Geographic information system-based simulation method and system for heavy metal migration path in mining area

Abstract

The application provides a mine area heavy metal migration path simulation method and system based on a geographic information system, relates to the technical field of mine area ecological environment simulation, and first constructs mine area environment element correlation layer groups and establishes correlation relations; obtains heavy metal occurrence state data and dynamically matches to generate a correlation data table; analyzes multi-driving factor interaction influence to generate a dynamic coupling rule set; constructs a stage-by-stage migration path simulation model based on the above data and the rule set to generate a data set; imports the data set into a geographic information system for dynamic visual rendering, and outputs a visual result containing stage conversion node identification, so that the migration path of the heavy metal in the mine area can be accurately simulated.

Description

Technical Field

[0001] This invention relates to the field of mining area ecological environment simulation technology, and more specifically, to a method and system for simulating the migration path of heavy metals in mining areas based on geographic information systems. Background Technology

[0002] In the study of the ecological environment of mining areas, accurate simulation of heavy metal migration pathways is crucial for assessing the environmental pollution risks of mining areas and formulating effective pollution prevention and control measures. Currently, traditional methods for studying heavy metal migration in mining areas have many limitations.

[0003] Some studies focus solely on the impact of single environmental factors on heavy metal migration, such as considering only the effects of topography or soil properties on heavy metal diffusion, while neglecting the fact that the mining environment is a complex system in which multiple environmental factors, including topography, soil, hydrology, and vegetation cover, are interconnected and influence each other. Such single-factor research methods cannot comprehensively and accurately reflect the actual migration of heavy metals in the mining environment.

[0004] Furthermore, some studies have not adequately considered the interactions and priorities among the driving factors of heavy metal migration. In reality, heavy metal migration in mining areas is driven by multiple factors, such as rainfall, topographic slope, and soil pH. These factors exhibit complex interactions, and different factors have varying degrees of influence on the migration process. Existing methods struggle to accurately determine the priorities and interactions of these factors, leading to significant discrepancies between simulation results and actual conditions. Moreover, traditional methods often lack dynamic and visual representations when simulating heavy metal migration pathways, making it difficult to intuitively present the migration process and changes of heavy metals at different stages. This hinders researchers' dynamic monitoring and analysis of heavy metal pollution in mining areas. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for simulating the migration path of heavy metals in mining areas based on a geographic information system, the method comprising:

[0006] Construct a mining area environmental element association layer group, which includes a mining area topographic element layer, a mining area soil element layer, a mining area hydrological element layer, and a mining area vegetation cover element layer, and establish spatial location association and attribute association relationships between each element layer;

[0007] Obtain data on the occurrence status of heavy metals in the mining area, dynamically match the data on the occurrence status of heavy metals in the mining area with the associated layer group of environmental elements in the mining area, and generate a data table on the association between occurrence status and environmental elements.

[0008] An interactive influence analysis was conducted on multiple driving factors of heavy metal migration in mining areas to determine the priority and interaction relationship of each driving factor on the migration process, and to generate a dynamic coupling rule set of multiple driving factors.

[0009] Based on the data table relating endowment state and environmental elements and the dynamic coupling rule set of multiple driving factors, a phased migration path simulation model is constructed, and a phased migration path dataset of heavy metals in the mining area is generated through this phased migration path simulation model.

[0010] The dataset of phased migration paths of heavy metals in mining areas is imported into a geographic information system (GIS). The GIS is then used to perform dynamic visualization rendering of the dataset, outputting a visualization result of the dynamic migration paths of heavy metals in mining areas that includes stage transition node identifiers.

[0011] Furthermore, embodiments of the present invention also provide a mining area heavy metal migration path simulation system based on a geographic information system, characterized in that it includes:

[0012] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described method for simulating the migration path of heavy metals in mining areas based on a geographic information system by executing the machine-executable instructions.

[0013] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of the geographic information system-based heavy metal migration path simulation system for mining areas reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the geographic information system-based heavy metal migration path simulation system for mining areas to execute the above-described geographic information system-based heavy metal migration path simulation method for mining areas.

[0014] Based on the above, a group of associated layers containing various mining area environmental elements was first constructed, and spatial location and attribute relationships between each element layer were established, integrating mining area environmental information. By dynamically matching the heavy metal occurrence status data of the mining area with the associated layer group, a data table relating occurrence status to environmental elements was generated, achieving a close integration of heavy metal occurrence status and environmental elements, which can more accurately reflect the initial distribution of heavy metals in the mining area environment. Interactive influence analysis of multiple driving factors was conducted, and a dynamic coupling rule set was generated, fully considering the interaction and priority of each driving factor, making the simulation process more consistent with reality and improving the accuracy of the simulation results. Based on the above data and rule set, a phased migration path simulation model was constructed, generating a phased migration path dataset that can present the migration process and change patterns of heavy metals at different stages. The dataset was imported into a geographic information system for dynamic visualization rendering, outputting visualization results containing stage transition node identifiers, allowing researchers to observe the dynamic migration path of heavy metals, facilitating dynamic monitoring and analysis. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the execution flow of the method for simulating the migration path of heavy metals in mining areas based on geographic information systems provided in this embodiment of the invention.

[0016] Figure 2 This is a schematic diagram of exemplary hardware and software components of a mining area heavy metal migration path simulation system based on a geographic information system provided in an embodiment of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for simulating the migration path of heavy metals in a mining area based on a geographic information system, according to an embodiment of the present invention. The following is a detailed description of this method.

[0018] Step S110: Construct a mining area environmental element association layer group, which includes a mining area topographic element layer, a mining area soil element layer, a mining area hydrological element layer, and a mining area vegetation cover element layer, and establish spatial location association and attribute association relationships between each element layer.

[0019] This embodiment takes a non-ferrous metal mining area as the research object. The mining area covers an area of ​​approximately fifty square kilometers and encompasses diverse terrains such as mountains, hills, and valleys. Historically, there has been heavy metal pollution caused by mining activities. To achieve accurate simulation of the heavy metal migration path in this mining area, it is first necessary to construct a group of associated environmental elements. This process requires integrating data from four major categories of environmental elements: topography, soil, hydrology, and vegetation, and establishing the relationships between layers through spatial coordinates and attribute mapping.

[0020] Step S111: Obtain the original topographic data of the mining area, perform slope information extraction and aspect information extraction processing on the original topographic data of the mining area, and generate a mining area topographic feature layer containing slope information and aspect information.

[0021] The original terrain data originated from UAV lidar measurements covering the entire mining area, with a point cloud density of several points per square meter and elevation accuracy down to the centimeter level. The raw point cloud data was imported into a geographic information system (GIS), and a digital elevation model (DEM) was generated using an irregular triangular network (ITN) modeling method, with the grid size set to several meters. Based on the DEM, a third-order inverse distance weighted difference algorithm was used to calculate the slope value of each grid cell. The slope value represents the degree of surface inclination and is calculated by the ratio of the elevation difference to the horizontal distance between the grid center and neighboring grid cells. The slope values ​​were divided into several levels, such as 0-5 degrees, 5-15 degrees, 15-25 degrees, 25-35 degrees, and above 35 degrees, each corresponding to a different slope level attribute. Simultaneously, the maximum gradient algorithm is used to calculate the aspect value, which represents the slope orientation. This aspect value is determined by calculating the projection direction of the grid normal vector onto the horizontal plane, starting with true north as 0 degrees and increasing clockwise to 360 degrees, dividing the area into eight directional intervals: North, Northeast, East, Southeast, South, Southwest, West, and Northwest. Each interval corresponds to a specific aspect attribute. The slope grade attribute and aspect attribute are then appended to each grid cell of the digital elevation model, forming a topographic feature layer for the mining area. The layer coordinate system adopts the Gauss-Krüger projection, consistent with the actual surveying of the mining area.

[0022] Step S112: Obtain the original soil data of the mining area, perform soil type classification and soil porosity annotation on the original soil data of the mining area, and generate a soil feature layer of the mining area containing soil type attributes and soil porosity information.

[0023] Raw soil data for this non-ferrous metal mining area was collected through a systematic sampling method. Several soil sampling points were established within the mining area using a grid method, and topsoil samples (0-20 cm) were collected at each point. The soil samples were sent to the laboratory for particle size analysis. The content of sand, silt, and clay particles was determined using sieving and densitometer methods. The soil type for each sampling point was determined according to soil texture classification standards, such as loam, clay, sandy soil, and sandy loam. Simultaneously, soil bulk density and field water holding capacity were measured using a ring cutter method. Soil porosity was calculated based on the soil particle size composition. Soil porosity represents the percentage of pore volume in the total soil volume and is calculated from soil bulk density and particle density using a specific empirical formula. Soil type and porosity data were entered into a database and linked to the latitude and longitude coordinates of the sampling points. In the geographic information system, Kriging interpolation was used to spatially interpolate the discrete soil sampling point data, generating a soil type raster layer and a soil porosity raster layer covering the entire mining area. The raster size was consistent with the topographic feature layer. By linking the attributes of two raster layers, each raster cell can simultaneously contain soil type attributes and soil porosity information, thus forming a soil feature layer for the mining area.

[0024] Step S113: Obtain raw hydrological data of the mining area, perform runoff path extraction and water level change trend analysis on the raw hydrological data of the mining area, and generate a hydrological element layer of the mining area containing runoff path information and water level dynamic information.

[0025] Raw hydrological data for the non-ferrous metal mining area were collected, including hydrological station observation data, remote sensing imagery, and digital elevation model (DEM) data from the past ten years. The hydrological station data included daily water level and flow data from several monitoring points, while the remote sensing imagery consisted of high-resolution optical images from the rainy and dry seasons. Based on the DEM, the D8 algorithm was used to extract the flow direction matrix of the mining area. The cumulative runoff volume was calculated based on the flow direction, and a cumulative runoff threshold was set. Grids with cumulative runoff volumes exceeding the threshold were identified as river channels, thus extracting the runoff path network of the mining area, including main streams, tributaries, and seasonal streams, forming a runoff path vector layer. Time series analysis was performed on the hydrological station water level data. The moving average method was used to remove short-term fluctuations, extracting the highest, lowest, and average water levels for each year. Combined with the water body range changes interpreted from the remote sensing imagery, the dynamic information of water levels in different seasons was analyzed. The dynamic information of water levels was divided into several levels according to time periods, such as high-water season, normal-water season, and low-water season, with each level corresponding to a specific water level range and duration. Spatially link the runoff path vector layer with the dynamic water level attribute, and label the corresponding dynamic water level information for different river sections of each runoff path to generate a hydrological element layer for the mining area.

[0026] Step S114: Obtain the original vegetation data of the mining area, perform vegetation type identification and vegetation coverage classification processing on the original vegetation data of the mining area, and generate a vegetation coverage element layer of the mining area containing vegetation type attributes and vegetation coverage level information.

[0027] Raw vegetation data for the non-ferrous metal mining area was acquired, including high-resolution multispectral remote sensing images from spring and summer. These images included blue, green, red, and near-infrared bands with a spatial resolution of several meters. An object-oriented image classification method was used to process the remote sensing images. First, multi-scale segmentation was performed, dividing the image into several object units based on spectral and shape heterogeneity parameters. The segmentation scale parameters were set according to vegetation distribution characteristics. For each object unit, spectral features (such as band reflectance, normalized difference vegetation index, and ratio vegetation index), texture features (such as mean, variance, and entropy), and shape features (such as area, perimeter, and compactness) were extracted. A vegetation type sample library was established through field surveys, including types such as arbor forests (e.g., pine and oak), shrub forests, herbaceous plants, crops, and bare land, with each type containing several sample objects. A random forest classifier was used to classify the object units. The classifier contained several decision trees, and the feature importance weights were determined through training with the sample library. Finally, the vegetation type classification results were output, forming a vegetation type vector layer. Simultaneously, vegetation cover is calculated based on the Normalized Difference Vegetation Index (NDVI), which is derived from near-infrared and red band reflectance. Vegetation cover is obtained by inverting an empirical model based on the NDVI and field-measured cover data. Vegetation cover is divided into several levels, such as high cover (greater than a certain percentage), medium cover (from a certain percentage to a certain percentage), and low cover (less than a certain percentage), generating a vegetation cover level raster layer. The vegetation type vector layer and the vegetation cover level raster layer are spatially overlaid, ensuring that each vegetation type polygon contains the corresponding cover level attribute, forming a vegetation cover element layer for the mining area.

[0028] Step S115: Import the mining area topographic feature layer, mining area soil feature layer, mining area hydrological feature layer, and mining area vegetation cover feature layer into the layer association module of the geographic information system. Based on the spatial coordinate information of each feature layer, establish the runoff path and topographic adaptation association between the mining area topographic feature layer and the mining area hydrological feature layer.

[0029] The generated mining area topographic, soil, hydrological, and vegetation cover layers are uniformly converted to the same coordinate system to ensure spatial location matching. Importing the layer association module of the geographic information system, a runoff path and topographic adaptation relationship is first established between the topographic and hydrological layers. Through spatial overlay analysis, the raster cells of the topographic layer traversed by each runoff path are extracted, and the slope grade and aspect attributes of these cells are obtained. The consistency between the runoff path direction and slope aspect is analyzed, and the slope change rate along the runoff path is calculated. The slope change rate is associated with the runoff path grade (e.g., main stream, tributary), establishing a model of the adaptation relationship between topographic features and runoff path development. For example, main streams typically extend along areas with gentle slopes and consistent slope aspects, while tributaries are distributed in tributary ditches with significant slope variations. The above association relationship is stored in the form of an attribute table, which includes fields such as runoff path ID, corresponding slope grade sequence, slope aspect interval, and slope change rate, realizing the spatial association between the topographic and hydrological layers.

[0030] Step S116: Based on the spatial distribution range information of the soil feature layer in the mining area, establish the root distribution adaptation relationship between the soil feature layer in the mining area and the vegetation cover feature layer in the mining area.

[0031] Step S1161: Extract the spatial distribution range boundary information of each soil type from the soil feature layer of the mining area to generate a soil type spatial distribution boundary dataset.

[0032] A soil feature layer of the mining area is loaded into the Geographic Information System (GIS). Raster cells of different soil types are filtered using the attribute query function. Raster cells of the same soil type are converted into vector polygons, and the boundary coordinates of the polygons are extracted to generate a spatial distribution boundary dataset for soil types. This dataset contains a unique identifier for each soil type, the vertex coordinate sequence of the boundary polygons, and attributes such as area. For example, loam soil corresponds to a polygon boundary defined by several vertex coordinates, and sandy soil corresponds to another polygon boundary. Each boundary dataset is independent and covers the entire mining area.

[0033] Step S1162: Extract the spatial distribution range boundary information of each vegetation type from the vegetation cover element layer of the mining area, generate a spatial distribution boundary dataset of vegetation types, import the spatial distribution boundary dataset of soil types and the spatial distribution boundary dataset of vegetation types into the spatial overlay analysis module of the geographic information system, perform boundary overlay processing, and determine the spatial overlap area between different soil types and different vegetation types.

[0034] Using a method similar to step S1161, vector polygon boundaries for each vegetation type are extracted from the vegetation cover feature layer of the mining area to generate a spatial distribution boundary dataset for vegetation types. The spatial distribution boundary datasets for soil types and vegetation types are simultaneously loaded into the spatial overlay analysis module of the geographic information system. A polygon overlay operation is selected, and the overlay rule is set to retain the intersection area of ​​the two. The system automatically calculates the spatial intersection of each soil type polygon and each vegetation type polygon, generating several spatially overlapping polygon regions. Each overlapping region contains the corresponding soil type ID and vegetation type ID attributes. For example, the overlapping region of the loam type polygon and the arbor forest type polygon forms a new polygon, marked as the loam-arbor forest overlapping region.

[0035] Step S1163: For each spatially overlapping region, extract the soil type attributes corresponding to that spatially overlapping region, and query the physicochemical property parameters of that soil type, including soil pH, soil nutrient content, and soil clay ratio.

[0036] For each spatially overlapping region, the corresponding soil type attributes are obtained through the attribute connection function. Based on the soil type ID, the standard physicochemical property parameters for that soil type are retrieved from the soil physicochemical property database. Soil pH is expressed as a value and determined using the laboratory potentiometry method; soil nutrient content includes the mass fraction of major nutrients such as nitrogen, phosphorus, and potassium, determined using spectrophotometry; the soil clay ratio is the percentage of particles smaller than a certain micrometer in diameter in the total mass of the soil, determined using a laser particle size analyzer. The retrieved physicochemical property parameters are then appended to the corresponding polygonal attribute table of the spatially overlapping region. For example, in the loam-arbor forest overlapping region, the pH value is a certain range, the nitrogen content is a certain percentage, and the clay ratio is a certain percentage.

[0037] Step S1164: Extract the vegetation type attributes corresponding to the spatially overlapping area, and query the biological characteristic parameters of the vegetation type, including root depth, root distribution density, and root exudate type.

[0038] Similarly, for each spatially overlapping region, the corresponding biological characteristic parameters are retrieved from the plant biological characteristic database based on the vegetation type ID. Root depth refers to the maximum depth of the main root system of the vegetation, measured in the field by excavation or root drilling; root distribution density represents the length or weight of roots per unit volume of soil, determined by soil column sampling; root exudate types include organic acids, amino acids, polysaccharides, etc., determined by high-performance liquid chromatography analysis of root exudate composition. These parameters are added to the attribute table of the spatially overlapping region. For example, for the arbor forest type, the root depth is a certain meter, the root distribution density is a certain length per cubic meter, and the root exudate types include citric acid and malic acid.

[0039] Step S1165: Analyze the compatibility between the physicochemical properties of the soil type and the biological characteristics of the vegetation type, and determine whether the soil type meets the environmental requirements for root growth of the corresponding vegetation type.

[0040] For each spatially overlapping area, a matching rule is established between soil physicochemical properties and the root growth requirements of vegetation. For example, the root growth of arbor forests requires neutral to slightly acidic soil pH (a certain range), high nitrogen, phosphorus, and potassium content (a certain content range), and a suitable clay ratio (a certain ratio range). The soil pH, nutrient content, and clay ratio of the overlapping area are compared with the required range of arbor forests. If all parameters are within the required range, the soil type is determined to meet the environmental requirements for arbor forest root growth; if any parameter exceeds the range, such as pH being too high or too low, it is determined not to meet the requirements. For shrub forests, whose root systems are shallower and whose nutrient requirements are relatively lower, the judgment criteria differ from those for arbor forests, and the matching rule needs to be adjusted according to the specific biological characteristics of the vegetation type.

[0041] Step S1166: For spatially overlapping areas that meet environmental requirements, determine the root distribution compatibility level between the soil type and the vegetation type. The compatibility level is based on the degree of matching between soil nutrient content and the nutrient absorption requirements of vegetation roots.

[0042] For spatially overlapping areas determined to meet environmental requirements, the matching degree between soil nutrient content and the nutrient absorption requirements of vegetation roots is further calculated. The matching degree is obtained by standardizing the ratio of actual soil nutrient content to required content; the closer the ratio is to 1, the higher the matching degree. Adaptability levels are assigned based on the matching degree values. For example, a matching degree above a certain range is considered excellent, between a certain range is good, and between a certain range is average. For instance, a certain ratio of nitrogen content in loam to the absorption requirements of tree roots indicates a high matching degree and is classified as excellent; a certain ratio of nitrogen content in sandy loam indicates a moderate matching degree and is classified as good. The adaptation level is added as an attribute to the overlapping area polygon.

[0043] Step S1167: For spatially overlapping areas that do not meet environmental requirements, analyze the key parameters in the soil physicochemical properties that restrict the growth of vegetation roots, and determine the reasons for the mismatch between the root distribution of the soil type and the vegetation type.

[0044] For spatially overlapping areas that do not meet environmental requirements, soil physicochemical properties are examined one by one to identify parameters that exceed the growth requirements of plant roots; these parameters are the limiting factors. For example, in areas where clay soils overlap with herbaceous plants, an excessively high proportion of clay particles leads to poor aeration, exceeding the aeration requirements of herbaceous plant roots. In this case, the clay particle proportion is the key limiting parameter, and the mismatch is due to the excessively high clay particle proportion. If multiple parameters exceed the range simultaneously, principal component analysis is used to determine the influence weight of each parameter. The parameter with the highest weight is selected as the key limiting parameter, and all parameters exceeding the range and their degree of deviation are recorded.

[0045] Step S1168: Associate and bind the soil type, vegetation type, adaptation level, and adaptation reason information of each spatially overlapping area to generate a soil-vegetation root system adaptation association data table.

[0046] The information obtained in the above steps, including soil type ID, vegetation type ID, adaptation level, adaptation reason (if compatible), or key limiting parameters (if incompatible) for each spatially overlapping region, is summarized to establish a soil-vegetation root system adaptation association data table. Each row in the table corresponds to a spatially overlapping region and includes fields such as overlapping region ID, soil type name, vegetation type name, adaptation level, limiting parameter name (optional), and limiting parameter value (optional). For example, a row of data might be: Overlapping region ID001, Soil type loam, Vegetation type arbor forest, Adaptation level excellent, Limiting parameter none. This data table serves as the core attribute data for associating the soil feature layer and the vegetation cover feature layer.

[0047] Step S1169: Based on the soil-vegetation root system adaptation association data table, establish an association link between the soil feature layer and the vegetation cover feature layer in the mining area. The attribute information of the association link includes the adaptation level and the adaptation reason.

[0048] In the layer association module of the Geographic Information System (GIS), a soil-vegetation root system adaptation association data table is used as a bridge to establish an association link between the soil feature layer and the vegetation cover feature layer of the mining area. The association link is represented by directed line segments, with the starting point at the geometric center of the soil type polygon and the ending point at the geometric center of the corresponding vegetation type polygon. The attribute information of the line segment is inherited from the association data table, including the adaptation level and the adaptation reason. For example, the association link from the center of the loam type polygon to the center of the arbor forest type polygon has an attribute of "superior adaptation level" and the adaptation reason is "good match between soil nutrients and root system requirements." For overlapping areas that do not match, the association link attribute is "incompatible adaptation level" and the adaptation reason is the name of the key constraint parameter and its deviation.

[0049] Step S11610: Perform spatial topology processing on the established association links, and adjust the coordinates of the start and end points of the links so that the start point of the association links is located within the soil type area of ​​the soil feature layer of the mining area, and the end point is located within the vegetation type area of ​​the vegetation cover feature layer of the mining area.

[0050] Because soil type and vegetation type polygons may have complex spatial distributions, the initial start and end points of the established association links may be located on or outside the polygon boundaries. A spatial topology check tool is used to perform topological verification on all association links. If the start point is found to be outside the corresponding soil type region or the end point is found to be outside the corresponding vegetation type region, a coordinate adjustment mechanism is activated. The adjustment method is as follows: calculate the shortest distance from the link start point to the boundary of the soil type polygon, and move the start point to a position inside the polygon along the distance direction; similarly, adjust the end point to be inside the vegetation type polygon. This ensures that the adjusted association links completely connect the corresponding areas in both layers, avoiding links crossing irrelevant areas.

[0051] Step S11611: Add the adjusted association links to the associated layer group of mining area environmental elements, update the association index and attribute mapping table of the associated layer group of mining area environmental elements, preview the association relationship of the updated associated layer group of mining area environmental elements, and optimize the display style of the association links based on the preview results. Different colors are used to display association links of different adaptation levels to improve the recognizability of the association relationship.

[0052] The topology-processed associated links are added as new layers to the mining area environmental element associated layer group. An entry for the link layer is added to the associated index file of the layer group, recording its association method and attribute field mapping relationship with the soil element layer and vegetation cover element layer. The attribute mapping table is updated to associate the adaptation level, adaptation reason, and other attributes of the associated links with the soil type and vegetation type attributes. The updated mining area environmental element associated layer group is loaded into the geographic information system. Selecting the association relationship preview mode, the system automatically displays all associated links. To improve recognizability, the display style is optimized: excellent adaptation links are green, good adaptation links are blue, general adaptation links are yellow, and unsuitable adaptation links are red; the width of the links is adjusted according to the area ratio of the soil-vegetation combination—the larger the area ratio, the wider the link. Through the differentiated display of color and width, the root distribution adaptation relationship of different soil-vegetation combinations is intuitively reflected.

[0053] Step S117: Based on the slope and aspect information of the mining area topographic feature layer, establish the soil erosion risk correlation between the mining area topographic feature layer and the mining area soil feature layer.

[0054] In a geographic information system (GIS), the slope grade and aspect attributes of the topographic feature layer of the mining area are spatially overlaid with the soil type attributes of the soil feature layer. Using the principle of the general soil loss equation, the influence of slope grade on soil erosion is analyzed: the steeper the slope, the stronger the soil erosion. Slope aspect indirectly affects soil erosion risk by influencing sunlight and precipitation distribution; for example, sunny slopes (southern slopes) have high evaporation and low soil moisture content, resulting in stronger erosion resistance, while shady slopes (north-facing slopes) have the opposite effect. Combining soil type erosion resistance parameters (such as higher clay content indicating stronger erosion resistance), a soil erosion risk assessment model is established, classifying the assessment results into low, medium, and high risk levels, which serve as the correlation attributes between the topographic and soil feature layers.

[0055] Step S118: Based on the vegetation type attribute of the vegetation cover element layer in the mining area, establish the interception influence relationship between the vegetation cover element layer in the mining area and the hydrological element layer in the mining area.

[0056] The canopy structure characteristics of different vegetation types were analyzed. For example, arbor forests have larger canopy heights and leaf area indices, resulting in a stronger ability to intercept precipitation; herbaceous plants have lower canopies and weaker interception capabilities. Maximum interception parameters for different vegetation types were obtained by reviewing literature. Combined with runoff path information from the hydrological element layer of the mining area, the interception rate of vegetation on runoff was calculated. The interception rate is positively correlated with vegetation cover level; the higher the cover, the greater the interception rate. The interception rate was added as a correlation attribute to the association between the vegetation cover element layer and the hydrological element layer. For example, if the runoff interception rate in a high-coverage arbor forest area is a certain percentage, the corresponding runoff depth in that area in the hydrological element layer is reduced by a certain value.

[0057] Step S119: By analyzing the impact of canopy structure of different vegetation types on precipitation interception, determine the correlation ratio between the interception coefficient of vegetation cover elements and the runoff intensity of hydrological elements.

[0058] For the main vegetation types in the mining area, canopy structure parameters, including canopy thickness, branch density, and leaf inclination angle, were collected. The interception amount under different rainfall intensities was determined through artificial rainfall simulation experiments. A regression model between interception amount and canopy structure parameters was established to calculate the interception coefficient for each vegetation type, representing the interception capacity per unit canopy area. The interception coefficient was correlated with runoff intensity in the hydrological element layer. Runoff intensity was expressed as runoff volume per unit time and calculated from hydrological station observation data. A linear or nonlinear relationship model between the interception coefficient and runoff intensity was established to determine the correlation ratio between the two; for example, a certain unit increase in the interception coefficient corresponds to a certain unit decrease in runoff intensity. This correlation ratio serves as an important parameter for subsequent migration simulations.

[0059] Step S1110: Integrate all relationships between the mining area topographic feature layer and the mining area hydrological feature layer, the mining area soil feature layer and the mining area vegetation cover feature layer, the mining area topographic feature layer and the mining area soil feature layer, and the mining area vegetation cover feature layer and the mining area hydrological feature layer to generate a mining area environmental feature associated layer group containing layer association index and attribute mapping table.

[0060] All established inter-layer relationships (including runoff path and terrain adaptation relationships, root distribution adaptation relationships, soil erosion risk relationships, and interception impact relationships) are systematically integrated. A layer association index file is created, recording the source layer, target layer, association type (spatial association or attribute association), and association fields for each association. An attribute mapping table is generated, detailing the correspondence and conversion rules between source layer attribute fields and target layer attribute fields. The topography, soil, hydrology, and vegetation cover feature layers, along with all associated link layers and associated attribute data, are packaged into a complete mining area environmental feature association layer group, stored in a specific file format to ensure that the geographic information system can correctly identify and load all layers and associations.

[0061] Step S1111: Extract the spatial range parameters of the associated layer group of environmental elements in the mining area, and use these spatial range parameters as the boundary reference for subsequent matching and processing of heavy metal occurrence status data in the mining area.

[0062] In the Geographic Information System (GIS), open the associated layer group of environmental features in the mining area. Use the layer attribute query function to obtain the overall spatial range parameters of the layer group, including minimum longitude, maximum longitude, minimum latitude, and maximum latitude. These parameters define a rectangular boundary that completely encompasses the spatial distribution range of all feature layers. Store the coordinate values ​​of this rectangular boundary in a configuration file. During subsequent data matching of heavy metal deposition status in the mining area, this configuration file is read, and heavy metal data exceeding this boundary range is automatically filtered out, ensuring that all matching data falls within the coverage area of ​​the associated layer group of environmental features in the mining area.

[0063] Step S1112: Perform layer overlay preview processing on the associated layer group of mining area environmental elements. Based on the preview results, perform coordinate correction processing on the element layers with spatial coordinate offsets. Store the associated layer group of mining area environmental elements with coordinate correction in the layer database of the geographic information system and generate a layer group call identifier.

[0064] Activate the layer overlay preview function in the Geographic Information System (GIS), simultaneously displaying the mining area topographic feature layer, soil feature layer, hydrological feature layer, vegetation cover feature layer, and all associated links. By comparing the spatial positions of landmark features (such as major road intersections and landmark buildings within the mining area) in each layer, check for coordinate offsets between layers. If an offset is found in a feature layer, for example, the sampling point positions in the soil feature layer do not match the actual terrain in the topographic feature layer, initiate the coordinate correction process. The correction method is as follows: select several control points with the same name on the offset layer and the reference layer (such as the topographic feature layer), obtain the coordinates of the control points in both layers, calculate the coordinate transformation parameters using affine transformation or polynomial fitting methods, and perform an overall coordinate transformation on the offset layer. Repeat the preview and correction process until the spatial position deviation of all layers is within the allowable range (e.g., less than a certain meter). Import the coordinate-corrected mining area environmental feature associated layer group into the GIS layer database, assigning it a unique layer group retrieval identifier (such as a string of letters and numbers) so that subsequent steps can quickly retrieve the layer group using this identifier.

[0065] Step S120: Obtain the heavy metal occurrence status data of the mining area, and dynamically match the heavy metal occurrence status data of the mining area with the associated layer group of environmental elements in the mining area to generate a data table of occurrence status and environmental element association.

[0066] In this non-ferrous metal mining area, data on the occurrence status of heavy metals, including their types, content, and spatial distribution, were obtained through systematic sampling and laboratory analysis. This data was then dynamically matched with an existing network of environmental elements in the mining area. Specifically, heavy metal data was linked to corresponding environmental attributes such as topography, soil, hydrology, and vegetation using spatial coordinates, forming a structured association data table.

[0067] Step S121: Collect heavy metal samples from different spatial locations in the mining area using mining area sampling equipment, perform component detection processing on the collected heavy metal samples, determine the types and contents of heavy metals in each heavy metal sample, and generate basic data on the occurrence status of heavy metals in the mining area.

[0068] Sampling was conducted using a combination of grid-based sampling and denser sampling in key areas. Regular sampling points were deployed at regular intervals throughout the mining area, with increased sampling density in key polluted areas such as mining areas, tailings ponds, and waste rock piles. Soil samples from the 0-20 cm surface layer were collected using a soil sampler. Several parallel samples were collected from each sampling point, mixed thoroughly, and placed in polyethylene sample bags. The precise coordinates of each sampling point were recorded using a GPS system during the sampling process. The collected soil samples were sent to a qualified laboratory for heavy metal analysis using inductively coupled plasma mass spectrometry (ICP-MS), detecting heavy metals including copper, lead, zinc, cadmium, and arsenic. The mass concentration of each heavy metal in each sample was determined by comparison with standard solutions, i.e., the heavy metal content. The test results were entered into a database, with each record including the sampling point number, heavy metal type code, and heavy metal content value, generating basic data on the heavy metal occurrence status of the mining area.

[0069] Step S122: Perform spatial coordinate annotation processing on the basic data of heavy metal occurrence status in the mining area, add corresponding spatial coordinate information to each set of heavy metal type and content data, and generate mining area heavy metal occurrence status data with spatial coordinates.

[0070] The coordinate data of sampling points are exported from the GPS recording device in latitude and longitude coordinates (degrees, minutes, and seconds). A table is established in the database linking sampling point numbers with coordinate data, connecting the basic data on the heavy metal occurrence status of the mining area with the coordinate data through the sampling point numbers. Corresponding longitude and latitude fields are added to each set of heavy metal type and content data, converting the degree-minute-second coordinates to decimal format for easier processing by the geographic information system. For example, if the heavy metal type at a sampling point is copper and the content is a certain value, the corresponding longitude and latitude are both decimal values. After spatial coordinate annotation, the mining area heavy metal occurrence status data with spatial coordinates is generated, with each data point containing spatial location information and heavy metal attribute information.

[0071] Step S123: Retrieve the associated layer group of environmental elements in the mining area from the layer database of the geographic information system, and read the spatial coordinate range information of each element layer in the associated layer group of environmental elements in the mining area.

[0072] Using the layer group call identifier generated in step S1112, a query operation is performed in the layer database of the geographic information system to locate and load the corresponding mining area environmental element associated layer group. Each element layer (topography, soil, hydrology, vegetation cover) in the layer group is traversed, and the spatial coordinate range of each layer is read through the layer attribute interface, including minimum longitude, maximum longitude, minimum latitude, and maximum latitude. The coordinate ranges of each element layer are compared, and the largest boundary is taken as the spatial coordinate range of the entire mining area environmental element associated layer group, ensuring coverage of the spatial distribution of all elements. The read coordinate range information is stored in a temporary variable for subsequent data filtering.

[0073] Step S124: Compare the spatial coordinate information of the heavy metal occurrence status data of the mining area with spatial coordinates with the spatial coordinate range information of the associated layer group of the mining area environmental elements, and filter out the heavy metal occurrence status data of the mining area whose spatial coordinates are within the range of the layer group.

[0074] Iterate through each record in the heavy metal occurrence status data of the mining area with spatial coordinates, and extract its longitude and latitude coordinates. Compare these coordinates with the spatial coordinate range of the associated layer group of mining area environmental elements read in step S123: if the longitude of the data is greater than or equal to the minimum longitude and less than or equal to the maximum longitude, and the latitude is greater than or equal to the minimum latitude and less than or equal to the maximum latitude, then the data is determined to be within the range of the layer group and is retained; otherwise, it is determined to be outside the range and is discarded. Implement the above filtering logic through database query statements, for example, using a WHERE clause to limit the value range of longitude and latitude. Store the filtered dataset as a new data table named "Heavy Metal Occurrence Data within Layer Group".

[0075] Step S125: For each group of heavy metal occurrence status data in the mining area after screening, extract the slope and aspect information from the topographic feature layer of the mining area corresponding to its spatial coordinates.

[0076] In the Geographic Information System (GIS), the filtered heavy metal occurrence status data of the mining area is loaded as an event layer, with each data point corresponding to one event. Using a spatial connection tool, the event layer is spatially associated with the mining area's topographic feature layer. The association method is a point-to-raster spatial query, i.e., finding the raster cell in the topographic feature layer where each data point is located. Slope grade and aspect range information are extracted from the attribute table of this raster cell. For example, the slope grade of the raster cell containing a data point is 15-25 degrees, and the aspect range is southeast (135-180 degrees). The extracted slope and aspect information are added as new fields to the attribute table of the heavy metal occurrence status data of the mining area, thus realizing the association between heavy metal data and topographic attributes.

[0077] Step S126: Extract soil type attributes and soil porosity information from the soil feature layer of the mining area corresponding to the spatial coordinates.

[0078] Using a method similar to step S125, the filtered heavy metal occurrence status data event layer of the mining area is spatially linked with the soil feature layer of the mining area. For each data point, the soil type attribute (e.g., loam, clay) and soil porosity value (e.g., a percentage) of the raster cell where it is located are obtained through spatial location lookup. This information is then added to the heavy metal data attribute table to form soil type and soil porosity fields. For example, a data point might correspond to sandy loam soil with a soil porosity of a certain percentage.

[0079] Step S127: Extract runoff path information and water level dynamic information from the hydrological element layer of the mining area corresponding to the spatial coordinates.

[0080] Spatially overlaying the heavy metal occurrence status data event layer and the hydrological element layer of the mining area is performed using a buffer analysis method. A buffer with a radius of a certain meter is created centered on each data point, and the existence of runoff paths within the buffer is checked. If a runoff path exists, its classification (e.g., main stream, tributary), channel width, and other runoff path information are extracted. Simultaneously, based on the runoff path ID, the corresponding water level dynamic information, such as the interval values ​​and durations of water levels during the wet season, normal season, and dry season, is retrieved from the attribute table of the hydrological element layer. The runoff path information and water level dynamic information are added to the heavy metal data attribute table. If no runoff path exists within the data point buffer, the relevant fields are marked as "None".

[0081] Step S128: Extract the vegetation type attribute and vegetation coverage level information from the vegetation cover element layer of the mining area corresponding to the spatial coordinates.

[0082] Using spatial connectivity tools, the heavy metal occurrence status data event layer of the mining area is linked with the vegetation cover feature layer of the mining area. For each data point, the vegetation type polygon of its location is queried, and vegetation type attributes (such as forest, shrubland) and vegetation cover level (such as high cover, medium cover) are extracted. The above information is added to the heavy metal data attribute table. For example, the vegetation type corresponding to a certain data point is herbaceous plants, and the vegetation cover level is medium cover (from a certain percentage to a certain percentage).

[0083] Step S129: Associate and bind the heavy metal types and contents corresponding to each set of heavy metal occurrence status data in the mining area with the slope information, aspect information, soil type attributes, soil porosity information, runoff path information, water level dynamic information, vegetation type attributes, and vegetation coverage level information corresponding to the spatial coordinates of the heavy metal occurrence status data in the mining area.

[0084] Create a new association table in the database. The table structure includes fields such as heavy metal type, heavy metal content, longitude, latitude, slope grade, aspect range, soil type, soil porosity, runoff path grade, water level dynamics, vegetation type, and vegetation cover grade. Iterate through the filtered heavy metal occurrence status data of the mining area, matching the heavy metal type and content of each data point with the environmental element information extracted in steps S125 to S128, and write this information into the corresponding fields of the association table. Ensure that the spatial coordinates of each record match the environmental element information one-to-one, without any missing or mismatched information. For example, a complete association record includes: copper, a certain content value, a certain longitude, a certain latitude, 15-25 degrees, southeast, loam, a certain percentage, tributary, a certain water level during the high-water season, arbor forest, and high cover.

[0085] Step S1210: Establish data association fields, including heavy metal type field, heavy metal content field, slope information field, slope aspect information field, soil type attribute field, soil porosity information field, runoff path information field, water level dynamic information field, vegetation type attribute field, and vegetation coverage level information field.

[0086] Based on the aforementioned association table, the definitions, data types, and value ranges of each data association field are clearly defined. The heavy metal type field is a string type, and its value is the name of the detected heavy metal element; the heavy metal content field is a numeric type, with the unit being milligrams per kilogram; the slope information field is a string type, and its value is a slope grade range; the slope aspect information field is a string type, and its value is a slope aspect range or direction name; the soil type attribute field is a string type, and its value is the soil type name; the soil porosity information field is a numeric type, with the unit being percentage; the runoff path information field is a string type, and its value is a runoff path grade or "none"; the water level dynamic information field is a string type, recording the water level range and duration; the vegetation type attribute field is a string type, and its value is the vegetation type name; the vegetation cover grade information field is a string type, and its value is the cover grade name. These field definitions ensure the standardization and consistency of the data.

[0087] Step S1211: Organize all the data after association and binding according to the established data association fields to generate a structured data table of associated storage status and environmental elements.

[0088] Following the data association fields defined in step S1210, all data after association and binding is structured and organized. Each record is checked for missing fields or data format errors, such as null values ​​for heavy metal content or misspelled soil type; abnormal data is marked or corrected. The organized data is stored in a relational database table, with the primary key set as a unique identifier for the heavy metal data point (e.g., sampling point number + heavy metal type). Appropriate indexes (e.g., spatial coordinate index, soil type index) are established to improve query efficiency. The final generated data table relating the occurrence status to environmental elements contains all valid heavy metal occurrence data for the mining area and their corresponding multi-factor environmental attributes, which can be directly used for subsequent multi-driving factor interaction analysis.

[0089] Step S130: Conduct an interactive influence analysis on the multiple driving factors of heavy metal migration in the mining area, determine the priority of each driving factor on the migration process and the interaction relationship, and generate a dynamic coupling rule set of multiple driving factors.

[0090] Based on the correlation data table between the occurrence state and environmental elements, this study conducts an in-depth analysis of the four major driving factors affecting heavy metal migration in this non-ferrous metal mining area: topography, soil, hydrology, and vegetation. Through a combination of statistical and mechanistic analysis, the study quantifies the influence of sub-factors (such as slope and aspect in the topography) on heavy metal migration, thereby determining the priority of each driving factor. Simultaneously, the study analyzes the interaction patterns between different driving factors (such as the interaction between topography and soil, and the interaction between soil and hydrology), ultimately integrating these into a dynamic coupling rule set for multiple driving factors.

[0091] Step S131: Identify multiple driving factors for heavy metal migration in the mining area, including topographic driving factors, soil driving factors, hydrological driving factors and vegetation driving factors.

[0092] Through literature review and expert consultation, combined with the actual environmental characteristics of this non-ferrous metal mining area, four major categories of driving factors affecting heavy metal migration were identified. Topographical driving factors mainly influence the direction and speed of heavy metal migration under gravity through surface slope and aspect; soil driving factors affect the migration and transformation of heavy metals in the soil through soil type (affecting adsorption capacity) and soil porosity (affecting permeability); hydrological driving factors dominate the hydrodynamic migration process through runoff pathways (migration carriers) and water level dynamics (affecting dissolution and transport capacity); vegetation driving factors play a moderating role in heavy metal migration through vegetation type (differences in interception capacity) and vegetation cover (affecting leaching and erosion).

[0093] Step S132: Retrieve the data table relating the occurrence state to environmental elements, extract the slope and aspect information related to topographic driving factors from the data table relating the occurrence state to environmental elements, analyze the changing trend of heavy metal content under different slope information, and determine the degree of influence of slope information on heavy metal migration among topographic driving factors.

[0094] From the data table relating the state of the landmass to environmental elements, all records with slope grade and corresponding heavy metal content fields were selected. The data were grouped by slope grade, such as 0-5 degrees, 5-15 degrees, and 15-25 degrees. Statistical parameters for heavy metal content, such as mean, median, and standard deviation, were calculated for each slope grade group. By comparing the average heavy metal content of different slope grade groups, the trend was analyzed: if the average heavy metal content decreased with increasing slope grade, it indicates that the steeper the slope, the easier it is for heavy metals to migrate and be lost, and the slope information has a greater impact on migration; conversely, if the trend is not obvious, the impact is smaller. Analysis of variance was used to test the significance of differences in heavy metal content among different slope grade groups. If the difference reached a statistically significant level (e.g., P < 0.05), the influence of slope information was further verified. Based on the strength and significance of the trend, the influence of slope information was divided into several levels (e.g., strong, medium, weak).

[0095] Step S133: Analyze the differences in the distribution of heavy metal content under different slope aspect information, and determine the degree of influence of slope aspect information on heavy metal migration among topographic driving factors.

[0096] The data in the correlation table between occurrence status and environmental elements were grouped according to slope aspect intervals (e.g., north, northeast, east, southeast, south, southwest, west, northwest), and the average and standard deviation of heavy metal content for each slope aspect group were calculated. By comparing the distribution of heavy metal content across different slope aspect groups, significant differences were analyzed: for example, the heavy metal content on sunny slopes (south-facing, southwest-facing slopes) may be lower than that on shady slopes (north-facing, northeast-facing slopes) because sunny slopes have strong sunlight, high evaporation rates, and low soil moisture, resulting in less heavy metal migration with water. On shady slopes, the soil is moist, making it easier for heavy metals to migrate through infiltration. The significance of the distribution differences was analyzed using chi-square or Kruskal-Wallis tests. Combining the degree of difference with the significance level, the influence of slope aspect information was classified into corresponding levels, consistent with the classification standard for the influence of slope gradient information.

[0097] Step S134: Extract soil type attributes and soil porosity information related to soil driving factors from the data table of soil occurrence status and environmental factors, analyze the differences in the adsorption capacity of heavy metals under different soil type attributes, and determine the degree of influence of soil type attributes on heavy metal migration among soil driving factors.

[0098] Soil type and heavy metal content fields were extracted from the associated data table, and the data were grouped according to soil type (e.g., loam, clay, sandy soil, sandy loam). Soil science literature was consulted to obtain reference values ​​for the adsorption coefficients of heavy metals for different soil types. The higher the adsorption coefficient, the stronger the soil's adsorption capacity for heavy metals, and the more difficult it is for heavy metals to migrate. Combining the heavy metal content data of different soil type groups in the associated data table: if a soil type has a large adsorption coefficient and a high average heavy metal content, it indicates that the soil type has a strong adsorption and fixation effect on heavy metals, weak heavy metal migration, and a significant influence of soil type attributes on migration; conversely, soil types with small adsorption coefficients and low heavy metal content indicate that heavy metals are easily migrated, and the influence is also relatively large (because adsorption capacity is a key factor affecting migration). By calculating the correlation coefficient between the adsorption coefficient and heavy metal content, the influence of soil type attributes was quantified and classified.

[0099] Step S135: Analyze the infiltration and migration rate of heavy metals under different soil porosity information to determine the degree of influence of soil porosity information on heavy metal migration among soil driving factors.

[0100] The soil porosity field in the associated data table is divided into several intervals (e.g., <a certain percentage, a certain percentage - a certain percentage, > a certain percentage), each interval representing a different porosity level. Higher soil porosity indicates stronger permeability, and a faster rate of heavy metal migration via water infiltration. Differences in infiltration and migration rates are indirectly reflected by analyzing the vertical distribution characteristics of heavy metal content in different porosity interval groups (if the data table includes profile data) or the horizontal migration distance (combined with spatial coordinate analysis). For example, the downstream content of heavy metals in the high-porosity group decreases faster than that in the low-porosity group, indicating a faster infiltration and migration rate and a greater influence of soil porosity. A regression analysis model is used to establish the relationship between soil porosity and heavy metal migration distance. The degree of influence is assessed based on the model's goodness of fit (e.g., R-squared value), and the model is categorized.

[0101] Step S136: Extract runoff path information and water level dynamic information related to hydrological driving factors from the data table of occurrence status and environmental elements, analyze the consistency of heavy metal migration direction under different runoff path information, and determine the degree of influence of runoff path information on heavy metal migration among hydrological driving factors.

[0102] Extract runoff path level fields (e.g., main stream, tributary, none) and spatial coordinates of data points from the associated data table. Project heavy metal data points of the same runoff path level onto the map of the geographic information system and observe whether their spatial distribution shows a regular change along the runoff path direction. For example, along the main stream direction, the heavy metal content gradually decreases from upstream to downstream, indicating that the migration direction is consistent with the runoff path, and the influence of runoff path information is significant. By calculating the correlation between heavy metal content and distance from the runoff path, if the closer the distance, the higher the heavy metal content, and the larger the absolute value of the correlation coefficient, the dominant role of the runoff path is further verified. Based on the significance of the consistency of migration direction and the results of correlation analysis, classify the influence level of runoff path information.

[0103] Step S137: Analyze the dissolution and migration capacity of heavy metals under different water level dynamic information, and determine the degree of influence of water level dynamic information on heavy metal migration among hydrological driving factors.

[0104] The data in the associated data table are grouped according to water level dynamic levels (high-water season, normal-water season, and low-water season). During the high-water season, water levels are high and runoff is large, resulting in strong dissolution and transport capacity of the water body. The proportion of dissolved heavy metals and their migration flux should be higher than during the normal-water season and the low-water season. The impact of water level dynamics on dissolution and migration capacity is analyzed by comparing the spatial variation coefficients of the proportion of dissolved heavy metal content (if the data table includes speciation data) or the total content in different water level dynamic groups (a large coefficient of variation indicates active migration). For example, the proportion of dissolved heavy metals in the high-water season group is significantly higher than that in the low-water season group, indicating a greater influence of water level dynamics. The degree of influence is quantified and classified by combining runoff data and the estimated heavy metal migration flux.

[0105] Step S138: Extract vegetation type attributes and vegetation coverage level information related to vegetation driving factors from the data table of vegetation status and environmental elements, analyze the differences in the interception effect of heavy metals under different vegetation type attributes, and determine the degree of influence of vegetation type attributes on heavy metal migration among vegetation driving factors.

[0106] The vegetation type and heavy metal content fields were extracted from the associated data table and grouped by vegetation type (tree forest, shrub forest, herbaceous plant, bare land). Different vegetation types have significant differences in canopy structure and root system characteristics, resulting in varying effects on heavy metal interception. Generally, tree forests have the strongest interception effect, while bare land has the weakest. By comparing the average heavy metal content of different vegetation type groups, if the heavy metal content of the tree forest group is higher than that of the herbaceous plant group, and the herbaceous plant group is higher than that of the bare land group, it indicates that the vegetation type has a significant interception effect and a large impact on heavy metal migration. The biomass and heavy metal enrichment coefficient data of the main vegetation types in the mining area were reviewed, and a comprehensive assessment was conducted based on the content differences to classify the degree of influence of vegetation type attributes.

[0107] Step S139: Analyze the differences in leaching and migration of heavy metals under different vegetation cover levels, and determine the degree of influence of vegetation cover level information on heavy metal migration among vegetation driving factors.

[0108] A correlation analysis was performed between the vegetation cover level field (high, medium, low) and the heavy metal content field in the correlation data table. High vegetation cover effectively reduces surface runoff and leaching, thereby reducing the migration and loss of heavy metals; low cover has the opposite effect. By comparing the spatial distribution range of heavy metal content in different cover level groups: high-coverage group has a concentrated distribution of high-value areas, while low-coverage group has a dispersed distribution of high-value areas (indicating a wide migration range), indicating that the influence of vegetation cover level is significant. The correlation between the surface runoff coefficient (smaller coefficient for high cover) and the heavy metal leaching loss rate under different cover levels was calculated to further quantify the degree of influence and classify the levels.

[0109] Step S1310: Based on the analysis results of the influence degree of each driving factor's sub-factors, determine the priority of the effects of topographic driving factors, soil driving factors, hydrological driving factors, and vegetation driving factors on the heavy metal migration process.

[0110] Step S13101: Set influence degree scoring dimensions for slope information and aspect information under topographic driving factors, soil type attributes and soil porosity information under soil driving factors, runoff path information and water level dynamic information under hydrological driving factors, and vegetation type attributes and vegetation coverage level information under vegetation driving factors; the influence degree scoring dimensions include the breadth of influence range, the duration of influence, and the magnitude of influence.

[0111] Three scoring dimensions are set for each sub-factor (eight sub-factors including slope information and aspect information). The breadth of influence refers to the proportion of the total mining area covered by the sub-factor's impact on heavy metal migration; the larger the breadth, the higher the score. The duration of influence refers to the time span over which the sub-factor affects the migration process, such as long-term effects (interannual scale) or short-term effects (seasonal scale); the longer the duration, the higher the score. The intensity of influence refers to the relative magnitude of the change in heavy metal migration caused by the sub-factor; the greater the magnitude (converted through the previously analyzed level of influence severity), the higher the score. The scoring range for each dimension is set as a number of points (e.g., 1-5 points), and detailed scoring criteria are established. For example, for the breadth of influence dimension: >80% is 5 points, 60-80% is 4 points, and so on.

[0112] Step S13102: Based on the data in the data table relating the state of existence and environmental elements, calculate the score of each sub-factor in the dimension of the breadth of influence. The score is determined based on the proportion of the spatial area where the sub-factor causes changes in heavy metal content.

[0113] For each sub-factor, based on the classification of its degree of influence, the areas significantly affected by the changes in heavy metal content are identified. For example, in slope information, areas with a slope grade >25 degrees and significantly lower heavy metal content than low-slope areas are defined as affected areas, and the proportion of this area to the total mining area is calculated. Based on the proportion and a scoring standard, a score is given for the breadth of influence. For example, an affected area accounting for 70% corresponds to a score of 4.

[0114] Step S13103: Calculate the score for each sub-factor in the dimension of duration of influence, the score being determined based on the time span by which the sub-factor influences the heavy metal migration process.

[0115] The temporal effects of each sub-factor were analyzed: Topographical driving factors (slope and aspect) are relatively stable, with a long-term (interannual or longer) impact on migration, resulting in a high duration score (e.g., 5 points); hydrological driving factors, particularly water level dynamics, exhibit seasonal variations, with a seasonal duration (e.g., 3 points); vegetation driving factors, such as vegetation cover level, may change with interannual vegetation growth, resulting in an interannual duration (e.g., 4 points). Based on the temporal stability characteristics of the sub-factors, a score for the duration dimension was given according to the scoring criteria.

[0116] Step S13104: Calculate the score of each sub-factor in the dimension of influence intensity. The score is determined based on the magnitude of the change in heavy metal migration caused by the sub-factor.

[0117] The influence levels (strong, medium, weak) of each sub-factor determined in the previous steps are converted into scores representing the magnitude of influence intensity. For example, strong corresponds to 5 points, medium to 3 points, and weak to 1 point. For cases requiring more precise differentiation, fine-tuning can be performed based on specific statistical analysis results (such as the F-value of ANOVA or the absolute value of the correlation coefficient) to ensure that the scores accurately reflect the differences in influence intensity.

[0118] Step S13105: Set weight coefficients for the breadth of influence, duration of influence, and magnitude of influence, respectively. The weight coefficients are determined based on the importance of each dimension to the heavy metal migration process.

[0119] The weighting coefficients for the three dimensions were determined using an expert scoring method, with a total weight of 1. For example, the magnitude of the impact has the greatest direct effect on the migration process, with a weight of 0.5; the breadth of the impact is second, with a weight of 0.3; and the duration of the impact has a weight of 0.2. Several experts in environmental geochemistry were invited to independently score the importance of each dimension, and the average value was used as the final weighting coefficient.

[0120] Step S13106: Multiply the scores of each sub-factor in the dimensions of breadth of influence, duration of influence, and magnitude of influence by the corresponding weight coefficients to obtain the weighted score of each sub-factor.

[0121] For each sub-factor, calculate the weighted score using the formula: Weighted Score = (Breadth of Influence Score × Breadth Weight) + (Duration of Influence Score × Time Weight) + (Intensity of Influence Score × Intensity Weight). For example, if the breadth score for slope information is 4 points, the time score is 5 points, and the intensity score is 5 points, with weights of 0.3, 0.2, and 0.5 respectively, then the weighted score = (4 × 0.3) + (5 × 0.2) + (5 × 0.5) = a certain value.

[0122] Step S13107: Summate the weighted scores of slope information and aspect information under the topographic driving factors to obtain the total weighted score of the topographic driving factors; sum the weighted scores of soil type attribute and soil porosity information under the soil driving factors to obtain the total weighted score of the soil driving factors; sum the weighted scores of runoff path information and water level dynamic information under the hydrological driving factors to obtain the total weighted score of the hydrological driving factors; sum the weighted scores of vegetation type attribute and vegetation cover level information under the vegetation driving factors to obtain the total weighted score of the vegetation driving factors.

[0123] The weighted scores of slope and aspect information included in the topographic driving factors are added together to obtain the total weighted score of the topographic driving factors. For example, if the weighted score of slope information is a certain value and the weighted score of aspect information is a certain value, the total weighted score of topography is the sum of the two. Similarly, the total weighted scores of soil (soil type + soil porosity), hydrology (runoff path + water level dynamics), and vegetation (vegetation type + vegetation cover) driving factors are calculated separately.

[0124] Step S13108: Sort the total weighted scores of topographic driving factors, soil driving factors, hydrological driving factors and vegetation driving factors in descending order. Based on the order of the total weighted scores, determine the priority of each driving factor on the heavy metal migration process. The higher the total weighted score, the higher the priority.

[0125] The total weighted scores of the four driving factors are ranked from highest to lowest. For example, if the ranking is: hydrological driving factor (total weighted score of a certain value) > soil driving factor (a certain value) > topographic driving factor (a certain value) > vegetation driving factor (a certain value), then the priority is hydrology, soil, topography, and vegetation. If there are cases where the total weighted scores are the same, the highest weighted score of each driving factor's sub-factors is compared, and the driving factor with the highest score has higher priority.

[0126] Step S13109: Record the priority ranking results of each driving factor, generate a driving factor priority list, organize the driving factor priority list together with the weighted scores of each sub-factor and the total weighted score to form a driving factor priority analysis report, and adjust the rule weights of each driving factor in the multi-driving factor dynamic coupling rule set based on the driving factor priority analysis report, and set the rule weights corresponding to driving factors with higher priority to a higher level.

[0127] The results of prioritizing driving factors, the weighted scores of each sub-factor, and the total weighted score of driving factors were compiled into a driving factor priority analysis report. The report includes the analysis methods, calculation process, result tables, and conclusions. Based on the priority ranking, rule weights were assigned to each driving factor within the multi-driving factor dynamic coupling rule set: hydrological driving factors had the highest weight (e.g., 0.4), followed by soil driving factors (0.3), topographic driving factors (0.2), and vegetation driving factors (0.1), with a total weight of 1. These rule weights directly affect the contribution of each driving factor in subsequent simulation models.

[0128] Step S131010: By comparing the simulation results of heavy metal migration under different weight settings with the actual observation data, the rationality of the weight settings is confirmed. The confirmed reasonable driving factor priority and corresponding rule weight are incorporated into the multi-driving factor dynamic coupling rule set, and the attribute information of the multi-driving factor dynamic coupling rule set is updated.

[0129] A typical heavy metal pollution migration path within the non-ferrous metal mining area was selected as the verification area. Migration simulation was performed using initially set weights to obtain the simulated migration path and migration amount. The simulation results were compared with actual heavy metal monitoring data in the verification area (such as the content changes at sampling points along the path), and the relative error between the simulated and measured values ​​was calculated. If the error is within an acceptable range (e.g., <10%), the weight settings are considered reasonable; otherwise, the weights of each driving factor are adjusted, and the simulation and verification are repeated until the simulation results match the measured data well. The final confirmed driving factor priorities and corresponding rule weights are written into the attribute file of the multi-driving factor dynamic coupling rule set, completing the initial configuration of the rule set.

[0130] Step S1311: Analyze the interaction between topographic driving factors and soil driving factors, and determine the correlation between slope information changes and soil porosity information.

[0131] In the data table relating the state of land cover to environmental factors, the slope grade and soil porosity fields are extracted. The average soil porosity is calculated by grouping by slope grade. The relationship between slope grade and average soil porosity is analyzed: generally, as the slope increases, surface runoff velocity accelerates, soil erosion intensifies, leading to the loss of fine-particle soil and potentially reducing soil porosity. By plotting a scatter plot of slope grade versus soil porosity and performing regression analysis, a significant negative correlation (negative correlation coefficient with a large absolute value) is observed. The correlation pattern is that slope information and soil porosity information are negatively correlated; for each increase in slope grade, the average soil porosity decreases by a certain percentage. This correlation pattern is recorded in the form of a function or lookup table as a rule governing the interaction between topographic and soil driving factors.

[0132] Step S1312: Analyze the interaction between soil driving factors and hydrological driving factors, and determine the correlation between changes in soil porosity information and runoff infiltration rate.

[0133] Soil porosity and runoff path information were extracted from a correlation data table and combined with hydrological observation data from the mining area (such as infiltration rate measurements). Higher soil porosity indicates better permeability, higher runoff infiltration rate, reduced surface runoff, and increased groundwater runoff. By analyzing the average infiltration rate across different soil porosity ranges, a positive correlation was established between the two; for example, for every percentage increase in soil porosity, the average runoff infiltration rate increases by a certain number of millimeters per minute. This correlation will be used to simulate how changes in soil porosity affect infiltration rate and, consequently, hydrologically driven heavy metal migration.

[0134] Step S1313: Analyze the interaction between hydrological driving factors and vegetation driving factors, and determine the correlation between changes in runoff intensity and vegetation interception capacity.

[0135] There is a mutual influence between runoff intensity (runoff volume / time) and vegetation interception capacity: increased runoff intensity may exceed the maximum interception capacity of vegetation, leading to a relative decrease in interception capacity; conversely, high vegetation cover can effectively reduce runoff intensity. By comparing vegetation interception under different runoff intensity levels (estimated through literature or experimental data), a correlation was established: within the range of vegetation interception capacity, as runoff intensity increases, interception increases linearly; after exceeding a threshold, interception tends to stabilize or decrease. This correlation was quantified as a runoff intensity-interception curve, serving as a rule for the interaction between hydrological and vegetation-driven factors.

[0136] Step S1314: Analyze the interaction between vegetation driving factors and topographic driving factors, and determine the correlation between changes in vegetation cover level and the degree of topographic erosion.

[0137] Increased vegetation cover enhances the soil-fixing effect of surface vegetation roots, effectively reducing topographic erosion (e.g., decreasing soil loss). Vegetation cover level and slope information were extracted from a correlation data table and combined with soil erosion modulus observation data from the mining area. The difference in erosion modulus between high-coverage and low-coverage areas under the same slope conditions was analyzed to establish a negative correlation between vegetation cover level and topographic erosion, for example, the erosion modulus under high-coverage levels is a certain percentage lower than that under low-coverage levels. This correlation will be used to simulate how vegetation change affects topographically driven erosion migration processes.

[0138] Step S1315: Integrate the priority and interaction relationships of each driving factor, formulate dynamic coupling rules for multiple driving factors that include factor weights and interaction coefficients, classify and organize all dynamic coupling rules for multiple driving factors according to the category of driving factors, and generate a set of dynamic coupling rules for multiple driving factors.

[0139] The rule weights corresponding to the priority of each driving factor determined in the previous steps (e.g., hydrology 0.4, soil 0.3, etc.), and the interaction and correlation laws of topography-soil, soil-hydrology, hydrology-vegetation, and vegetation-topography (represented by interaction influence coefficients, such as the influence coefficient of slope on soil porosity being a certain value), are integrated into a multi-driving-factor dynamic coupling rule. Each rule includes fields such as rule ID, driving factor category, target object, influence weight, interaction influence coefficient, and applicable conditions. Rules are classified according to driving factor category (topography, soil, hydrology, vegetation). Topography rules include slope influence rules, aspect influence rules, and interaction rules with soil; hydrology rules include runoff path rules, water level dynamic rules, and interaction rules with soil and vegetation. All classified rules are summarized to form a multi-driving-factor dynamic coupling rule set, stored in an Extensible Markup Language (Extreme Markup Language) format file for easy parsing and calling by computer programs.

[0140] Step S1316: By comparing the actual heavy metal migration observation data of the mining area, adjust the role weight and interaction influence coefficient of the rules in the multi-driving factor dynamic coupling rule set, store the adjusted multi-driving factor dynamic coupling rule set in the rule database of the geographic information system, and generate a rule set call identifier.

[0141] Another independent heavy metal migration path in the non-ferrous metal mining area was selected as a verification area, and migration simulation was performed using a dynamic coupling rule set of multiple driving factors. The simulated heavy metal migration amount and migration path were compared with the actual observation data of the verification area (such as the distribution of heavy metal content at several monitoring sections along the path), and the root mean square error was calculated. If the error was large, the weight and interaction coefficient of each rule were checked one by one, and the parameters with the greatest impact on the simulation results were determined through sensitivity analysis and adjusted accordingly. For example, if the simulated migration distance was shorter than the measured distance, the weight of the hydrological driving factor might be too low and needed to be appropriately increased; if the simulation deviation of the effect of soil porosity on the infiltration rate was large, the corresponding interaction coefficient needed to be adjusted. Iterative adjustments were made repeatedly until the simulation results met the accuracy requirements. The final adjusted dynamic coupling rule set of multiple driving factors was imported into the rule database of the geographic information system and assigned a unique rule set call identifier for use by the migration path simulation model.

[0142] Step S140: Based on the data table relating the endowment state and environmental elements and the set of dynamic coupling rules for multiple driving factors, construct a phased migration path simulation model, and generate a dataset of phased migration paths of heavy metals in the mining area through this phased migration path simulation model.

[0143] Using a data table relating the state of the minerals to environmental factors as input, and employing a multi-driving factor dynamic coupling rule set as the core algorithm, a phased migration path simulation model suitable for this non-ferrous metal mining area is constructed. Based on the natural process of heavy metal migration, it is divided into several continuous stages. A corresponding simulation sub-model is designed for each stage, and each sub-model invokes relevant rules from the multi-driving factor dynamic coupling rule set according to the characteristics of that stage. Through the sequential execution of the sub-models and the transmission of results, the migration behavior of heavy metals at different stages is simulated. Finally, a phased migration path dataset of heavy metals in the mining area is generated, containing key information such as the migration amount, migration direction, and spatial coordinates for each stage.

[0144] Step S141: Determine the stage division criteria for heavy metal migration in the mining area. Based on the priority of each driving factor in the dynamic coupling rule set of multiple driving factors, the heavy metal migration process is divided into the initial release stage, soil infiltration stage, hydrological transport stage and vegetation interception balance stage.

[0145] Based on the complete process of heavy metals being released from pollution sources, migrating in the soil, being transported by hydrodynamics, and ultimately being intercepted by vegetation or adsorbed by soil, and considering the priority of each driving factor (hydrological > soil > topographic > vegetation), the migration stages are divided as follows: Initial release stage: Heavy metals are released from pollution sources (such as tailings ponds) under the influence of gravity and initial runoff, with topographic driving factors (slope) playing a major role; Soil infiltration stage: Released heavy metals enter the soil and migrate through soil pores and adsorption, with soil driving factors dominating; Hydrological transport stage: Dissolved heavy metals in the soil are carried by runoff and migrate along the runoff path, with hydrological driving factors playing a decisive role; Vegetation interception equilibrium stage: Migrating heavy metals are intercepted by vegetation roots and canopy, and some are re-adsorbed by the soil, reaching a relative equilibrium state, with vegetation and soil driving factors working together.

[0146] Step S142: Retrieve the data table relating the occurrence state to environmental elements, extract the initial heavy metal content data for each spatial location in the data table relating the occurrence state to environmental elements, and use it as input data for the initial release stage; retrieve the dynamic coupling rule set of multiple driving factors, extract the terrain driving factor rules and soil driving factor rules related to the initial release stage, and construct the simulation sub-model for the initial release stage.

[0147] Heavy metal content data from all heavy metal pollution sources within the mining area (such as the area around tailings ponds and waste rock piles) were selected from the data table relating the occurrence state to environmental elements. This data served as the initial content data for the initial release phase, including spatial coordinates, heavy metal types, and initial content values. Rules relevant to the initial release phase were retrieved from the dynamic coupling rule set of multiple driving factors: slope influence rules (relationship between slope grade and release coefficient) and aspect influence rules from the topographic driving factors; and soil type adsorption rules (affecting the initial release ratio) from the soil driving factors. These rules were encoded into computer-executable functions to construct the core algorithm of the initial release phase simulation sub-model. The input parameters of the sub-model include initial heavy metal content, slope grade, aspect, and soil type; the output parameters are initial release amount and release direction.

[0148] Step S143: Import the input data of the initial release stage into the initial release stage simulation sub-model to simulate the initial release amount of heavy metals under the combined effect of terrain slope and soil porosity, and generate migration data of the initial release stage.

[0149] Initial heavy metal content data are input into the initial release stage simulation sub-model according to spatial location. The sub-model calculates the gravity release coefficient based on the slope level of the data points using slope influence rules (the steeper the slope, the larger the coefficient); it also calculates the initial adsorption ratio based on soil type using soil adsorption rules (a higher adsorption ratio results in a lower release ratio). Initial release amount = initial content × gravity release coefficient × (1 - initial adsorption ratio). The release direction is determined based on slope aspect information, along the downhill direction. Simulation calculations are performed on all pollution source data points in the mining area to obtain the initial release amount and release direction for each point. These are then summarized to form the initial release stage migration data. The data format includes spatial coordinates, heavy metal type, initial release amount, release direction, and stage identifier (initial release).

[0150] Step S144: Using the initial release stage migration data as the input data for the soil infiltration stage, retrieve the soil driving factor rules and vegetation driving factor rules related to the soil infiltration stage from the multi-driving factor dynamic coupling rule set, and construct a soil infiltration stage simulation sub-model.

[0151] The initial release amount from the initial release stage migration data is used as the input migration amount for the soil infiltration stage. Soil porosity infiltration rules (relationship between porosity and infiltration rate) and soil type adsorption rules (secondary adsorption) are retrieved from the multi-driving factor dynamic coupling rule set; vegetation cover retention rules (affecting infiltration amount) are also retrieved from the vegetation driving factor set. A soil infiltration stage simulation sub-model is constructed, with input parameters including input migration amount, soil porosity, soil type, and vegetation cover level; output parameters are infiltration migration amount, infiltration depth, and infiltration direction (vertically downward or along the horizontal direction of soil pores).

[0152] Step S145: Import the input data of the soil infiltration stage into the soil infiltration stage simulation sub-model to simulate the infiltration migration of heavy metals under the combined action of soil adsorption and vegetation interception, and generate soil infiltration stage migration data.

[0153] The initial release amount is input into the soil infiltration stage simulation sub-model. The sub-model calculates the infiltration rate based on soil porosity using infiltration rules, and the infiltration distance is obtained by multiplying the infiltration rate by the time step. It then calculates the adsorption amount based on soil type using secondary adsorption rules: adsorption amount = infiltration migration × soil adsorption coefficient. Finally, it calculates the vegetation retention amount based on vegetation cover level using retention rules: retention amount = infiltration migration × vegetation retention coefficient. Infiltration migration amount = input migration amount - adsorption amount - retention amount. The infiltration direction is determined by considering the terrain slope and soil porosity distribution, and may be vertically downward or inclined along the slope direction. Simulations are performed at each initial release point to generate soil infiltration stage migration data, including spatial coordinates, infiltration migration amount, infiltration direction, infiltration depth, and stage identifier (soil infiltration).

[0154] Step S146: Using the soil infiltration stage migration data as the input data for the hydrological transport stage, retrieve the hydrological driving factor rules and topographic driving factor rules related to the hydrological transport stage from the multi-driving factor dynamic coupling rule set, and construct a simulation sub-model for the hydrological transport stage.

[0155] The infiltration migration data from the soil infiltration stage is used as the input flux for the hydrological transport stage. Runoff path level rules (difference in transport capacity between main stream and tributaries), water level dynamic dissolution rules (dissolution coefficient during high-water / low-water seasons), and slope-oriented rules (influencing transport direction) for hydrological driving factors are retrieved from a multi-driving factor dynamic coupling rule set. A simulation sub-model for the hydrological transport stage is constructed, with input parameters including input flux, runoff path level, water level dynamic level, and slope; and output parameters including transport migration amount, transport path, and transport distance.

[0156] Step S147: Import the input data of the hydrological transport stage into the hydrological transport stage simulation sub-model to simulate the transport and migration path of heavy metals under the combined action of runoff transport and topographic guidance, and generate the migration data of the hydrological transport stage.

[0157] The infiltration migration amount is input into the hydrological transport stage simulation sub-model. The sub-model calls the transport capacity rule according to the runoff path level, with the transport capacity coefficient of the main stream being higher than that of the tributaries; it also calls the dissolution rule according to the water level dynamic level, with the dissolution coefficient during the high-water season being higher than that during the low-water season; the transport flux = input flux × transport capacity coefficient × dissolution coefficient. The transport direction is determined according to the topographic slope and the runoff path direction, moving along the combined direction of the slope decrease and the runoff path direction. The migration path of heavy metals in the runoff is simulated using a mass tracking algorithm, calculating the transport distance and location coordinates at each time step. Hydrological transport stage migration data is generated, including spatial coordinate sequences (path points), transport migration amount, transport direction, and stage identifier (hydrological transport).

[0158] Step S148: Using the migration data of the hydrological transport stage as the input data of the vegetation interception balance stage, retrieve the vegetation driving factor rules and soil driving factor rules related to the vegetation interception balance stage from the multi-driving factor dynamic coupling rule set, and construct a simulation sub-model of the vegetation interception balance stage.

[0159] Take the transport migration volume in the hydrological transport stage migration data as the input load for the vegetation interception balance stage. Retrieve the vegetation type interception rules (differences in interception capabilities of arbors / shrubs / herbs) and vegetation coverage balance rules (long-term interception effects) of vegetation driving factors from the multi-driving factor dynamic coupling rule set; the soil secondary adsorption rules (final adsorption) of soil driving factors. Construct a simulation sub-model for the vegetation interception balance stage. The input parameters include input load, vegetation type, vegetation coverage level, and soil type; the output parameters are the final interception volume, final adsorption volume, remaining migration volume after balance, and final distribution coordinates.

[0160] Step S149: Import the input data of the vegetation interception balance stage into the simulation sub-model of the vegetation interception balance stage, simulate the final distribution state of heavy metals under the combined action of continuous vegetation interception and soil secondary adsorption, and generate the migration data of the vegetation interception balance stage.

[0161] Input the transport migration volume into the simulation sub-model of the vegetation interception balance stage. The sub-model calls the interception rules according to the vegetation type to calculate the final interception volume of the vegetation (the interception coefficient of arbor forests is the highest); calls the secondary adsorption rules according to the soil type to calculate the final adsorption volume of the soil; the remaining migration volume after balance = input load - final interception volume - final adsorption volume. If the remaining migration volume is less than a certain threshold (considered negligible), the heavy metals reach distribution balance, and record the final distribution coordinates (the position of the interception point or adsorption point). Generate the migration data of the vegetation interception balance stage, including the final distribution coordinates, final interception volume, final adsorption volume, remaining migration volume after balance, and stage identifier (vegetation interception balance).

[0162] Step S1410: Integrate the migration data of the initial release stage, soil infiltration stage, hydrological transport stage, and vegetation interception balance stage, and add stage identifiers and time series information to the migration data of each stage.

[0163] Arrange the migration data of the four stages in chronological order: initial release stage (time step t1), soil infiltration stage (t2), hydrological transport stage (t3), vegetation interception balance stage (t4), where t1 < t2 < t3 < t4. Add a stage identifier field (such as "initial release", "soil infiltration", etc.) and a time series field (such as t1 = 0 - 10 days, t2 = 10 - 30 days, t3 = 30 - 60 days, t4 = 60 - 90 days) to each migration data record of each stage. Ensure that the spatial coordinates of the data in each stage can be connected to each other. For example, the starting coordinates of the soil infiltration stage are the release point coordinates of the initial release stage, and the starting coordinates of the hydrological transport stage are the infiltration end coordinates of the soil infiltration stage.

[0164] Step S1411: Establish phased migration path data fields, including phase identifier field, time series field, spatial coordinate field, heavy metal migration amount field, and migration direction field.

[0165] Define standard fields for phased migration path data: Phase Identifier (string type, recording phase name), Time Series (string type, recording the time interval of the phase), Spatial Coordinates (including longitude, latitude, and elevation, numeric type), Heavy Metal Migration Amount (numeric type, unit: mg / m²), and Migration Direction (string type, recording direction angle or orientation name). Specify data type, length, and value range constraints for each field to ensure data standardization.

[0166] Step S1412: The integrated migration data of each stage is structured according to the established stage migration path data fields to generate a mining area heavy metal stage migration path dataset.

[0167] Based on the defined fields for the phased migration path data, the integrated migration data for each phase undergoes format conversion and field population. For example, the phase identifier field for the initial release phase is populated with "Initial Release," the time series field with "0-10 days," the spatial coordinate field with the longitude and latitude of the release point, the heavy metal migration amount field with the initial release amount, and the migration direction field with the release direction. All phases of data are processed uniformly to form a structured dataset of phased heavy metal migration paths in the mining area, stored in a geodatabase feature class format, supporting spatial querying and analysis.

[0168] Step S1413: For data in the phased migration path dataset of heavy metals in the mining area where there are deviations in the continuity of the phases, adjust the driving factor rule parameters in the simulation sub-model of the corresponding phase, regenerate the migration data of the corresponding phase, store the adjusted phased migration path dataset of heavy metals in the mining area in the data storage module of the geographic information system, and generate a dataset retrieval identifier.

[0169] For example, step S14131: Identify data in the dataset of heavy metal migration paths in mining areas where there are deviations in the continuity of the stages, and determine the stage where the deviations occur, including the deviation between the initial release stage and the soil infiltration stage, the deviation between the soil infiltration stage and the hydrological transport stage, and the deviation between the hydrological transport stage and the vegetation interception balance stage.

[0170] In a geographic information system (GIS), visualize the dataset of heavy metal migration paths in mining areas at different stages and examine the spatial coherence of migration data between adjacent stages: the release endpoint of the initial release stage should coincide with the starting point of the soil infiltration stage; the infiltration endpoint of the soil infiltration stage should connect with the starting point of the hydrological transport stage; and the transport endpoint of the hydrological transport stage should be consistent with the starting point of the vegetation interception balance stage. If there is a significant disconnect or excessive overlap, it is considered a stage coherence deviation. For example, if the release amount in the initial release stage is a certain value, but the input migration amount in the soil infiltration stage is significantly lower than that value without a reasonable explanation (such as no additional loss), then there is a deviation between the initial release and soil infiltration stages.

[0171] Step S14132: For data where there is a discrepancy between the initial release stage and the soil infiltration stage, extract the heavy metal release amount from the migration data of the initial release stage and the heavy metal infiltration input amount from the migration data of the soil infiltration stage, and calculate the discrepancy between the two.

[0172] For initial release points with deviations, extract the initial release amount (Q1) and the corresponding soil infiltration stage input migration amount (Q2), and calculate the deviation value = (Q1-Q2) / Q1×100%. If the absolute value of the deviation value exceeds the preset threshold (e.g., 15%), adjustments are required.

[0173] Step S14133: Retrieve the driving factor rules related to the initial release stage and soil infiltration stage from the multi-driving factor dynamic coupling rule set, and check the parameter settings of topographic driving factors, soil driving factors, and vegetation driving factors in the rules.

[0174] From the set of dynamic coupling rules for multiple driving factors, query the slope influence rule parameters (such as gravity release coefficient) and soil adsorption rule parameters (such as initial adsorption coefficient) for the initial release stage; and the soil porosity and permeability parameters and vegetation interception parameters for the soil infiltration stage. Check whether these parameters are within a reasonable range; for example, whether the initial adsorption coefficient is too high, causing Q2 to be too small.

[0175] Step S14134: If the deviation is caused by the slope influence parameter setting in the simulation sub-model of the initial release stage, adjust the slope influence parameter to increase or decrease the coefficient of influence of slope on the amount of heavy metal release.

[0176] If the analysis finds that the deviation is due to improper setting of the slope influence parameter, such as the actual slope having a stronger promoting effect on the release amount than the model setting, then increase the gravity release coefficient in the slope influence parameter, recalculate the initial release amount Q1, and make Q2=Q1×(1-initial adsorption ratio) closer to the actual value.

[0177] Step S14135: If the deviation is caused by the soil adsorption parameter setting in the soil infiltration stage simulation sub-model, adjust the soil adsorption parameter to increase or decrease the soil's adsorption capacity coefficient for heavy metals.

[0178] If the deviation is due to excessively high soil adsorption parameters, resulting in Q2 = Q1 × (1 - initial adsorption ratio) being too small, then reduce the soil adsorption coefficient to increase the infiltration input Q2 and reduce the deviation value.

[0179] Step S14136: Input the adjusted parameters into the simulation sub-model of the corresponding stage, rerun the simulation sub-model, generate new initial release stage migration data or soil infiltration stage migration data, calculate the deviation value between the newly generated migration data and the migration data of the adjacent stage, if the deviation value exceeds the preset reasonable range, repeat the process of adjusting the driving factor rule parameters and resimulating until the deviation value is within the reasonable range.

[0180] Input the adjusted slope influence parameters or soil adsorption parameters into the corresponding simulation sub-model, rerun the simulation, and obtain a new Q1 or Q2. Calculate the new deviation value. If it still exceeds the threshold, continue to fine-tune the parameters and iteratively optimize until the deviation value is <15%. Adjust all data points with deviation one by one.

[0181] Step S14137: For data where there is a discrepancy between the soil infiltration stage and the hydrological transport stage, extract the heavy metal infiltration output from the soil infiltration stage migration data and the heavy metal transport input from the hydrological transport stage migration data, and calculate the discrepancy between the two.

[0182] Using a method similar to step S14132, extract the infiltration output (Q3) of the soil infiltration stage and the transport input (Q4) of the hydrological transport stage, calculate the deviation value = (Q3-Q4) / Q3×100%, and determine whether it exceeds the threshold.

[0183] Step S14138: Retrieve the driving factor rules related to the soil infiltration stage and hydrological transport stage from the multi-driving factor dynamic coupling rule set, and check the parameter settings of soil driving factors, vegetation driving factors, hydrological driving factors and topographic driving factors in the rules.

[0184] Check whether the vegetation interception parameters and soil porosity permeability parameters during the soil infiltration stage are reasonable; and whether the runoff path transport coefficient and water level dynamic dissolution parameters during the hydrological transport stage are reasonable.

[0185] Step S14139: If the deviation is caused by the vegetation interception parameter setting in the soil infiltration stage simulation sub-model, adjust the vegetation interception parameter to increase or decrease the vegetation's heavy metal interception capacity coefficient.

[0186] If the vegetation interception parameter is too high, resulting in Q3-Q4 being too large (Q4 being too small), then the vegetation interception coefficient should be reduced to allow more infiltration to enter the hydrological transport stage, thus increasing Q4.

[0187] Step S141310: If the deviation is caused by the runoff transport parameter settings in the simulation sub-model of the hydrological transport stage, adjust the runoff transport parameter to increase or decrease the runoff's transport capacity coefficient for heavy metals.

[0188] If the runoff transport parameter is too low, resulting in a smaller Q4, then the runoff transport coefficient should be increased to make Q4 closer to Q3.

[0189] Step S141311: Input the adjusted parameters into the simulation sub-model of the corresponding stage, rerun the simulation sub-model, generate new soil infiltration stage migration data or hydrological transport stage migration data, calculate the deviation value between the newly generated migration data and the migration data of the adjacent stage, if the deviation value exceeds the preset reasonable range, repeat the process of adjusting the driving factor rule parameters and resimulating until the deviation value is within the reasonable range.

[0190] Following the same steps as S14136, iteratively adjust the parameters and resimulate until the deviation values ​​of the soil infiltration and hydrological transport stages are within a reasonable range.

[0191] Step S141312: For data that shows a discrepancy between the hydrological transport stage and the vegetation interception balance stage, extract the heavy metal transport output from the migration data of the hydrological transport stage and the heavy metal balance input from the migration data of the vegetation interception balance stage, and calculate the discrepancy between the two.

[0192] Extract the hydrological transport output (Q5) and the vegetation interception balance input (Q6), and calculate the deviation value = (Q5-Q6) / Q5×100%.

[0193] Step S141313: Retrieve the driving factor rules related to the hydrological transport stage and the vegetation interception balance stage from the multi-driving factor dynamic coupling rule set, and check the parameter settings of hydrological driving factors, topographic driving factors, vegetation driving factors and soil driving factors in the rules.

[0194] Examine transport loss parameters during the hydrological transport stage, vegetation interception coefficient during the vegetation interception balance stage, and soil secondary adsorption coefficient, etc.

[0195] Step S141314: If the deviation is caused by the terrain guidance parameter setting in the simulation sub-model of the hydrological transport stage, adjust the terrain guidance parameter to increase or decrease the guidance coefficient of the terrain on the runoff direction.

[0196] If the terrain guidance parameter settings cause the transport path to deviate from reality and the amount of migration collected by Q6 is insufficient, then adjust the terrain guidance parameters to correct the transport direction, so that Q5 enters the vegetation interception balance stage more often.

[0197] Step S141315: If the deviation is caused by the setting of the soil secondary adsorption parameter in the simulation sub-model of the vegetation interception balance stage, adjust the soil secondary adsorption parameter to increase or decrease the soil's secondary adsorption capacity coefficient for heavy metals.

[0198] If the soil secondary adsorption parameter is too high, resulting in a smaller calculated value of Q6, then reduce this parameter to increase the equilibrium input Q6.

[0199] Step S141316: Input the adjusted parameters into the simulation sub-model of the corresponding stage, rerun the simulation sub-model, generate new migration data of the hydrological transport stage or migration data of the vegetation interception balance stage, calculate the deviation value between the newly generated migration data and the migration data of the adjacent stage, if the deviation value exceeds the preset reasonable range, repeat the process of adjusting the driving factor rule parameters and resimulating until the deviation value is within the reasonable range.

[0200] The parameters were iteratively adjusted and the simulation was repeated to ensure that the deviation values ​​of the hydrological transport and vegetation interception balance stage met the standards.

[0201] Step S141317: Reintegrate all the adjusted data to generate a phased migration path dataset of heavy metals in the mining area that meets the requirements of phase coherence. Store the final adjusted phased migration path dataset of heavy metals in the mining area into the data storage module of the geographic information system and generate a dataset retrieval identifier.

[0202] After adjusting for all stage deviations, the migration data from each stage are reintegrated to ensure data consistency and no significant deviations between stages. The final dataset of heavy metal migration paths in the mining area is then imported into the data storage module of the geographic information system, and a unique dataset retrieval identifier is assigned for subsequent dynamic visualization rendering.

[0203] Step S150: Import the phased migration path dataset of heavy metals in the mining area into the geographic information system, and perform dynamic visualization rendering processing on the phased migration path dataset of heavy metals in the mining area through the geographic information system, and output the visualization result of the dynamic migration path of heavy metals in the mining area containing the stage transition node identifier.

[0204] In a geographic information system platform, its visualization capabilities are utilized to create thematic maps and dynamically display datasets of phased migration paths of heavy metals in mining areas. By setting differentiated visual styles (colors, symbols, line types) for migration paths at different stages, the characteristics of each stage are highlighted; stage transition nodes (such as the end of the initial release stage and the beginning of the soil infiltration stage) are identified and marked; interactive functions are added to support user operations such as querying, zooming, and playing time series data, ultimately outputting intuitive and easy-to-understand visualizations of the dynamic migration paths of heavy metals in mining areas.

[0205] Step S151: Retrieve the phased migration path dataset of heavy metals in the mining area from the data storage module of the geographic information system, and read the phase identifier field, time series field, spatial coordinate field, heavy metal migration amount field, and migration direction field from the phased migration path dataset of heavy metals in the mining area.

[0206] The dataset of phased migration paths of heavy metals in the mining area is loaded from the data storage module of the geographic information system (GIS) by calling the dataset identifier. In the attribute table view of the GIS, all records in the dataset are reviewed to verify that the phase identifier field, time series field, etc., are correctly populated, ensuring data integrity and accuracy. For example, check for records with empty phase identifiers or incorrect time series formats; if any are found, return to step S14 for correction.

[0207] Step S152: Load the mining area base map layer in the map window of the geographic information system and use the mining area base map layer as the base map for dynamic visualization rendering.

[0208] Create a new map document in the Geographic Information System (GIS), load the base map layer of the mining area. This layer typically includes basic geographic features such as administrative boundaries, main roads, water systems, and settlements, and is displayed using standard topographic map symbols. Adjust the display scale of the base map to a suitable range to ensure that the entire mining area is displayed.

[0209] Step S153: Based on the spatial coordinate field in the phased migration path dataset of heavy metals in the mining area, map the spatial location of the migration data of each phase to the corresponding coordinates of the basic map layer of the mining area.

[0210] Using the feature creation tools of a Geographic Information System (GIS), migration data points or path lines for each stage are plotted on the base map layer of the mining area based on the spatial coordinate fields (longitude and latitude) in the dataset of phased migration paths of heavy metals in the mining area. For example, migration data points in the initial release stage are located in the area surrounding the tailings dam according to their coordinates, while migration path lines in the hydrological transport stage are plotted along the main stream of the mining area. To ensure the accuracy of spatial mapping, visual inspection can be performed by overlaying high-resolution remote sensing imagery of the mining area.

[0211] Step S154: For the initial release phase migration data, set exclusive visualization rendering colors and symbols, associate the heavy metal migration amount field in the initial release phase migration data with the symbol size, adjust the symbol size corresponding to the migration amount proportionally, and render the initial release phase migration path on the mining area base map layer.

[0212] A visualization scheme was designed for the migration data during the initial release phase: red was used as the exclusive color to symbolize the release of pollution sources; circles were chosen as the symbols, with the size of the circles proportional to the heavy metal migration amount field value—the larger the migration amount, the larger the circle. A proportional coefficient for the symbol size was set, for example, the symbol diameter increased by a certain number of millimeters for each increase in migration amount. In the symbolization settings panel of the geographic information system, "Scale Symbols by Attribute" was selected to associate the heavy metal migration amount field with the symbol size. The red color scheme was applied to complete the rendering of the migration path during the initial release phase, clearly displaying the location and release amount of each release point on the base map.

[0213] Step S155: For the soil infiltration stage migration data, set different visualization rendering colors and symbols than those in the initial release stage, associate the heavy metal migration amount field in the soil infiltration stage migration data with the symbol size, adjust the symbol size corresponding to the migration amount proportionally, and render the soil infiltration stage migration path on the mining area base map layer.

[0214] A differentiated visualization scheme was designed for each soil infiltration stage: orange was chosen as the color to distinguish it from the red of the initial release stage; triangles were used as symbols to represent the direction of infiltration in the soil (downward or sloping); the symbols were also scaled by attribute, with the area of ​​the triangle proportional to the amount of heavy metal migration in the soil infiltration stage migration data. Soil infiltration stage data was loaded into the geographic information system, and the orange triangle symbolization was applied to render the infiltration path, overlaying it on top of the initial release stage path.

[0215] Step S156: For the migration data in the hydrological transport stage, set different visualization rendering colors and symbols than the previous two stages, associate the migration direction field in the migration data of the hydrological transport stage with the direction of the arrow symbol, associate the length of the arrow symbol with the heavy metal migration amount field proportionally, and render the migration path of the hydrological transport stage on the base map layer of the mining area.

[0216] Visualization scheme for the hydrological transport stage: Blue is used to represent hydrodynamic transport; arrows are used as symbols, with the arrow direction consistent with the direction recorded in the migration direction field (e.g., downstream of the river); the length of the arrow is proportional to the amount of heavy metal migration, the greater the migration, the longer the arrow; the width of the arrow can represent the runoff path level (wider arrows for main streams, narrower arrows for tributaries). In the geographic information system, the migration direction field is associated through the "rotate symbol by attribute" function, the proportional relationship between arrow length and migration amount is set, and blue color scheme is applied to render the migration path of the hydrological transport stage, clearly showing the direction and scale of heavy metal transport with runoff.

[0217] Step S157: For the migration data in the vegetation interception balance stage, set different visualization rendering colors and symbols than the previous three stages. Associate the heavy metal migration amount field in the migration data of the vegetation interception balance stage with the symbol transparency. Adjust the symbol transparency corresponding to the migration amount proportionally. Render the migration path of the vegetation interception balance stage on the base map layer of the mining area.

[0218] Visualization scheme for the vegetation interception balance stage: Green is used to symbolize the role of vegetation; squares are used to represent a stable balance state; the transparency of the symbols is inversely proportional to the amount of heavy metal migration—the greater the migration (i.e., the more migration remains after balance), the lower the transparency (the darker the color), and vice versa. In the geographic information system, a correlation rule is set between the transparency of the square symbols and the migration amount field. Using a green color scheme, the migration path during the vegetation interception balance stage is rendered, showcasing the spatial pattern of the final heavy metal distribution.

[0219] Step S158: Identify the transition nodes of the migration path at each stage, wherein the transition node is the point where the end point of the previous stage migration path coincides with the start point of the next stage migration path.

[0220] In a geographic information system (GIS), spatial overlay analysis tools are used to locate points where the endpoint coordinates of the initial release stage migration path coincide with the starting coordinates of the soil infiltration stage migration path, marking these points as initial release-soil infiltration transition nodes. Similarly, points where the endpoint of the soil infiltration stage coincides with the starting point of the hydrological transport stage are marked as soil infiltration-hydrological transport transition nodes; and points where the endpoint of the hydrological transport stage coincides with the starting point of the vegetation interception balance stage are marked as hydrological transport-vegetation interception balance transition nodes. These transition nodes are critical locations in the heavy metal migration process, marking the transition from one stage to the next.

[0221] Step S159: Add a stage transition identifier to each transition node. The identifier contains the identifier of the previous stage and the identifier of the next stage.

[0222] Each transition node is assigned a unique identifier: a circular outer ring divided into left and right sections. The left section indicates the abbreviation of the previous stage (e.g., "Release" for initial release), and the right section indicates the abbreviation of the next stage (e.g., "Infiltration" for soil infiltration). The color scheme uses a blend of the previous and next stages; for example, the initial release-soil infiltration transition node uses a red-orange gradient. In the geographic information system (GIS), the coordinates of the transition nodes are associated with the identifier and plotted on the map, allowing viewers to visually identify the transition location and the relationship between the preceding and following stages.

[0223] Step S1510: Overlay and integrate the rendering results of the migration path at each stage with the conversion node identifier to generate a preliminary draft of the dynamic migration path visualization of heavy metals in the mining area.

[0224] In the map window of the Geographic Information System (GIS), the rendering results of the migration paths in the initial release stage, soil infiltration stage, hydrological transport stage, and vegetation interception balance stage are overlaid in sequence, and conversion node identifiers are added. The display order of each layer is adjusted to ensure that the conversion node identifiers are on the top layer and not covered by the paths. The overall visual effect is checked to ensure that the paths in each stage are clearly distinguishable and the conversion nodes are highlighted, generating a preliminary draft of the visualization of the dynamic migration path of heavy metals in the mining area.

[0225] Step S1511: Configure interactive functions for the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area, and add a time axis control function. The migration path status corresponding to different time series can be viewed through the time axis.

[0226] In the layout view of the Geographic Information System (GIS), add a timeline control and associate the timeline scale with the time series field in the dataset of phased migration paths of heavy metals in the mining area. Set keyframes for each time interval (e.g., 0-10 days, 10-30 days, etc.) on the timeline, with each keyframe displaying a corresponding stage of the migration path. Users can dynamically display the evolution of the migration path in chronological order—from the initial release stage to the soil infiltration stage, the hydrological transport stage, and the vegetation interception balance stage—by dragging the timeline slider or clicking the play button, allowing for a direct observation of the temporal dynamics of heavy metal migration.

[0227] Step S1512: Add a data query function. Clicking on any migration path symbol in the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area will display the stage identifier, time series, heavy metal migration amount, and migration direction information corresponding to that migration path symbol.

[0228] Enable feature query tools in the geographic information system and configure query templates for all migration path symbols and transformation node symbols. When a user clicks on a symbol on the map, the system pops up an information window displaying the corresponding attribute information, such as the stage identifier "initial release," the time series "0-10 days," the heavy metal migration amount "mg / m²," and the migration direction "southeast." The query function allows users to gain a deeper understanding of the detailed data for each migration point or path segment, enhancing the information interactivity of the visualization results.

[0229] Step S1513: Add zoom and pan functions to support zooming to view detailed areas and panning to view different spatial ranges on the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area.

[0230] Using the map navigation tools built into the Geographic Information System (GIS), enable the zoom tool (via mouse wheel or zoom button) and pan tool (via mouse drag). Users can zoom in to view details of a specific migration path segment, such as the precise location of a transition node; and pan to browse the distribution of migration paths in different areas of the mining area, gaining a comprehensive understanding of the migration patterns of heavy metals throughout the entire mining area.

[0231] Step S1514: Output the visualization results with the interactive functions set up to a visualization file format supported by the geographic information system, generate a visualization result of the dynamic migration path of heavy metals in the mining area containing stage transition node identifiers, store the visualization result of the dynamic migration path of heavy metals in the mining area to the visualization result library of the geographic information system, and generate a sharing link for the visualization result of the dynamic migration path of heavy metals in the mining area.

[0232] Save the initial draft of the interactive visualization of the dynamic migration path of heavy metals in the mining area in a project file format supported by the Geographic Information System (GIS), ensuring that all symbolization settings, interactive functions, and layer overlay relationships are correctly saved. Simultaneously, export it as a common image format (such as PNG) and a dynamic format (such as GIF or video files) for easy viewing by non-professional users. Store the visualization results in the GIS visualization results library and generate a unique sharing link. This link allows users to open and interactively view the visualization results in a web browser, facilitating convenient sharing and application of the results.

[0233] In one exemplary embodiment, a geographic information system (GIS)-based heavy metal migration path simulation system for mining areas is provided. This GIS-based heavy metal migration path simulation system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2As shown, this geographic information system-based heavy metal migration path simulation system for mining areas includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements a geographic information system-based method for simulating heavy metal migration paths in mining areas. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of a geographic information system-based mining heavy metal migration path simulation system, or an external keyboard, touchpad, or mouse, etc.

[0234] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for simulating the migration path of heavy metals in mining areas based on geographic information systems, characterized in that, The method includes: Construct a mining area environmental element association layer group, which includes a mining area topographic element layer, a mining area soil element layer, a mining area hydrological element layer, and a mining area vegetation cover element layer, and establish spatial location association and attribute association relationships between each element layer; Obtain data on the occurrence status of heavy metals in the mining area, dynamically match the data on the occurrence status of heavy metals in the mining area with the associated layer group of environmental elements in the mining area, and generate a data table on the association between occurrence status and environmental elements. An interactive influence analysis was conducted on multiple driving factors of heavy metal migration in mining areas to determine the priority and interaction relationship of each driving factor on the migration process, and to generate a dynamic coupling rule set of multiple driving factors. Based on the data table relating endowment state and environmental elements and the dynamic coupling rule set of multiple driving factors, a phased migration path simulation model is constructed, and a phased migration path dataset of heavy metals in the mining area is generated through this phased migration path simulation model. The dataset of phased migration paths of heavy metals in mining areas is imported into a geographic information system (GIS). The GIS is then used to perform dynamic visualization rendering of the dataset, outputting a visualization result of the dynamic migration paths of heavy metals in mining areas that includes stage transition node identifiers.

2. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 1, characterized in that, The construction of the associated layer group of environmental elements in the mining area includes: Obtain the original topographic data of the mining area, extract and process the slope and aspect information from the original topographic data of the mining area, and generate a mining area topographic feature layer containing slope and aspect information; Obtain raw soil data from the mining area, perform soil type classification and soil porosity labeling on the raw soil data, and generate a soil feature layer from the mining area that includes soil type attributes and soil porosity information. Obtain raw hydrological data of the mining area, perform runoff path extraction and water level change trend analysis on the raw hydrological data of the mining area, and generate a hydrological element layer of the mining area containing runoff path information and water level dynamic information. Obtain raw vegetation data of the mining area, perform vegetation type identification and vegetation coverage classification processing on the raw vegetation data of the mining area, and generate a vegetation coverage element layer of the mining area containing vegetation type attributes and vegetation coverage level information. The topographic feature layer, soil feature layer, hydrological feature layer, and vegetation cover feature layer of the mining area are imported into the layer association module of the geographic information system. Based on the spatial coordinate information of each feature layer, the runoff path and topographic adaptation association between the topographic feature layer and the hydrological feature layer of the mining area are established. Based on the spatial distribution range information of the soil feature layer in the mining area, a root distribution adaptation relationship is established between the soil feature layer and the vegetation cover feature layer in the mining area; based on the slope and aspect information of the topographic feature layer in the mining area, a soil erosion risk relationship is established between the topographic feature layer and the soil feature layer in the mining area; based on the vegetation type attribute of the vegetation cover feature layer in the mining area, a interception impact relationship is established between the vegetation cover feature layer and the hydrological feature layer in the mining area. By analyzing the impact of canopy structure of different vegetation types on precipitation interception, the correlation ratio between the interception coefficient of vegetation cover elements and the runoff intensity of hydrological elements was determined. Integrate all relationships between the topographic feature layer and the hydrological feature layer, the soil feature layer and the vegetation cover layer, the topographic feature layer and the soil feature layer, and the vegetation cover layer and the hydrological feature layer to generate a group of associated layers of environmental features in the mining area, which includes layer association indexes and attribute mapping tables. Extract the spatial range parameters of the associated layer group of environmental elements in the mining area, and use these spatial range parameters as the boundary reference for subsequent matching and processing of heavy metal occurrence status data in the mining area. The layer overlay preview process is performed on the associated layer group of environmental elements in the mining area. Based on the preview results, the coordinate correction process is performed on the element layers with spatial coordinate offset. The associated layer group of environmental elements in the mining area with coordinate correction is stored in the layer database of the geographic information system, and a layer group call identifier is generated.

3. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 1, characterized in that, The process of acquiring heavy metal occurrence status data in the mining area involves dynamically matching this data with the associated layer group of environmental elements in the mining area to generate a data table relating occurrence status to environmental elements, including: Heavy metal samples were collected from different spatial locations in the mining area using mining area sampling equipment. The collected heavy metal samples were processed for compositional analysis to determine the types and contents of heavy metals in each sample, thereby generating basic data on the occurrence status of heavy metals in the mining area. Spatial coordinate annotation is performed on the basic data of heavy metal occurrence status in the mining area. Corresponding spatial coordinate information is added to each set of heavy metal type and content data to generate heavy metal occurrence status data of the mining area with spatial coordinates. Retrieve the associated layer group of environmental elements in the mining area from the layer database of the geographic information system, and read the spatial coordinate range information of each element layer in the associated layer group of environmental elements in the mining area. The spatial coordinate information of the heavy metal occurrence status data of the mining area with spatial coordinates is compared with the spatial coordinate range information of the layer group associated with the environmental elements of the mining area, and the heavy metal occurrence status data of the mining area whose spatial coordinates are within the range of the layer group are selected. For each set of heavy metal occurrence status data in the mining area after screening, the slope and aspect information of the topographic feature layer of the mining area corresponding to its spatial coordinates are extracted. Extract soil type attributes and soil porosity information from the soil element layer of the mining area corresponding to the spatial coordinates; extract runoff path information and water level dynamic information from the hydrological element layer of the mining area corresponding to the spatial coordinates; extract vegetation type attributes and vegetation cover level information from the vegetation cover element layer of the mining area corresponding to the spatial coordinates. The heavy metal types and contents corresponding to each set of heavy metal occurrence status data in the mining area are associated and bound with the slope information, aspect information, soil type attributes, soil porosity information, runoff path information, water level dynamic information, vegetation type attributes, and vegetation coverage level information corresponding to the spatial coordinates of the heavy metal occurrence status data in the mining area. Establish data association fields, including heavy metal type field, heavy metal content field, slope information field, slope aspect information field, soil type attribute field, soil porosity information field, runoff path information field, water level dynamic information field, vegetation type attribute field, and vegetation coverage level information field. All the data after being linked and bound are organized according to the established data association fields to generate a structured data table relating storage status and environmental elements.

4. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 1, characterized in that, The interaction analysis of multiple driving factors for heavy metal migration in mining areas determines the priority and interaction relationships of each driving factor on the migration process, and generates a dynamic coupling rule set of multiple driving factors, including: Identify multiple driving factors for heavy metal migration in mining areas, including topographic driving factors, soil driving factors, hydrological driving factors, and vegetation driving factors. Retrieve the data table relating the occurrence state to environmental elements, extract the slope and aspect information related to topographic driving factors from the data table relating the occurrence state to environmental elements, analyze the changing trend of heavy metal content under different slope information, and determine the degree of influence of slope information on heavy metal migration among topographic driving factors. The distribution differences of heavy metal content under different slope aspect information were analyzed to determine the degree of influence of slope aspect information on heavy metal migration among topographic driving factors. Soil type attributes and soil porosity information related to soil driving factors were extracted from the data table of soil occurrence status and environmental factors. The differences in the adsorption capacity of heavy metals under different soil type attributes were analyzed to determine the degree of influence of soil type attributes on heavy metal migration among soil driving factors. The infiltration and migration rates of heavy metals under different soil porosity information were analyzed to determine the degree of influence of soil porosity information on heavy metal migration among soil driving factors. Extract runoff path information and water level dynamic information related to hydrological driving factors from the data table of occurrence status and environmental elements, analyze the consistency of heavy metal migration direction under different runoff path information, and determine the degree of influence of runoff path information on heavy metal migration among hydrological driving factors. The dissolution and migration capacity of heavy metals under different water level dynamics were analyzed to determine the degree of influence of water level dynamics on heavy metal migration among hydrological driving factors. Extract vegetation type attributes and vegetation coverage level information related to vegetation driving factors from the data table of vegetation status and environmental factors, analyze the differences in heavy metal interception effect under different vegetation type attributes, and determine the degree of influence of vegetation type attributes on heavy metal migration among vegetation driving factors. The differences in leaching and migration of heavy metals under different vegetation cover levels were analyzed to determine the degree of influence of vegetation cover level information on heavy metal migration among vegetation driving factors. Based on the analysis of the influence of each driving factor's sub-factors, the priority of the effects of topographic driving factors, soil driving factors, hydrological driving factors, and vegetation driving factors on the heavy metal migration process was determined. This study analyzes the interaction between topographic and soil driving factors to determine the correlation between changes in slope information and soil porosity information; it also analyzes the interaction between soil and hydrological driving factors to determine the correlation between changes in soil porosity information and runoff infiltration rate; the interaction between hydrological and vegetation driving factors to determine the correlation between changes in runoff intensity and vegetation interception capacity; and the interaction between vegetation and topographic driving factors to determine the correlation between changes in vegetation cover level and topographic erosion degree. By integrating the priorities and interactions of various driving factors, dynamic coupling rules for multiple driving factors are formulated, including factor weights and interaction coefficients. All dynamic coupling rules for multiple driving factors are classified and organized according to the categories of driving factors, and a set of dynamic coupling rules for multiple driving factors is generated. By comparing actual heavy metal migration observation data in mining areas, the role weights and interaction coefficients of the rules in the multi-driving factor dynamic coupling rule set are adjusted. The adjusted multi-driving factor dynamic coupling rule set is then stored in the rule database of the geographic information system, and a rule set call identifier is generated.

5. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 1, characterized in that, Based on the data table relating endowment state and environmental elements, and a set of dynamic coupling rules for multiple driving factors, a phased migration path simulation model is constructed. This model generates a dataset of phased migration paths of heavy metals in the mining area, including: The criteria for dividing the heavy metal migration in mining areas were determined. Based on the priority of each driving factor in the dynamic coupling rule set of multiple driving factors, the heavy metal migration process was divided into the initial release stage, soil infiltration stage, hydrological transport stage and vegetation interception balance stage. The data table relating the occurrence state to environmental elements was retrieved, and the initial heavy metal content data for each spatial location in the data table was extracted as input data for the initial release stage. The dynamic coupling rule set of multiple driving factors was retrieved, and the topographic driving factor rules and soil driving factor rules related to the initial release stage were extracted to construct a simulation sub-model for the initial release stage. The input data of the initial release stage is imported into the initial release stage simulation sub-model to simulate the initial release amount of heavy metals under the combined effect of topographic slope and soil porosity, and the initial release stage migration data is generated. The initial release stage migration data is used as the input data of the soil infiltration stage. The soil driving factor rules and vegetation driving factor rules related to the soil infiltration stage are retrieved from the multi-driving factor dynamic coupling rule set to construct the soil infiltration stage simulation sub-model. The input data of the soil infiltration stage is imported into the soil infiltration stage simulation sub-model to simulate the infiltration migration of heavy metals under the combined action of soil adsorption and vegetation interception, and the migration data of the soil infiltration stage is generated. The migration data of the soil infiltration stage is used as the input data of the hydrological transport stage. The hydrological driving factor rules and topographic driving factor rules related to the hydrological transport stage are retrieved from the dynamic coupling rule set of multiple driving factors to construct the hydrological transport stage simulation sub-model. Input data from the hydrological transport stage is imported into the hydrological transport stage simulation sub-model to simulate the transport and migration path of heavy metals under the combined effects of runoff transport and topographic guidance, generating migration data for the hydrological transport stage. The migration data from the hydrological transport stage is used as input data for the vegetation interception balance stage. The vegetation driving factor rules and soil driving factor rules related to the vegetation interception balance stage are retrieved from the dynamic coupling rule set of multiple driving factors to construct the simulation sub-model for the vegetation interception balance stage. The input data of the vegetation interception balance stage is imported into the vegetation interception balance stage simulation sub-model to simulate the final distribution state of heavy metals under the combined effect of continuous vegetation interception and secondary adsorption in the soil, and to generate migration data of the vegetation interception balance stage. Integrate migration data from the initial release stage, soil infiltration stage, hydrological transport stage, and vegetation interception balance stage, and add stage identifiers and time series information to the migration data of each stage. Establish data fields for the phased migration path, including a phase identifier field, a time series field, a spatial coordinate field, a heavy metal migration amount field, and a migration direction field; The integrated migration data from each stage is structured according to the established staged migration path data fields to generate a phased migration path dataset for heavy metals in the mining area. For data in the phased migration path dataset for heavy metals in the mining area that have deviations in stage continuity, the driving factor rule parameters in the simulation sub-model of the corresponding stage are adjusted, and the migration data of the corresponding stage is regenerated. The adjusted phased migration path dataset for heavy metals in the mining area is stored in the data storage module of the geographic information system, and a dataset retrieval identifier is generated.

6. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 1, characterized in that, The process involves importing the phased migration path dataset of heavy metals in the mining area into a geographic information system (GIS), performing dynamic visualization rendering on the dataset using the GIS, and outputting a visualization result of the dynamic migration path of heavy metals in the mining area that includes stage transition node identifiers. This includes: Retrieve the phased migration path dataset of heavy metals in the mining area from the data storage module of the geographic information system, and read the phase identifier field, time series field, spatial coordinate field, heavy metal migration amount field, and migration direction field of the phased migration path dataset of heavy metals in the mining area. Load the mining area base map layer in the map window of the geographic information system and use the mining area base map layer as the base map for dynamic visualization rendering; Based on the spatial coordinate field in the phased migration path dataset of heavy metals in the mining area, the spatial location of the migration data in each phase is mapped to the corresponding coordinates in the basic map layer of the mining area. For the migration data in the initial release phase, set exclusive visualization rendering colors and symbols, associate the heavy metal migration amount field in the initial release phase migration data with the symbol size, adjust the symbol size corresponding to the migration amount proportionally, and render the initial release phase migration path on the mining area base map layer. For the migration data in the soil infiltration stage, different visualization rendering colors and symbols are set compared with the initial release stage. The heavy metal migration amount field in the soil infiltration stage migration data is associated with the symbol size, and the symbol size corresponding to the migration amount is adjusted proportionally. The migration path of the soil infiltration stage is rendered on the base map layer of the mining area. For the migration data in the hydrological transport stage, different visualization rendering colors and symbols are set compared to the previous two stages. The migration direction field in the migration data of the hydrological transport stage is associated with the direction of the arrow symbol. The length of the arrow symbol is proportionally associated with the heavy metal migration amount field. The migration path of the hydrological transport stage is rendered on the basic map layer of the mining area. For the migration data in the vegetation interception balance stage, different visualization rendering colors and symbols are set compared to the previous three stages. The heavy metal migration amount field in the migration data of the vegetation interception balance stage is associated with the symbol transparency. The symbol transparency corresponding to the migration amount is adjusted proportionally. The migration path of the vegetation interception balance stage is rendered on the basic map layer of the mining area. Identify the transition nodes of the migration path at each stage, where the endpoint of the previous migration path coincides with the starting point of the next migration path. Add a stage transition identifier to each transition node, which includes the identifier of the previous stage and the identifier of the next stage; overlay and integrate the rendering results of the migration path of each stage with the transition node identifier to generate a preliminary draft of the visualization of the dynamic migration path of heavy metals in the mining area. Interactive features were added to the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area, including a timeline control function. The timeline allows users to switch between viewing the migration path status corresponding to different time series. Add a data query function. Clicking on any migration path symbol in the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area will display the corresponding stage identifier, time series, heavy metal migration amount, and migration direction information. Add zoom and pan functions to support zooming to view detailed areas and panning to view different spatial ranges in the initial draft of the visualization of the dynamic migration path of heavy metals in the mining area. The visualization results with interactive functions set up are output in a visualization file format supported by the geographic information system. A visualization result of the dynamic migration path of heavy metals in the mining area containing stage transition node identifiers is generated. The visualization result of the dynamic migration path of heavy metals in the mining area is stored in the visualization result library of the geographic information system, and a sharing link of the visualization result of the dynamic migration path of heavy metals in the mining area is generated at the same time.

7. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 2, characterized in that, The method of establishing a root distribution adaptation relationship between the soil feature layer and the vegetation cover feature layer in the mining area based on the spatial distribution range information of the soil feature layer includes: Extract the spatial distribution range boundary information of each soil type from the soil feature layer of the mining area to generate a soil type spatial distribution boundary dataset. Extract the spatial distribution range boundary information of each vegetation type from the vegetation cover element layer of the mining area, generate a spatial distribution boundary dataset of vegetation types, import the spatial distribution boundary dataset of soil types and the spatial distribution boundary dataset of vegetation types into the spatial overlay analysis module of the geographic information system, perform boundary overlay processing, and determine the spatial overlap area between different soil types and different vegetation types. For each spatially overlapping region, extract the corresponding soil type attributes and query the physicochemical properties of the soil type, including soil pH, soil nutrient content, and soil clay ratio. Extract the vegetation type attributes corresponding to the spatially overlapping area, and query the biological characteristic parameters of the vegetation type, including root depth, root distribution density, and root exudate type; The compatibility between the physicochemical properties of soil types and the biological characteristics of vegetation types is analyzed to determine whether the soil type meets the environmental requirements for root growth of the corresponding vegetation type. For spatially overlapping areas that meet the environmental requirements, the compatibility level of root distribution between the soil type and the vegetation type is determined, based on the degree of matching between soil nutrient content and the nutrient absorption requirements of vegetation roots. For spatially overlapping areas that do not meet the environmental requirements, the key parameters in the soil physicochemical properties that limit root growth are analyzed to determine the reasons for the mismatch in root distribution between the soil type and the vegetation type. The soil type, vegetation type, adaptation level, and adaptation reason information of each spatially overlapping area are associated and bound to generate a soil-vegetation root system adaptation association data table. Based on the soil-vegetation root system adaptation association data table, an association link is established between the soil feature layer and the vegetation cover feature layer in the mining area. The attribute information of the association link includes the adaptation level and the adaptation reason. Spatial topology processing is performed on the established links to adjust the coordinates of the start and end points of the links so that the start point of the links is located within the soil type area of ​​the soil feature layer in the mining area, and the end point is located within the vegetation type area of ​​the vegetation cover feature layer in the mining area. The adjusted association links are added to the associated layer group of mining area environmental elements. The association index and attribute mapping table of the associated layer group of mining area environmental elements are updated. The association relationship of the updated associated layer group of mining area environmental elements is previewed. Based on the preview results, the display style of the association links is optimized. Association links of different adaptation levels are displayed in different colors to improve the recognizability of the association relationship.

8. The method for simulating the migration path of heavy metals in mining areas based on geographic information systems according to claim 4, characterized in that, Based on the analysis of the influence of sub-factors under each driving factor, the priority of the effects of topographic driving factors, soil driving factors, hydrological driving factors, and vegetation driving factors on the heavy metal migration process is determined, including: For slope and aspect information under topographic driving factors, soil type attributes and soil porosity information under soil driving factors, runoff path information and water level dynamics information under hydrological driving factors, and vegetation type attributes and vegetation coverage level information under vegetation driving factors, an impact degree scoring dimension is set for each of them; the impact degree scoring dimension includes the breadth of the impact range, the duration of the impact, and the magnitude of the impact. Based on the data in the correlation table between the state of occurrence and environmental elements, the score of each sub-factor in the breadth dimension of the scope of influence is calculated. The score is determined based on the proportion of the spatial area where the sub-factor causes changes in heavy metal content. Calculate the score for each sub-factor in the dimension of duration of influence, based on the time span by which the sub-factor affects the heavy metal migration process; calculate the score for each sub-factor in the dimension of magnitude of influence, based on the magnitude of the change in heavy metal migration caused by the sub-factor. Weighting coefficients were set for the dimensions of breadth of influence, duration of influence, and intensity of influence, and the weighting coefficients were determined based on the importance of each dimension to the heavy metal migration process. The weighted score of each sub-factor is obtained by multiplying the scores of the breadth of influence, duration of influence, and intensity of influence of each sub-factor by the weight coefficient of the corresponding dimension. The total weighted score for topographic driving factors is obtained by summing the weighted scores of slope information and aspect information under topographic driving factors; the total weighted score for soil driving factors is obtained by summing the weighted scores of soil type attribute information and soil porosity information under soil driving factors; the total weighted score for hydrological driving factors is obtained by summing the weighted scores of runoff path information and water level dynamic information under hydrological driving factors; and the total weighted score for vegetation driving factors is obtained by summing the weighted scores of vegetation type attribute information and vegetation cover level information under vegetation driving factors. The total weighted scores of topographic, soil, hydrological, and vegetation driving factors were sorted in descending order. Based on the order of the total weighted scores, the priority of each driving factor on the heavy metal migration process was determined. The higher the total weighted score, the higher the priority. Record the priority ranking results of each driving factor, generate a priority list of driving factors, organize the priority list of driving factors together with the weighted scores of each sub-factor and the total weighted score to form a priority analysis report of driving factors, and adjust the rule weights of each driving factor in the dynamic coupling rule set of multiple driving factors based on the priority analysis report of driving factors. By comparing the simulation results of heavy metal migration under different weight settings with actual observation data, the rationality of the weight settings is confirmed. The confirmed reasonable driving factor priority and corresponding rule weights are incorporated into the multi-driving factor dynamic coupling rule set, and the attribute information of the multi-driving factor dynamic coupling rule set is updated.

9. A simulation system for heavy metal migration paths in mining areas based on geographic information systems, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the method for simulating the migration path of heavy metals in a mining area based on a geographic information system, as described in any one of claims 1 to 8, by executing the machine-executable instructions.

10. A computer program product, characterized in that, The computer program product includes machine-executable instructions stored in a computer-readable storage medium. The processor of the geographic information system-based heavy metal migration path simulation system reads the machine-executable instructions from the computer-readable storage medium and executes the machine-executable instructions, causing the geographic information system-based heavy metal migration path simulation system to perform the geographic information system-based heavy metal migration path simulation method as described in any one of claims 1 to 8.

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