.

Claims

1. A GIS-based risk management system for pollutant migration in mining area watersheds, characterized in that: It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mine watershed pollution risk management module. The 3D GIS functional module includes a measurement unit, a micro-runoff analysis unit, and a 3D pollution model unit; the hydrodynamic-water quality model coupling module includes a realistic 3D model unit, a field investigation and sampling unit, a data preprocessing unit, a water quality model construction unit, a hydrodynamic model construction unit, and a hydrodynamic-water quality model coupling unit; the mine watershed pollution risk management module includes a pollutant watershed migration risk assessment and early warning unit, a pollutant migration dynamic visualization unit, and a risk control strategy unit. Measurement Unit: Includes spatial distance measurement, spatial area measurement, and triangulation. Spatial distance measurement: Used by UAV to calculate the straight-line distance between the coordinates of two selected points; Spatial area measurement: Measure the area of ​​the drawn plane; Triangulation: Calculate the straight-line distance, perpendicular distance, and planar distance between two selected points. Microrunoff Analysis Unit: Using UAV aerial surveying technology, three-dimensional oblique and orthophoto data of the study area with a high resolution of 1.5cm were acquired and generated. Then, a three-dimensional oblique model and an orthophoto model were generated based on the three-dimensional oblique and orthophoto data. Based on the ray projection algorithm, a corresponding number of rays were projected from the Z-axis coordinate system at a set density of 0.1 rays / m² to 100 rays / m², perpendicular to the XY-axis plane. Then, the intersection point of each ray with the three-dimensional oblique model was taken to obtain the corresponding elevation data. Finally, the GSFLOW software was used to extract the microrunoff of the mine in the watershed with high precision. 3D pollution model unit: used to perform 3D rendering of pollution migration data and visualize it, making hidden pollution easier to observe; Real-world 3D model unit: Real-world information is collected simultaneously from vertical and four oblique angles by five camera lenses of the drone. POS data and pixel data are obtained by combining oblique photogrammetry automatic modeling technology with GIS platform application technology. The data is then imported into processing software to obtain a high-precision real-world 3D model, digital orthophoto, and surface model. On-site survey and sampling unit: used to set the coordinates and navigation of UAV flight sampling points and to arrange sampling tasks; The data preprocessing unit is used to process the collected UAV sampling data according to a standard format, including editing the sampling number, latitude and longitude coordinates, detection concentration and sampling depth; Water quality model building unit: The migration of pollutants in water bodies, the pollutants formed by the leaching of pollutant solutions from the storage yard under the action of rainfall, are added to the model in the form of rainfall based on the given water quality concentration of rainfall, so as to match the actual pollution situation as closely as possible; Hydrodynamic model building unit: The terrain is gridded, and the roughness, boundary, rainfall, evaporation and wind speed parameters of the object are input. The measured water level, flow rate and flow direction flow field data are substituted into the model. Hydrodynamic and water quality model coupling unit: used to establish a hydrodynamic model and couple a solute migration model. The established model is then used to perform interpolation calculations and predictive simulations of pollutant migration in surface water, and to analyze the range and concentration distribution of pollutants. Pollutant Migration Risk Assessment and Early Warning Unit: Used to evaluate and classify the water quality of the basin, and to determine the surface water pollution early warning level as 5 levels, namely no warning - minor warning - moderate warning - severe warning - major warning; no warning means that the surface water faces the risk of pollution and no warning needs to be issued, while minor warning, moderate warning, severe warning and major warning mean that the surface water faces different degrees of pollution and an early warning needs to be issued; Risk control strategy unit: It assesses the impact of pollutants on the watershed by calculating the pollutant migration and diffusion flux, exports the calculation results, and uses the 3D GIS function module to dynamically visualize the pollutant migration process. Based on the surface water environmental quality standards, it guides decision-makers to provide corresponding risk control countermeasures and solutions.

2. A method for using the GIS risk management system for pollutant migration in a mining area watershed as described in claim 1, characterized in that... The steps are as follows: The drones acquire real-world information about the mining watershed, and then use this information to build a high-precision 3D model of the watershed. The drones also collect image data of pollutant migration in the mining area from vertical and four tilt angles, thereby converting the image data into a high-precision 3D model, including digital orthophotos and surface models. After generating a high-precision real-scene 3D data model, topographic mapping software is used to draw contour lines and extract elevation points for the entire mining area of ​​the mining basin, thereby collecting a 2D contour map of the entire mining area, so as to analyze the on-site conditions of the area in a more intuitive way by combining 2D and 3D. High-resolution 3D oblique and orthophoto images of the study area were acquired and generated using UAV aerial surveying technology. This allowed for the differentiation between vegetation-covered and vegetation-free areas of the mining area. After rainfall, ray casting algorithms were used to obtain surface microrunoff generated in the vegetation-free areas. Since vegetation has good water retention and its transpiration consumes some groundwater, only the movement of pollutants after infiltration into the ground with groundwater and with surface microrunoff needs to be considered. Pollutant movement with groundwater was detected using monitoring equipment. However, surface microrunoff is not only small but also gradually decreases with rainfall and disappears after the rainfall ends. Therefore, a combination of CIS surface analysis, hydrological analysis, and mathematical statistics methods is needed. The development characteristics of surface microrunoff in the micro-topography are analyzed through visualization of slope, aspect, and elevation profiles of the mining area. This reveals the short-term development pattern of surface microrunoff accompanying rainfall. Combining this with groundwater and the short-term surface microrunoff that occurs with rainfall, pollution migration paths along water flow are generated in a realistic 3D model of the mining area's watershed. Based on a realistic 3D model of pollutant migration in a mining area watershed, and combined with surface micro-runoff development characteristic data, a simulation of pollutant migration in the mining area watershed is conducted. A mathematical model of pollutant migration in the mining area watershed is established, including a hydrodynamic model and a water quality model. First, the terrain is gridded, and the roughness, boundary, rainfall, evaporation, and wind speed parameters of the object are input. The flow field data of the measured surface micro-runoff are used to construct a hydrodynamic model. Then, based on the hydrodynamic model, a water quality model of the surface micro-runoff is constructed. The pollutant concentration is used as the initial condition for water quality, and the pollution input is the pollutant solution leached from the stockpile under the action of rainfall. The amount of pollution input is given in the form of rainfall. The overall rainfall and rainfall infiltration in the mining area were calculated by measuring the spatial area. The instantaneous flow rate of surface microrunoff and the migration and diffusion flux of pollutants in the storage yard with microrunoff were simulated and calculated by combining hydrodynamic and water quality models. The calculated flux of pollutants from the stockpile migrating and diffusing with microrunoff is used to conduct pollution risk early warning and control assessment of the mining watershed. A single-factor evaluation method is employed to assess and classify the watershed's water quality. The comprehensive water quality category of the entire assessed water area is determined based on the category of the worst single indicator in the evaluation classification, thus directly reflecting the water quality status to meet water quality protection requirements. Then, a comprehensive index evaluation method is used to compare runoff sections with the same water quality category to identify the main pollutants and comprehensively reflect the pollution status of surface microrunoff water. The pollution indices of each pollution indicator are statistically analyzed to calculate the pollution index of each pollutant. Based on the pollution index, the degree of pollution and the main pollutants in the water body are determined, and whether to issue an early warning is determined according to the pre-set warning level. The impact of pollutants on the watershed is assessed by measuring the pollutant migration and diffusion flux. At the same time, the pollutant migration and diffusion flux values ​​are combined with the three-dimensional GIS function module to dynamically visualize the pollutant migration process. Based on the surface micro-runway environmental quality standards, the system guides decision-makers to give corresponding risk management countermeasures and solutions.

3. The GIS risk management method for pollutant migration in mining area watersheds according to claim 2, characterized in that, The process of obtaining surface microrunoff using the ray casting algorithm is as follows: Based on the three-dimensional oblique and orthophoto image data, a three-dimensional oblique model and an orthophoto model are generated. According to the set density of 0.1 rays / m² to 100 rays / m², a corresponding number of rays are projected from the Z-axis coordinate system at high altitude, perpendicular to the XY-axis plane. Then, the intersection point of each ray with the three-dimensional oblique model is taken to obtain the corresponding elevation data. The development characteristics of the watershed mine microrunoff are extracted and restored from the elevation data using GSFLOW software to improve the water flow data of surface microrunoff.

4. The GIS risk management method for pollutant migration in mining area watersheds according to claim 2, characterized in that, Image recognition algorithms are used to identify lush vegetation areas as impermeable areas and non-vegetated areas as permeable areas in mining area images. By selecting normal and extreme rainfall meteorological conditions, the surface runoff and water infiltration information of permeable areas can be calculated using the water temperature model GSFLOW and the infiltration runoff mechanism. Water storage, evaporation and infiltration runoff data of impermeable areas can be calculated. Then, the actual rainwater infiltration amount of the storage yard can be calculated based on the water balance.

5. The GIS risk management method for pollutant migration in mining area watersheds according to claim 2, characterized in that, The specific method for constructing a hydrodynamic model is as follows: a1. Mesh generation for the hydrodynamic model: High-precision topographic data of the study area was obtained through UAV oblique photogrammetry technology and converted into a format suitable for MIKE software. The computational grid of the study area was generated using the MIKE software mesh generator. Taking into account the requirements of model simulation accuracy, software computation time and result accuracy, the model adopted a triangular mesh. Considering the topographic relief, the mesh was further densified locally to ensure higher computational efficiency. a2. Set the initial conditions for the hydrodynamic model: The initial conditions include the initial water level and the initial flow velocity. In order to avoid instability in the calculation, the initial conditions should be consistent with the actual data at the initial moment in the model simulation cycle. a3. Set boundary conditions for the hydrodynamic model: Set the upstream boundary of the runoff path for pollutant migration as the inlet and the downstream boundary as the outlet. Set the flow rate value at the upstream boundary and the inlet of each tributary in the hydrodynamic model, and set the water level value at the downstream boundary. a4. Setting hydrodynamic parameters: In the hydrodynamic calculation process, the riverbed roughness of micro-runoff and the start time of the hydrodynamic model calculation are considered. The riverbed roughness is used as a comprehensive factor to reflect the resistance during the water flow process. Based on UAV aerial survey technology, 1.5cm high-precision resolution three-dimensional oblique and orthophoto image data of the study area are obtained and generated. The riverbed roughness is defined according to the topography, geomorphology and surface features. The other dry and wet boundary model parameters, eddy viscosity model parameters and bed friction force model parameters adopt the default values. a5. Verify the hydrodynamic model: Calibrate the model parameters based on measured data, use the boundary positions as verification points, output the simulated water level and flow rate results, then select the measured water level and flow rate data within the study period, plot the comparison chart of the simulated water level and flow rate results and the actual results, and compare them. If the simulated values ​​are within the range of the measured values ​​and there is no difference, and the trend is highly consistent, it proves that the hydrodynamic model has good accuracy and can be used for hydrodynamic simulation research.

6. The GIS risk management method for pollutant migration in mining area watersheds according to claim 5, characterized in that, Applicability assessment of the water quality model: The similarity between simulated and measured water quality data is quantitatively described using the skill score (SI) and root mean square error (RMSE). The SI value ranges from 0 to 1, representing "poor simulation results" to "perfectly matching simulation results," respectively. The formula is as follows: ， ， In the formula, n represents the number of water quality monitoring points deployed near the upstream inlet and downstream outlet of the watershed; Q i and S i These are the measured water quality values ​​and the simulated water quality values, respectively.

7. The GIS risk management method for pollutant migration in mining area watersheds according to claim 2, characterized in that, The watershed water quality is evaluated and classified using the single-factor evaluation method. The comprehensive water quality category of the water area is determined by selecting the category of the worst-performing single indicator from all participating water quality indicators. The calculation formula is as follows: ， ， In the formula: G i For the water quality category of the i-th pollutant, C i Let C be the concentration of the i-th pollutant. s The evaluation criteria for the i-th pollutant.

8. The GIS risk management method for pollutant migration in mining area watersheds according to claim 7, characterized in that, The comprehensive index evaluation method is used for micro-runoff in watersheds. This method first statistically analyzes the pollution indices of various pollution indicators, calculates the pollution index of each pollutant, and then determines the degree of pollution and the main pollutants based on these indices. The calculation formula for the comprehensive pollution index evaluation method is as follows: ， ， In the formula: P i Let be the pollution index of the i-th pollutant; n be the number of water quality indicators being evaluated. The evaluation and grading method is as follows: When P≦0.40, it indicates good water quality, meaning that most items are not detected and the few detected items are within the standard; when P is between 0.41 and 0.7, it indicates slight pollution, meaning that some items are detected and exceed the standard; when P is between 0.71 and 1, it indicates moderate pollution, meaning that two detected values ​​exceed the standard; when P is between 1.01 and 2.00, it indicates heavy pollution, meaning that a considerable number of detected values ​​exceed the standard; when P≥2.00, it indicates severe pollution, meaning that a considerable number of detected values ​​exceed the standard by several times or tens of times. If the early warning indicator only needs to provide early warning for a single factor water quality indicator, the concept of water quality change rate is introduced, and its calculation formula is as follows: ， In the formula: S i This represents the simulated water quality value; Q i t represents the measured water quality value; t represents time.

9. The GIS risk management method for pollutant migration in mining area watersheds according to claim 7, characterized in that, The calculation method for pollutant migration and diffusion flux is as follows: ， In the formula For instantaneous flow rate, m 3 / s .

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