Mining area groundwater pollution risk assessment system and method

Abstract

The invention discloses a mining area groundwater pollution risk assessment system and method, and relates to the technical field of mining area groundwater environmental protection, and the system comprises a hydrogeological data collection module, a pollution source data collection module, a dual grid division module, an overlapping area calculation module, and a dynamic risk assessment module. According to the method, a natural grid is generated through constraint Delaunay triangulation according to a waterproof boundary, a fault and a lithologic mandatory natural boundary, relative uniformity of hydrogeological characteristics in each evaluation unit is ensured, the natural law of an underground water system is followed from the space foundation, and meanwhile, the evaluation result is more accurate. According to the system, the influence radius is dynamically calculated according to the emission intensity of a pollution source and the parameters of pollutant degradability, a source grid is generated, the potential influence range of pollutants is accurately depicted, an overlapping area formed by fusing the influence radius and the source grid serves as a final evaluation unit, and normal form transformation from blind global investigation to accurate targeted positioning is achieved.

Description

Technical Field

[0001] This invention relates to the field of groundwater environmental protection technology in mining areas, specifically to a groundwater pollution risk assessment system and method for mining areas. Background Technology

[0002] Groundwater systems in mining areas are crucial for maintaining regional ecological balance and drinking water safety. However, the exploration and mining of mineral resources inevitably pose a severe challenge to this fragile system. Pollutants such as acidic mine drainage, heavy metal ions, and mineral processing reagents migrate and transform through the complex vadose zone and aquifer structure, exhibiting significant lag, concealment, and irreversibility in their pollution processes. Therefore, conducting scientific and accurate risk assessments before pollution occurs or in its early stages is a core prerequisite for effective protection of water resources and control of pollution sources in mining areas.

[0003] Currently, existing technologies mainly rely on geographic zoning methods based on regular grids or administrative boundaries for risk assessment. These methods have two inherent drawbacks: First, the assessment units they divide are severely disconnected from the actual hydrogeological units in nature, causing the assumption of uniformity of hydrogeological parameters within the assessment units to be invalid and failing to truly reflect the dominant pathways of groundwater flow and pollutant migration. Second, existing assessments are mostly static, that is, risk is fixed based on data at a certain time section, which cannot respond to the dynamic changes in the location and intensity of pollution sources caused by continuous mining activities in the mining area, as well as the transformation processes of pollutants in the underground environment over time, such as migration and attenuation. Such static snapshot-style assessment results are difficult to guide dynamic environmental management and emergency decision-making in mining areas.

[0004] In summary, the fundamental problem with existing technologies lies in the separation of the spatial division of assessment units from the natural flow field of groundwater and the dynamic processes of pollutants. This spatial misalignment and temporal lag often lead to deviations between risk assessment results and actual conditions. Therefore, developing a refined risk assessment technology that can achieve geological constraints and pollution-driven processes has become a critical technological bottleneck that urgently needs to be overcome in this field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the above-mentioned defects of the prior art and provide a mining area groundwater pollution risk assessment system and method that ensures the relative uniformity of hydrogeological characteristics within each assessment unit, follows the natural laws of the groundwater system from a spatial perspective, accurately delineates the potential impact range of pollutants, and realizes a paradigm shift from blind full-area investigation to precise targeted positioning by accurately locating key areas.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: A groundwater pollution risk assessment system for mining areas, comprising: Hydrogeological data acquisition module: used to collect and process hydrogeological data of the mining area, including spatial distribution data of water-bearing boundaries, spatial distribution data of water-rich areas, dynamic groundwater level data, and rock strata permeability coefficient data; Pollution source data acquisition module: used to collect and process pollution source data in the mining area. The pollution source data includes pollution source type, spatial location coordinates of pollution source, pollutant emission intensity data, and a list of characteristic pollutant types. Dual grid division module: connected to the hydrogeological data acquisition module and the pollution source data acquisition module respectively, used to divide natural grids based on hydrogeological boundaries according to the processed hydrogeological data, and to initially divide source grids based on preset pollutant influence radii according to the processed pollution source data; Overlay region calculation module: connected to the dual grid division module, used to receive spatial data of the natural grid and the source grid, and output the spatial boundary coordinates and attribute data of each overlay region in real time through spatial overlay analysis of the geographic information system; Dynamic risk assessment module: Connected to the overlay area calculation module, it is used to obtain attribute data of each overlay area, dynamically calculate the risk index of each overlay area, and automatically adjust its risk level according to the risk index threshold.

[0007] Preferably, the hydrogeological data acquisition module includes: Ground survey unit: used to obtain data on stratigraphic lithology, structural distribution, and spatial distribution of water-impermeable boundaries through field geological mapping and drilling; Physical exploration unit: using ground-penetrating radar to detect the spatial distribution and burial depth of water-rich areas in underground aquifers; Monitoring well network unit: It consists of groundwater monitoring wells deployed in the mining area. The wells are equipped with water level gauges and permeability testers to continuously monitor the spatiotemporal dynamic changes of groundwater level and the permeability coefficient of aquifer. The output data from the ground survey unit, the physical exploration unit, and the monitoring well network unit together constitute the hydrogeological data and are transmitted to the dual grid division module.

[0008] Preferably, the pollution source data acquisition module includes: Pollution source inventory management unit: Based on the environmental management archives of the mining area, establish a pollution source database including tailings ponds, spoil heaps, mine drainage points, and chemical storage areas, and record the unique identifier, geographical coordinates, main pollutant types and emission intensity of each pollution source; On-site monitoring sensor network unit: Composed of multi-parameter water quality sensors deployed around the pollution source, it monitors the water quality parameters of leachate and discharge outlet in real time, including pH value, heavy metal ion concentration, and chemical oxygen demand, thereby obtaining dynamic data on the actual discharge of pollution sources; Remote sensing inversion unit: By analyzing UAV remote sensing data, it identifies indirect indicators such as surface temperature anomalies and vegetation stress index in the pollution source area, and assists in assessing the diffusion range of the pollution source. The output data from the pollution source inventory management unit, the on-site monitoring sensor network unit, and the remote sensing inversion unit together constitute the pollution source data and are transmitted to the dual grid division module.

[0009] Preferably, in the dual-grid partitioning module, the natural grid is partitioned using the Delaunay triangulation algorithm based on hydrogeological boundary constraints, which discretizes the continuous hydrogeological body into irregular triangular mesh units, each of which is a natural grid. The execution steps of the Delaunay triangulation algorithm are as follows: Construction of control point set and constraint edges: Hydrogeological feature points and boundary lines are extracted from the data output by the hydrogeological data acquisition module to construct a control point set for triangulation. The control point set includes: the turning points of the aquitard boundary line, the sampling points of the fault line, the feature points of the water-rich area and the weakly water-rich area, and the spatial coordinate points of the monitoring well location. The aquitard boundary line and the fault line are defined as constraint edges to ensure that these boundaries are completely preserved as the edges of the triangle in the final triangulation network. Initial triangulation generation: The generated control point set and constraint edge data are input into the geographic information system, and the constrained Delaunay triangulation algorithm is called to generate an initial mesh composed of triangular elements. Each triangle's circumcircle does not contain any other control points, thus ensuring the mathematical optimality of the mesh. This makes each triangle as close as possible to an equilateral triangle, minimizing the occurrence of elongated and distorted triangles, and laying the geometric foundation for the stability and accuracy of subsequent numerical simulations. Composite natural grid generation: Based on the initial grid composed of triangular units, the triangular units are aggregated according to the hydrogeological properties inherited by the triangular units. Adjacent triangular units with the same hydrogeological properties are merged to form a composite natural grid with uniform hydrogeological properties. Grid attribute assignment: Assign a set of standardized attribute fields to each composite natural grid, including: grid unique identifier, area, average permeability coefficient, and dominant hydrogeological type.

[0010] Preferably, in the dual-mesh partitioning module, the step of partitioning the source mesh is as follows: Radius of influence calculation: For each pollution source in the pollution source inventory, calculate its initial radius of influence based on its pollution source type and real-time emission intensity data. ,in, The radius of influence of the pollution source. It is an empirical coefficient and its value ranges from (0.5, 2). For the emission intensity of pollutants, The duration of continuous emissions from the pollution source. The effective porosity of the aquifer; Source grid generation: Based on the center point of each pollution source, the calculated influence radius is used. To buffer the distance, a circular buffer polygon is generated, which serves as the source grid for the pollution source. Source grid attribute assignment: Assign a set of standardized attribute fields to each source grid, including: the identifier of the pollution source to which it belongs, the list of characteristic pollutants, the radius of influence used in the calculation, and the emission intensity.

[0011] Preferably, the execution process of the overlay region calculation module is as follows: Grid data reception and preprocessing: Receive natural grids and source grids from the dual grid generation module. Before overlay analysis, the two types of grid data need to be preprocessed, including coordinate system unification and topology checking. Spatial overlay analysis: Overlaying two natural meshes and a source mesh, all spatially intersecting natural meshes and the source mesh will be generated as a new overlay region polygon; Overlay region attribute construction: Assign a composite identifier to each generated overlay region and inherit the hydrogeological attributes of its parent natural grid and the pollution source attributes of its parent source grid to form a comprehensive attribute table for the overlay region; Dynamic update trigger: When the spatial data of any natural grid and source grid changes due to data updates, a new round of overlay analysis is triggered, realizing dynamic updates of the overlay region.

[0012] Preferably, the specific process by which the dynamic risk assessment module calculates the risk index and adjusts the risk level is as follows: Hydrogeological factors were extracted from the comprehensive attribute table of the overlay area. and hazardous factors of pollution sources The factors are then standardized to a uniform dimension in the [0, 1] interval. Construct a weighted summation model to calculate the risk index for each overlay region. ,in, It is the ultimate risk index. These are hydrogeological factors, obtained from the attributes of natural grids through multi-factor weighting, i.e. , I It is the total number of geological factors. It is the first The weights of each geological factor and , It is the first Observed values ​​of geological factors, These are pollution source hazard factors, obtained from the attributes of the source grid through multi-factor weighting, i.e. , It is the total number of pollution source hazard assessment factors. It is the first The weights of each hazard assessment factor and , It is the first Actual data for each hazard assessment factor. and The weighting coefficients for vulnerability and hazard are respectively, satisfying... + =1, and It is a nonlinear moderating index used to characterize the nonlinear effect of vulnerability and hazard on the contribution of total risk. This is the model residual term, with a value of 0.001; The system presets a risk index threshold range and calculates the results. The values ​​are mapped to corresponding high-risk, medium-risk, low-risk, and negligible-risk levels, where negligible risk is: Low risk: Medium risk: High risk: ; When the input pollution source data is updated, a recalculation process is triggered to dynamically adjust the risk level of each area.

[0013] The technical solution adopted by the present invention to further solve its technical problem is as follows: A method for risk assessment of groundwater pollution in mining areas, comprising the following steps: S100. Collect hydrogeological data, including water-retaining boundaries, water-rich areas, groundwater levels, and permeability coefficients, through the hydrogeological data acquisition module. S200. Through the pollution source data acquisition module, collect pollution source data including pollution source type, location, emission amount and pollutant type; Natural grids were generated using the constrained Delaunay triangulation algorithm based on S300 and S100 hydrogeological data. At the same time, source grids were generated based on S200 pollution source data. S400 receives the spatial data of the natural grid and source grid generated in S300, calculates the spatial overlay area of ​​all natural grids and source grids in real time, and generates an overlay area attribute table containing comprehensive hydrogeological and pollution source attributes. S500: Obtain the comprehensive attribute data of each overlay region output by S400, calculate the comprehensive risk index of each overlay region, and dynamically determine its risk level according to the preset threshold range.

[0014] The beneficial effects of the groundwater pollution risk assessment system and method for mining areas of this invention are as follows: This invention transforms the assessment unit from a man-made grid to a dual grid defined by both natural attributes and human activities. The system generates a natural grid through constrained Delaunay triangulation based on mandatory natural boundaries such as impermeable boundaries, faults, and lithology. This ensures the relative homogeneity of hydrogeological characteristics within each assessment unit, spatially adhering to the natural laws of the groundwater system. Simultaneously, the system dynamically calculates the influence radius based on parameters such as the emission intensity of pollution sources and the degradability of pollutants, generating a source grid that accurately characterizes the potential impact range of pollutants. By merging these two elements to form an overlay area as a final assessment unit, the identification of high-risk areas no longer relies solely on the proximity or high concentration of pollution sources, but rather on precisely locating key areas. This represents a paradigm shift from blind, comprehensive investigation to precise, targeted localization. Attached Figure Description

[0015] Figure 1 This is an operation flowchart of an embodiment of the mine area groundwater pollution risk assessment system of the present invention; Figure 2 This is a flowchart illustrating the steps of an embodiment of the groundwater pollution risk assessment method for mining areas according to the present invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0017] Example 1 This embodiment uses a metal mining area as the application scenario. Relying on a mining area groundwater pollution risk assessment system, it conducts dynamic assessments of groundwater pollution risks. The system acquires basic hydrogeological information and dynamic pollution source data of the mining area through a hydrogeological data acquisition module and a pollution source data acquisition module, respectively. Using a dual-grid partitioning module, it generates natural grids based on hydrogeological boundaries and divides source grids according to the influence radius calculated based on pollution source characteristics. The overlay region calculation module performs spatial overlay analysis of the two types of grids, generating overlay regions with comprehensive attributes. Finally, the dynamic risk assessment module calculates the risk index and classifies risk levels, achieving a refined and dynamic assessment of groundwater pollution risks in the mining area. This provides a scientific basis for groundwater environmental protection and pollution prevention in mining areas. Figure 1 As shown.

[0018] The hydrogeological data acquisition module, through multi-unit collaborative acquisition, obtains comprehensive and accurate hydrogeological data of the mining area, providing data support for subsequent natural grid division and risk index calculation. This module includes a ground survey unit, a physical exploration unit, and a monitoring well network unit. The ground survey unit, using field surveys as its core method, aims to obtain basic data on the lithology, structural distribution, and spatial distribution of aquitard boundaries in the mining area. Staff first determine key areas for field geological mapping based on existing geological survey reports, including mining area boundaries, known fault zones, and historical mining areas. Stratigraphic profile measurements are then conducted along a pre-set survey route, recording the lithological characteristics, thickness, and distribution range of different strata, while also marking stratigraphic contact relationships. All collected data is then organized and archived to form a stratigraphic lithology dataset, a structural distribution dataset, and a spatial distribution dataset of aquitard boundaries. The data set is distributed to provide the foundation for the subsequent construction of the control point set; the physical exploration unit uses ground-penetrating radar technology to detect the spatial distribution and burial depth of water-rich areas of underground aquifers, generating a dataset of spatial distribution of water-rich areas and burial depth of aquifers, supplementing the underground spatial information of hydrogeological data; the monitoring well network unit consists of multiple groundwater monitoring wells deployed in the mining area, equipped with water level gauges and permeability testers, to continuously monitor the spatiotemporal dynamic changes of groundwater levels and the permeability coefficient of aquifers, forming a dynamic groundwater level dataset and a rock stratum permeability coefficient dataset; the output data of the surface survey unit, the physical exploration unit, and the monitoring well network unit together constitute complete hydrogeological data, including spatial distribution data of impermeable boundaries, spatial distribution data of water-rich areas, dynamic groundwater level data, and rock stratum permeability coefficient data, which are finally transmitted to the dual grid division module.

[0019] The pollution source data acquisition module is responsible for acquiring static basic information and dynamic emission data of pollution sources in the mining area, providing data support for source grid division and calculation of pollution source hazard factors. This module includes a pollution source inventory management unit, an on-site monitoring sensor network unit, and a remote sensing inversion unit. The pollution source inventory management unit, based on the mining area's environmental management archives, constructs a comprehensive pollution source database, records static basic information of pollution sources, and organizes existing environmental management archives in the mining area, including documents, monitoring reports, and operation and maintenance records of pollution sources such as tailings ponds, spoil heaps, mine drainage points, and chemical storage areas. Based on the archive information, a unique identifier is assigned to each pollution source to ensure traceability. The geographical coordinates of each pollution source are obtained through on-site measurement or archive query. The main pollutant types of each pollution source are determined according to the pollution source type. Pollutant emission intensity is calculated by reviewing emission records and monitoring reports, and the information is entered into the database. The system constructs a pollution source database containing fields such as unique identifiers of pollution sources, geographical coordinates, major pollutant types, and emission intensity, forming static data such as pollution source types, spatial coordinates of pollution sources, pollutant emission intensity data, and a list of characteristic pollutant types. The on-site monitoring sensor network unit acquires dynamic emission data of pollution sources in real time through multi-parameter water quality sensors deployed around the pollution sources. Water quality parameters include pH value, heavy metal ion concentration, and chemical oxygen demand, supplementing the timeliness of static data and forming a dynamic emission dataset of pollution sources. The remote sensing inversion unit analyzes UAV remote sensing data to identify indirect indicators such as surface temperature anomalies and vegetation stress index in the pollution source area, assisting in assessing the diffusion range of pollution sources and providing supplementary information for defining the impact range of pollution sources. Together with the output data from the pollution source inventory management unit and the on-site monitoring sensor network unit, it constitutes complete pollution source data, which is then transmitted to the dual-grid partitioning module.

[0020] The dual-grid generation module generates natural and source grids separately to spatially discretize the hydrogeological natural attributes and the anthropogenic impact attributes of pollution sources, laying the foundation for subsequent overlay analysis. The module's operation includes two parts: natural grid generation and source grid generation. Natural grid generation is based on hydrogeological data and employs the Delaunay triangulation algorithm based on hydrogeological boundary constraints to ensure relatively uniform hydrogeological characteristics within each grid. The specific steps are as follows: Control point set and constraint edge construction: Hydrogeological feature points and boundary lines are extracted from the data output by the hydrogeological data acquisition module to construct a control point set for triangulation. The feature points include: the turning points of the aquitard boundary line, the sampling points of the fault line, the feature points of the water-rich area and the weakly water-rich area, and the spatial coordinates of the monitoring well location. At the same time, the aquitard boundary line and the fault line are defined as constraint edges. These boundaries are completely preserved as the edges of the triangle to avoid the grid crossing the boundary and causing uneven hydrogeological properties. Initial triangulation generation: The generated control point set and constraint edge data are input into the geographic information system, and the constrained Delaunay triangulation algorithm is called to generate an initial mesh composed of triangular elements. Each triangle's circumcircle does not contain any other control points, thus ensuring the mathematical optimality of the mesh. This makes each triangle as close as possible to an equilateral triangle, minimizing the occurrence of elongated and distorted triangles, and laying the geometric foundation for the stability and accuracy of subsequent numerical simulations. Composite natural grid generation: Based on the initial grid, the triangular units are aggregated according to the hydrogeological properties inherited by them. Adjacent triangular units with the same hydrogeological properties are merged to form a composite natural grid with uniform hydrogeological properties. Grid attribute assignment: Assign a set of standardized attribute fields to each composite natural grid, including: grid unique identifier, area, average permeability coefficient, and dominant hydrogeological type.

[0021] Source grid partitioning is based on pollution source data. The influence radius is calculated according to the characteristics of the pollution sources, and a ring-shaped buffer zone characterizing the potential impact range of pollutants is generated. The specific steps are as follows: Radius of influence calculation: For each pollution source in the pollution source inventory, based on its pollution source type and real-time emission intensity data, the formula is used. ,in, The radius of influence of the pollution source. It is an empirical coefficient and its value ranges from (0.5, 2). For the emission intensity of pollutants, The duration of continuous emissions from the pollution source. The effective porosity of the aquifer is calculated using this formula, which can dynamically reflect the differences in the potential impact range of different pollution sources and different emission stages. Source grid generation: Based on the center point of each pollution source, and with the calculated influence radius R as the buffer distance, a circular buffer polygon is generated. This buffer is the source grid of the pollution source, and its spatial range directly corresponds to the area affected by the pollutants. Source grid attribute assignment: Assign a set of standardized attribute fields to each source grid, including: the identifier of the pollution source to which it belongs, the list of characteristic pollutants, the radius of influence used in the calculation, and the emission intensity.

[0022] The overlay region calculation module performs spatial overlay analysis between the natural grid and the source grid to achieve spatial correlation between hydrogeological attributes and pollution source attributes, generating an overlay region that combines both types of attributes as the final risk assessment unit. The specific operation process is as follows: Grid data reception and preprocessing: Receive natural grid and source grid data from the dual grid generation module. Before overlay analysis, the two types of grid data need to be preprocessed, including coordinate system unification and topology checking. Spatial overlay analysis: The natural mesh and the source mesh are overlaid. By calculating the spatial intersection of the two types of meshes, the intersection of all natural meshes and source meshes that have spatial intersection will be generated as a new overlay region polygon. If a natural mesh overlaps with multiple source meshes, multiple overlay regions will be generated, each corresponding to the intersection with different source meshes. Overlay region attribute construction: Each generated overlay region is assigned a composite identifier, which is composed of the unique identifier of the natural grid and the pollution source identifier of the source grid, making it easy to trace its origin; at the same time, it inherits the hydrogeological attributes of its parent natural grid and the pollution source attributes of its parent source grid. These attributes are integrated to form a comprehensive attribute table for the overlay region. The comprehensive attribute table provides complete attribute data support for subsequent risk index calculation. Dynamic update triggering: A data update monitoring mechanism is established. When the spatial data of any natural grid and source grid changes due to data updates, the system automatically triggers a new round of overlay analysis, recalculates the overlay area, realizes dynamic updates of the overlay area, and ensures that the evaluation unit is consistent with the actual situation.

[0023] The dynamic risk assessment module extracts attribute data from overlay areas, constructs a risk index calculation model, and dynamically classifies risk levels, providing a basis for decision-making in mine pollution prevention and control. The specific operation process is as follows: Hydrogeological factors were extracted from the comprehensive attribute table of the overlay area. and hazardous factors of pollution sources The factors are then standardized to a uniform dimension in the [0, 1] interval. Construct a weighted summation model to calculate the risk index for each overlay region. ,in, It is the ultimate risk index. These are hydrogeological factors, obtained from the attributes of natural grids through multi-factor weighting, i.e. , I It is the total number of geological factors. It is the first The weights of each geological factor and , It is the first Observed values ​​of geological factors, These are pollution source hazard factors, obtained from the attributes of the source grid through multi-factor weighting, i.e. , It is the total number of pollution source hazard assessment factors. It is the first The weights of each hazard assessment factor and , It is the first Actual data for each hazard assessment factor. and The weighting coefficients for vulnerability and hazard are respectively, satisfying... + =1, and It is a nonlinear moderating index used to characterize the nonlinear effect of vulnerability and hazard on the contribution of total risk. This is the model residual term, with a value of 0.001; The system presets a risk index threshold range and calculates the results. The values ​​are mapped to corresponding high-risk, medium-risk, low-risk, and negligible-risk levels, where negligible risk is: Low risk: Medium risk: High risk: ; When the input pollution source data is updated, a recalculation process is triggered to dynamically adjust the risk level of each area.

[0024] In summary, this embodiment demonstrates the entire process of the groundwater pollution risk assessment system from data acquisition to risk output by applying it in a metal mining area. The entire system operation effectively realizes the refined and dynamic assessment of groundwater pollution risk in the mining area, providing accurate decision support for groundwater environmental protection in the mining area. It also verifies the feasibility and practicality of the groundwater pollution risk assessment system for the mining area.

[0025] Example 2 After completing the detailed operation of each module of the system, a standardized method for assessing groundwater pollution risk in mining areas can be used to connect the functions of each module and the data flow logic into a complete assessment process. This ensures the standardization and repeatability of the assessment work. This method relies on the mining area groundwater pollution risk assessment system to achieve a closed loop from data acquisition to risk output, such as... Figure 2 As shown, it includes the following steps: S100. Collect hydrogeological data, including water-retaining boundaries, water-rich areas, groundwater levels, and permeability coefficients, through the hydrogeological data acquisition module. Field geological mapping and drilling were conducted through ground survey units to obtain data on stratigraphic lithology, structural distribution, and spatial distribution of water-impermeable boundaries. The spatial distribution and burial depth of water-rich areas in underground aquifers are detected using ground-penetrating radar through physical exploration units. By monitoring well network units, the spatiotemporal dynamic changes of groundwater level and the permeability coefficient of aquifer are continuously monitored using the deployed groundwater monitoring wells; Integrate all hydrogeological data and transmit it to the dual-grid partitioning module; S200. Through the pollution source data acquisition module, collect pollution source data including pollution source type, location, emission amount and pollutant type; Through the pollution source inventory management unit, based on the environmental management archives of the mining area, a pollution source database is established to record the unique identifier, geographical coordinates, main pollutant types and emission intensity of each pollution source; By deploying multi-parameter water quality sensors through on-site monitoring sensor network units, the water quality parameters of leachate and discharge outlets are monitored in real time to obtain dynamic data on the actual discharge of pollution sources. By using remote sensing inversion units, we can analyze UAV remote sensing data and identify indirect indicators such as surface temperature anomalies and vegetation stress index in pollution source areas to help assess the spread of pollution sources. Integrate all pollution source data and transmit it to the dual-grid partitioning module; Natural grids were generated using the constrained Delaunay triangulation algorithm based on S300 and S100 hydrogeological data. At the same time, source grids were generated based on S200 pollution source data. Dividing natural grids: Extract hydrogeological feature points and boundary lines from hydrogeological data (such as the turning points of aquifer boundary lines, sampling points of fault lines, feature points of water-rich and weakly water-rich areas, and spatial coordinates of monitoring well locations). Define the water-tight boundary line and the fault line as constraint edges; The constrained Delaunay triangulation algorithm is used to generate triangular cell meshes; Adjacent triangular units are aggregated based on hydrogeological properties to form a composite natural grid; Assign attribute fields to each composite natural grid; Source mesh generation: For each pollution source, its radius of influence is calculated based on its pollution source type and real-time emission intensity data; Based on the center point of each pollution source, and using the calculated influence radius as the buffer distance, a circular buffer polygon is generated; Assign attribute fields to each source mesh; S400 receives the spatial data of the natural grid and source grid generated in S300, calculates the spatial overlay area of ​​all natural grids and source grids in real time, and generates an overlay area attribute table containing comprehensive hydrogeological and pollution source attributes. Receives spatial data from both natural and source grids; Perform a geographic information system spatial overlay analysis on the two grids to generate all overlay polygons that intersect in space; Assign a composite identifier to each overlay region and inherit the hydrogeological attributes of the parent natural grid and the pollution source attributes of the parent source grid to form a comprehensive attribute table for the overlay region; When the spatial data of the natural grid or source grid changes due to data updates, a new round of overlay analysis is triggered to achieve dynamic updates of the overlay region; S500: Obtain the comprehensive attribute data of each overlay region output by S400, calculate the comprehensive risk index of each overlay region, and dynamically determine its risk level according to the preset threshold range.

[0026] Dynamic risk assessment: Hydrogeological factors and pollution source hazard factors were extracted from the comprehensive attribute table of the overlay area; Hydrogeological factors and pollution source hazard factors are standardized and their dimensions are unified to the [0, 1] interval. The risk index for each overlay region is calculated using a weighted summation model. Based on the preset risk index threshold range, the risk index is mapped to the corresponding risk level; When the input pollution source data is updated, a recalculation process is triggered to dynamically adjust the risk level of each area; Output risk assessment results: Output the risk level of each overlay area and provide spatial visualization results for mining area environmental management and decision support.

Claims

1. A risk assessment system for groundwater pollution in mining areas, characterized in that, include: Hydrogeological data acquisition module: used to collect and process hydrogeological data of the mining area, including spatial distribution data of water-bearing boundaries, spatial distribution data of water-rich areas, dynamic groundwater level data, and rock strata permeability coefficient data; Pollution source data acquisition module: used to collect and process pollution source data in the mining area. The pollution source data includes pollution source type, spatial location coordinates of pollution source, pollutant emission intensity data, and a list of characteristic pollutant types. Dual grid division module: connected to the hydrogeological data acquisition module and the pollution source data acquisition module respectively, used to divide natural grids based on hydrogeological boundaries according to the processed hydrogeological data, and to initially divide source grids based on preset pollutant influence radii according to the processed pollution source data; Overlay region calculation module: connected to the dual grid division module, used to receive spatial data of the natural grid and the source grid, and output the spatial boundary coordinates and attribute data of each overlay region in real time through spatial overlay analysis of the geographic information system; Dynamic risk assessment module: Connected to the overlay area calculation module, it is used to obtain attribute data of each overlay area, dynamically calculate the risk index of each overlay area, and automatically adjust its risk level according to the risk index threshold.

2. The mining area groundwater pollution risk assessment system according to claim 1, characterized in that, The hydrogeological data acquisition module includes: Ground survey unit: used to obtain data on stratigraphic lithology, structural distribution, and spatial distribution of water-impermeable boundaries through field geological mapping and drilling; Physical exploration unit: using ground-penetrating radar to detect the spatial distribution and burial depth of water-rich areas in underground aquifers; Monitoring well network unit: It consists of groundwater monitoring wells deployed in the mining area. The wells are equipped with water level gauges and permeability testers to continuously monitor the spatiotemporal dynamic changes of groundwater level and the permeability coefficient of aquifer. The output data from the ground survey unit, the physical exploration unit, and the monitoring well network unit together constitute the hydrogeological data and are transmitted to the dual grid division module.

3. The mining area groundwater pollution risk assessment system according to claim 1 or 2, characterized in that, The pollution source data acquisition module includes: Pollution source inventory management unit: Based on the environmental management archives of the mining area, establish a pollution source database including tailings ponds, spoil heaps, mine drainage points, and chemical storage areas, and record the unique identifier, geographical coordinates, main pollutant types and emission intensity of each pollution source; On-site monitoring sensor network unit: Composed of multi-parameter water quality sensors deployed around the pollution source, it monitors the water quality parameters of leachate and discharge outlet in real time, including pH value, heavy metal ion concentration, and chemical oxygen demand, thereby obtaining dynamic data on the actual discharge of pollution sources; Remote sensing inversion unit: By analyzing UAV remote sensing data, it identifies indirect indicators such as surface temperature anomalies and vegetation stress index in the pollution source area, and assists in assessing the diffusion range of the pollution source. The output data from the pollution source inventory management unit, the on-site monitoring sensor network unit, and the remote sensing inversion unit together constitute the pollution source data and are transmitted to the dual grid division module.

4. The mining area groundwater pollution risk assessment system according to claim 1 or 2, characterized in that, In the dual-grid partitioning module, the natural grid is partitioned using the Delaunay triangulation algorithm based on hydrogeological boundary constraints. This discretizes the continuous hydrogeological body into irregular triangular mesh elements, each of which is a natural grid. The execution steps of the Delaunay triangulation algorithm are as follows: Construction of control point set and constraint edge: Hydrogeological feature points and boundary lines are extracted from the data output by the hydrogeological data acquisition module to construct a control point set for triangulation. The control point set includes: the turning point of the water-impermeable boundary line, the sampling point of the fault line, the feature points of the water-rich area and the weak water-rich area, and the spatial coordinate points of the monitoring well location. The water-impermeable boundary line and the fault line are defined as constraint edges. Initial triangulation generation: Input the generated control point set and constraint edge data into the geographic information system, call the constrained Delaunay triangulation algorithm to generate an initial mesh composed of triangular elements; Composite natural grid generation: Based on the initial grid composed of triangular units, the triangular units are aggregated according to the hydrogeological properties inherited by the triangular units. Adjacent triangular units with the same hydrogeological properties are merged to form a composite natural grid with uniform hydrogeological properties. Grid attribute assignment: Assign a set of standardized attribute fields to each composite natural grid, including: grid unique identifier, area, average permeability coefficient, and dominant hydrogeological type.

5. The mining area groundwater pollution risk assessment system according to claim 1 or 2, characterized in that, In the dual-mesh partitioning module, the steps for partitioning the source mesh are as follows: Radius of influence calculation: For each pollution source in the pollution source inventory, calculate its initial radius of influence based on its pollution source type and real-time emission intensity data. ,in, The radius of influence of the pollution source. It is an empirical coefficient and its value ranges from (0.5, 2). For the emission intensity of pollutants, The duration of continuous emissions from the pollution source. The effective porosity of the aquifer; Source grid generation: Based on the center point of each pollution source, the calculated influence radius is used. To buffer the distance, a circular buffer polygon is generated, which serves as the source grid for the pollution source. Source grid attribute assignment: Assign a set of standardized attribute fields to each source grid, including: the identifier of the pollution source to which it belongs, the list of characteristic pollutants, the radius of influence used in the calculation, and the emission intensity.

6. The risk assessment system for groundwater pollution in mining areas according to claim 1 or 2, characterized in that, The execution process of the overlay region calculation module is as follows: Grid data reception and preprocessing: Receive natural grids and source grids from the dual grid generation module. Before overlay analysis, the two types of grid data need to be preprocessed, including coordinate system unification and topology checking. Spatial overlay analysis: Overlaying two natural meshes and a source mesh, all spatially intersecting natural meshes and the source mesh will be generated as a new overlay region polygon; Overlay region attribute construction: Assign a composite identifier to each generated overlay region and inherit the hydrogeological attributes of its parent natural grid and the pollution source attributes of its parent source grid to form a comprehensive attribute table for the overlay region; Dynamic update trigger: When the spatial data of any natural grid and source grid changes due to data updates, a new round of overlay analysis is triggered, realizing dynamic updates of the overlay region.

7. The mining area groundwater pollution risk assessment system according to claim 1 or 2, characterized in that, The specific process by which the dynamic risk assessment module calculates the risk index and adjusts the risk level is as follows: Hydrogeological factors were extracted from the comprehensive attribute table of the overlay area. and hazardous factors of pollution sources The factors are then standardized to a uniform dimension in the [0, 1] interval. Construct a weighted summation model to calculate the risk index for each overlay region. ,in, It is the ultimate risk index. These are hydrogeological factors, obtained from the attributes of natural grids through multi-factor weighting, i.e. , I It is the total number of geological factors. It is the first The weights of each geological factor and , It is the first Observed values ​​of geological factors, These are pollution source hazard factors, obtained from the attributes of the source grid through multi-factor weighting, i.e. , It is the total number of pollution source hazard assessment factors. It is the first The weights of each hazard assessment factor and , It is the first Actual data for each hazard assessment factor. and The weighting coefficients for vulnerability and hazard are respectively, satisfying... + =1, and It is a nonlinear moderating index used to characterize the nonlinear effect of vulnerability and hazard on the contribution of total risk. This is the model residual term, with a value of 0.001; The system presets a risk index threshold range and calculates the results. The values ​​are mapped to corresponding high-risk, medium-risk, low-risk, and negligible-risk levels, where negligible risk is: Low risk: Medium risk: High risk: ; When the input pollution source data is updated, a recalculation process is triggered to dynamically adjust the risk level of each area.

8. A method for assessing groundwater pollution risk in mining areas, applicable to the groundwater pollution risk assessment system described in any one of claims 1 to 7, characterized in that, Includes the following steps: S100. Collect hydrogeological data, including water-retaining boundaries, water-rich areas, groundwater levels, and permeability coefficients, through the hydrogeological data acquisition module. S200. Through the pollution source data acquisition module, collect pollution source data including pollution source type, location, emission amount and pollutant type; Natural grids were generated using the constrained Delaunay triangulation algorithm based on S300 and S100 hydrogeological data. At the same time, source grids were generated based on S200 pollution source data. S400 receives the spatial data of the natural grid and source grid generated in S300, calculates the spatial overlay area of ​​all natural grids and source grids in real time, and generates an overlay area attribute table containing comprehensive hydrogeological and pollution source attributes. S500: Obtain the comprehensive attribute data of each overlay region output by S400, calculate the comprehensive risk index of each overlay region, and dynamically determine its risk level according to the preset threshold range.

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